commit 4244787e587bd49f591a3a4d6a9c18d498296dfa Author: ModelHub XC Date: Sat Aug 29 19:29:19 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: sabari2005/cyberslm-instruct Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..8bbadc7 --- /dev/null +++ b/README.md @@ -0,0 +1,182 @@ +--- +license: apache-2.0 +language: +- en +library_name: pytorch +pipeline_tag: text-generation +base_model: sabari2005/cyberslm-base +tags: +- cybersecurity +- security +- small-language-model +- instruction-tuned +- sft +- causal-lm +--- + +# CyberSLM-instruct — 33.5M-parameter cybersecurity assistant + +Instruction-tuned from +[**sabari2005/cyberslm-base**](https://huggingface.co/sabari2005/cyberslm-base) +on 23,540 cybersecurity conversations. + +**Code:** [github.com/Sabari2005/cyberslm](https://github.com/Sabari2005/cyberslm) + +## Read this before using it + +At 33.5M parameters this model learned the **shape** of a good answer — markdown +structure, numbered steps, worked examples, mitigation sections — and is +**frequently wrong about the content**. + +Measured over 8 greedy prompts: + +| category | mean 8-gram repetition | stopped on EOS | +|---|---:|---:| +| security | 18.7% | 1 / 4 | +| general | 18.9% | 1 / 2 | +| code | 19.9% | 0 / 2 | +| **overall** | **19.0%** | **2 / 8** | + +**Do not use this as a factual security reference. Do not run code it +generates.** It is a demonstration of a correct end-to-end training pipeline and +a baseline for scaling. + +### Where it works + +Well-covered in-domain questions come out correct and terminate cleanly: + +> **What is SQL injection and how do I prevent it?** +> +> SQL injection (SQLi) is a security vulnerability that allows attackers to +> manipulate database queries by injecting malicious SQL code through input +> fields. It occurs when user-supplied data is improperly sanitized, allowing +> attackers to manipulate the database. For example, if an attacker submits a +> username like `admin' --` as the username, the query becomes: +> `SELECT * FROM users WHERE username = '[input]' AND password = '[input]'`. + +> **What is a firewall?** +> +> A firewall is a network security device or software that monitors and controls +> incoming and outgoing network traffic based on predefined security rules. It +> acts as a barrier between trusted internal networks and untrusted external +> networks, such as the internet. + +### Where it fails + +All observed, reproducible, and the majority case: + +* **Wrong content, confident tone.** Asked to contrast symmetric and asymmetric + encryption it answered about hashing and IKE, never addressing the question. +* **Circular definitions.** "A buffer overflow is a type of buffer overflow that + could lead to arbitrary code execution." +* **Topically-adjacent but wrong vocabulary.** Asked how to investigate a + phishing email it produced `SameSite` and `Strict` — real security terms, + wrong topic (they are cookie attributes). +* **Degenerate loops in code.** `port: The port to use` repeated to the token + limit. +* **Unreliable termination.** Only 2 of 8 prompts stopped on EOS; the rest ran + to the token limit. + +These are consequences of scale, not of the training run — the loss curve is +healthy and the pipeline is machine-verified (35 architecture checks, 173 tests). + +## Model details + +| | | +|---|---| +| parameters | 33,531,264 | +| architecture | 12 layers, d_model 384, 6 heads, SwiGLU 1024, RoPE, RMSNorm, tied head | +| context | 2048 | +| vocab | 32,000 (SentencePiece BPE) | +| base model | sabari2005/cyberslm-base | +| SFT data | 23,540 conversations, 15.1M supervised tokens | +| epochs | 3 (2,208 optimizer steps) | +| optimiser | AdamW, lr 2e-5, 3% warmup, cosine, bf16 | +| best val loss | 2.2627 (response tokens only) | + +Loss is computed on assistant responses only; prompts are masked. 88% of tokens +in the SFT set are supervised. + +## Usage + +```bash +pip install torch sentencepiece +git clone https://huggingface.co/sabari2005/cyberslm-instruct +cd cyberslm-instruct +python infer_chat.py --prompt "What is SQL injection and how do I prevent it?" +``` + +Interactive: + +```bash +python infer_chat.py --interactive +``` + +Options: + +```bash +python infer_chat.py \ + --prompt "What is a buffer overflow?" \ + --max-new-tokens 200 \ + --temperature 0.0 # 0 = greedy, recommended for this model +``` + +### Prompt format + +The model was trained on this exact layout, with a real BOS token id prepended +and EOS terminating each response: + +``` +### User: +{question} + + ### Assistant: +{response} +``` + +**Build prompts with the bundled formatter** (`infer_chat.py` does this). +Hand-assembling the string produces different token ids at every segment +boundary, because SentencePiece prepends a word-boundary marker per `encode()` +call — the model then sees something it was never trained on. + +```python +import torch +from configs.sft_config import default_config +from data.prompt_formatter import PromptFormatter, Tokenizer +from model.cyberslm import CyberSLM + +cfg = default_config() +cfg.tokenizer.model_path = "tokenizer/tokenizer.model" +cfg.model.max_seq_len = cfg.data.max_seq_len = 2048 + +tok = Tokenizer(cfg.tokenizer.model_path) +fmt = PromptFormatter(cfg=cfg, tokenizer=tok) + +model = CyberSLM(cfg.model) +model.load_state_dict(torch.load("models/instruct.pt", map_location="cpu", + weights_only=False)) +model.eval() + +ids = fmt.format_for_inference({"messages": [{"role": "user", + "content": "What is XSS?"}]}) +out = model.generate(torch.tensor([ids]), max_new_tokens=200, + temperature=0.0, eos_id=tok.eos_id) +print(tok.decode(out[0, len(ids):].tolist())) +``` + +Decoding uses a KV cache — roughly 50–70 tok/s on CPU. + +## Intended use + +Research into small language models; a scaling baseline; a demonstration of a +verified training pipeline. **Not** for security advice, incident response, code +generation, or anything where accuracy matters. + +## Training data + +Not published. Curated cybersecurity instruction data; not redistributed. + +## License + +Apache-2.0 for the code and weights. Verify licensing for downstream use against +the sources the data was curated from. diff --git a/config.json b/config.json new file mode 100644 index 0000000..36ae5f6 --- /dev/null +++ b/config.json @@ -0,0 +1,32 @@ +{ + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 2, + "dtype": "float32", + "eos_token_id": 3, + "head_dim": 64, + "hidden_act": "silu", + "hidden_size": 384, + "initializer_range": 0.02, + "intermediate_size": 1024, + "max_position_embeddings": 2048, + "mlp_bias": false, + "model_type": "llama", + "num_attention_heads": 6, + "num_hidden_layers": 12, + "num_key_value_heads": 6, + "pad_token_id": 0, + "pretraining_tp": 1, + "rms_norm_eps": 1e-06, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "tie_word_embeddings": true, + "transformers_version": "5.15.1", + "use_cache": true, + "vocab_size": 32000 +} diff --git a/cyberslm/model/__init__.py b/cyberslm/model/__init__.py new file mode 100644 index 0000000..1bd5fb5 --- /dev/null +++ b/cyberslm/model/__init__.py @@ -0,0 +1,35 @@ +""" +CyberSLM model package. + +Public API (grows as phases are added): + Phase 1: CyberSLMConfig, default_config, RMSNorm, RotaryPositionEmbedding, apply_rope + Phase 2: MultiHeadSelfAttention, SwiGLUFeedForward + Phase 3: DecoderBlock, CyberSLM, build_model + Phase 4: (training engine lives in cyberslm.training) +""" + +from cyberslm.model.config import CyberSLMConfig, default_config +from cyberslm.model.norm import RMSNorm +from cyberslm.model.rope import RotaryPositionEmbedding, apply_rope +from cyberslm.model.attention import MultiHeadSelfAttention +from cyberslm.model.ffn import SwiGLUFeedForward +from cyberslm.model.block import DecoderBlock +from cyberslm.model.model import CyberSLM, build_model, count_parameters, model_summary + +__all__ = [ + # Phase 1 + "CyberSLMConfig", + "default_config", + "RMSNorm", + "RotaryPositionEmbedding", + "apply_rope", + # Phase 2 + "MultiHeadSelfAttention", + "SwiGLUFeedForward", + # Phase 3 + "DecoderBlock", + "CyberSLM", + "build_model", + "count_parameters", + "model_summary", +] diff --git a/cyberslm/model/attention.py b/cyberslm/model/attention.py new file mode 100644 index 0000000..53fd27e --- /dev/null +++ b/cyberslm/model/attention.py @@ -0,0 +1,302 @@ +""" +Multi-Head Self Attention (MHSA) +================================= +Standard scaled dot-product multi-head self attention with: + - Rotary Position Embedding (RoPE) on queries and keys + - Causal (auto-regressive) masking + - No bias on projection layers + - Pre-norm placement handled by the enclosing DecoderBlock + +Mathematical definition +----------------------- +Given input X ∈ ℝ^{B×T×d}: + + Q = X Wq, K = X Wk, V = X Wv (projections, no bias) + +Split into H heads, each of dimension d_h = d / H: + + Qₕ, Kₕ = RoPE(Qₕ), RoPE(Kₕ) (apply rotary embeddings) + +Scaled dot-product attention per head: + + Aₕ = softmax( (Qₕ Kₕᵀ) / √d_h + mask ) Vₕ + +where mask[i,j] = 0 if j ≤ i else −∞ (causal constraint). + +Concatenate and project: + + output = concat(A₁, ..., A_H) Wo + +Complexity +---------- +Time : O(T² · d) — quadratic in sequence length (standard attention) +Space: O(T² · H) — attention weight matrix per head + +Numerical stability +------------------- +- Scaling by 1/√d_h keeps the pre-softmax logits in a well-conditioned + range, preventing vanishing gradients from very peaked softmax outputs. +- Softmax is computed by PyTorch's numerically stable implementation + (subtract max before exp). +- RoPE is applied in float32 (see rope.py). +- Causal mask adds −∞ (not a large negative number) so masked positions + become exactly 0 after softmax — no gradient leakage. + +FlashAttention compatibility +----------------------------- +The forward pass is written in a way that is structurally compatible with +a future drop-in replacement by ``torch.nn.functional.scaled_dot_product_attention`` +(PyTorch 2.0+) or the ``flash-attn`` library. To migrate: + 1. Replace the manual QKᵀ/softmax/V block with: + F.scaled_dot_product_attention(q, k, v, attn_mask=None, + dropout_p=0.0, is_causal=True) + 2. Remove the manual mask addition (is_causal=True handles it). +""" + +from __future__ import annotations + +import math +from typing import Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +from cyberslm.model.config import CyberSLMConfig +from cyberslm.model.rope import RotaryPositionEmbedding, apply_rope + + +def _causal_bias( + q_len: int, + k_len: int, + past_len: int, + dtype: torch.dtype, + device: torch.device, +) -> Tensor: + """ + Additive causal mask of shape ``(q_len, k_len)`` for a query block that + starts at absolute position ``past_len``. + + Query row ``i`` represents absolute position ``past_len + i`` and may attend + to key columns ``0 .. past_len + i`` inclusive; everything after is -inf. + With ``past_len == 0`` this reduces to the usual upper-triangular mask. + """ + q_pos = torch.arange(q_len, device=device).unsqueeze(1) + past_len # (q,1) + k_pos = torch.arange(k_len, device=device).unsqueeze(0) # (1,k) + return torch.where( + k_pos <= q_pos, + torch.zeros((), dtype=dtype, device=device), + torch.full((), float("-inf"), dtype=dtype, device=device), + ) + + +class MultiHeadSelfAttention(nn.Module): + """ + Multi-Head Self Attention with RoPE and causal masking. + + This module owns the four projection matrices (Wq, Wk, Wv, Wo), + the RoPE cache, and the causal mask buffer. + + Parameters + ---------- + config : CyberSLMConfig + Validated model configuration. + + Attributes + ---------- + q_proj : nn.Linear ``(hidden_dim, hidden_dim)``, no bias + k_proj : nn.Linear ``(hidden_dim, hidden_dim)``, no bias + v_proj : nn.Linear ``(hidden_dim, hidden_dim)``, no bias + o_proj : nn.Linear ``(hidden_dim, hidden_dim)``, no bias + rope : RotaryPositionEmbedding + + Shape + ----- + Input : ``(batch, seq_len, hidden_dim)`` + Output : ``(batch, seq_len, hidden_dim)`` + """ + + def __init__( + self, + config: CyberSLMConfig, + rope: Optional[RotaryPositionEmbedding] = None, + ) -> None: + super().__init__() + self.hidden_dim = config.hidden_dim + self.num_heads = config.num_heads + self.head_dim = config.head_dim + self.scale = 1.0 / math.sqrt(self.head_dim) + self.attn_dropout_p = config.attn_dropout + + # ------------------------------------------------------------------ # + # Projection layers — no bias (modern practice, saves ~4×384 params) # + # ------------------------------------------------------------------ # + self.q_proj = nn.Linear(config.hidden_dim, config.hidden_dim, bias=False) + self.k_proj = nn.Linear(config.hidden_dim, config.hidden_dim, bias=False) + self.v_proj = nn.Linear(config.hidden_dim, config.hidden_dim, bias=False) + self.o_proj = nn.Linear(config.hidden_dim, config.hidden_dim, bias=False) + + # ------------------------------------------------------------------ # + # RoPE cache # + # ------------------------------------------------------------------ # + # The cos/sin tables depend only on (head_dim, max_seq_len, base), so + # every layer's would be byte-identical. CyberSLM builds ONE and passes + # it in; previously each of the 12 layers constructed its own, costing + # ~12 MB of duplicated buffers. Falls back to building its own so the + # module stays usable standalone (tests, ablations). + self.rope = rope if rope is not None else RotaryPositionEmbedding( + head_dim=config.head_dim, + max_seq_len=config.max_seq_len, + base=config.rope_base, + ) + + # Causality is enforced by scaled_dot_product_attention(is_causal=...) + # rather than a materialised (max_seq_len × max_seq_len) mask buffer, + # which previously cost ~67 MB per layer. + + def forward( + self, + x: Tensor, + attention_mask: Optional[Tensor] = None, + return_attn_weights: bool = False, + kv_cache: Optional[Tuple[Tensor, Tensor]] = None, + use_cache: bool = False, + ) -> Tuple[Tensor, Optional[Tensor], Optional[Tuple[Tensor, Tensor]]]: + """ + Compute multi-head self attention. + + Parameters + ---------- + x : Tensor + Input of shape ``(batch, seq_len, hidden_dim)``. + attention_mask : Optional[Tensor] + Key-padding mask of shape ``(batch, seq_len)`` with 1 for real + tokens and 0 for padding. When provided, padded keys are excluded + from every query's attention (in addition to the causal mask). + ``None`` means no padding (the common training case with packed + sequences). + return_attn_weights : bool + If True, also return the attention weight matrix for inspection. + This forces the slower explicit-softmax path; leave False for + training so the fused kernel is used. + + Returns + ------- + output : Tensor + Shape ``(batch, seq_len, hidden_dim)``. + attn_weights : Optional[Tensor] + Shape ``(batch, num_heads, seq_len, seq_len)`` if + ``return_attn_weights=True``, else ``None``. + present : Optional[Tuple[Tensor, Tensor]] + The concatenated ``(k, v)`` for this layer when ``use_cache=True``, + to be fed back on the next decoding step. ``None`` otherwise. + """ + B, T, _ = x.shape + # Number of tokens already in the cache == absolute position of x[0]. + past_len = kv_cache[0].size(2) if kv_cache is not None else 0 + + # ------------------------------------------------------------------ # + # 1. Linear projections # + # ------------------------------------------------------------------ # + q = self.q_proj(x) # (B, T, hidden_dim) + k = self.k_proj(x) + v = self.v_proj(x) + + # ------------------------------------------------------------------ # + # 2. Reshape to (B, H, T, head_dim) for multi-head computation # + # ------------------------------------------------------------------ # + q = q.view(B, T, self.num_heads, self.head_dim).transpose(1, 2) # (B,H,T,D) + k = k.view(B, T, self.num_heads, self.head_dim).transpose(1, 2) + v = v.view(B, T, self.num_heads, self.head_dim).transpose(1, 2) + + # ------------------------------------------------------------------ # + # 3. Apply Rotary Position Embeddings to Q and K # + # ------------------------------------------------------------------ # + # offset=past_len so a cached decode step rotates the new token by its + # TRUE absolute position rather than position 0. + q, k = apply_rope(q, k, self.rope, offset=past_len) + + # ------------------------------------------------------------------ # + # 3b. Prepend the cache. RoPE is applied to the new k BEFORE the + # concat, and cached keys were already rotated when they were first + # computed -- so each key keeps the rotation for its own position. + # ------------------------------------------------------------------ # + if kv_cache is not None: + k = torch.cat([kv_cache[0], k], dim=2) + v = torch.cat([kv_cache[1], v], dim=2) + present = (k, v) if use_cache else None + S = k.size(2) # total key length (past + current) + + # ------------------------------------------------------------------ # + # 4. Build the additive key-padding bias (if any). # + # Shape broadcasts over heads and query positions: (B, 1, 1, T). # + # ------------------------------------------------------------------ # + pad_bias: Optional[Tensor] = None + if attention_mask is not None: + # 0 where padding → -inf added to those key columns. + pad = (attention_mask == 0)[:, None, None, :] # (B,1,1,S) bool + pad_bias = torch.zeros( + (B, 1, 1, pad.size(-1)), dtype=q.dtype, device=q.device + ).masked_fill(pad, float("-inf")) + + if not return_attn_weights: + # Fused, memory-efficient path (FlashAttention when available). + # is_causal=True applies the causal mask without materialising it. + if pad_bias is None and past_len == 0: + context = F.scaled_dot_product_attention( + q, k, v, + is_causal=True, + dropout_p=self.attn_dropout_p if self.training else 0.0, + ) + elif pad_bias is None and T == 1: + # Single-token decode: every cached key is in the past, so the + # causal constraint is already satisfied and no mask is needed. + context = F.scaled_dot_product_attention( + q, k, v, dropout_p=0.0, + ) + else: + # Combine causal + padding into one additive float mask. + # Query i sits at absolute position past_len + i and may attend + # to keys 0..past_len+i, so the triangle is offset by past_len. + causal = _causal_bias(T, S, past_len, q.dtype, q.device) + attn_bias = causal[None, None, :, :] + if pad_bias is not None: + attn_bias = attn_bias + pad_bias # (B,1,T,S) + context = F.scaled_dot_product_attention( + q, k, v, + attn_mask=attn_bias, + dropout_p=self.attn_dropout_p if self.training else 0.0, + ) + attn_weights = None + else: + # Explicit path — needed only when the caller wants the weights. + scores = torch.matmul(q, k.transpose(-2, -1)) * self.scale # (B,H,T,S) + causal = _causal_bias(T, S, past_len, scores.dtype, scores.device) + scores = scores + causal[None, None, :, :] + if pad_bias is not None: + scores = scores + pad_bias + attn_weights = F.softmax(scores, dim=-1, dtype=torch.float32) + if self.attn_dropout_p > 0.0 and self.training: + attn_weights = F.dropout(attn_weights, p=self.attn_dropout_p) + attn_weights = attn_weights.to(v.dtype) + context = torch.matmul(attn_weights, v) # (B,H,T,head_dim) + + # ------------------------------------------------------------------ # + # 8. Merge heads: (B, H, T, D) → (B, T, H*D) = (B, T, hidden_dim) # + # ------------------------------------------------------------------ # + context = context.transpose(1, 2).contiguous().view(B, T, self.hidden_dim) + + # ------------------------------------------------------------------ # + # 9. Output projection # + # ------------------------------------------------------------------ # + output = self.o_proj(context) + + return output, attn_weights, present + + def extra_repr(self) -> str: + return ( + f"hidden_dim={self.hidden_dim}, " + f"num_heads={self.num_heads}, " + f"head_dim={self.head_dim}" + ) diff --git a/cyberslm/model/block.py b/cyberslm/model/block.py new file mode 100644 index 0000000..e518c82 --- /dev/null +++ b/cyberslm/model/block.py @@ -0,0 +1,144 @@ +""" +Transformer Decoder Block +========================== +A single Pre-Norm residual decoder block consisting of: + 1. RMSNorm → Multi-Head Self Attention → residual add + 2. RMSNorm → SwiGLU FFN → residual add + +Pre-Norm architecture +--------------------- +Post-Norm (original Transformer): + x = LayerNorm(x + SubLayer(x)) + +Pre-Norm (modern: GPT-2 onward, LLaMA, etc.): + x = x + SubLayer(LayerNorm(x)) + +Pre-Norm is strongly preferred for deep networks because: + - Gradients flow through the residual connection bypassing the + normalisation, preventing vanishing gradients in very deep stacks. + - Training is more stable without learning-rate warmup tricks. + - Final RMSNorm on the output is added at the model level (not here) + to normalise the final residual stream before the LM head. + +Residual stream +--------------- +The residual stream x ∈ ℝ^{B×T×d} is the backbone of the model. Each +sub-layer reads from it, computes a delta, and adds back: + + Δ_attn = MHSA( RMSNorm(x) ) + x = x + Δ_attn + + Δ_ffn = FFN( RMSNorm(x) ) + x = x + Δ_ffn + +This additive structure means the gradient of the loss with respect to +early layers contains a direct path through the identity (residual), +enabling reliable training of 12+ layer models. +""" + +from __future__ import annotations + +from typing import Optional, Tuple + +import torch +import torch.nn as nn +from torch import Tensor + +from cyberslm.model.config import CyberSLMConfig +from cyberslm.model.norm import RMSNorm +from cyberslm.model.attention import MultiHeadSelfAttention +from cyberslm.model.ffn import SwiGLUFeedForward + + +class DecoderBlock(nn.Module): + """ + Pre-Norm Transformer Decoder Block. + + Parameters + ---------- + config : CyberSLMConfig + Validated model configuration. + layer_idx : int + Zero-based index of this block in the stack (used for display only). + + Sub-modules + ----------- + attn_norm : RMSNorm + Normalises the residual stream before attention. + attn : MultiHeadSelfAttention + Self attention with RoPE and causal masking. + ffn_norm : RMSNorm + Normalises the residual stream before the FFN. + ffn : SwiGLUFeedForward + SwiGLU position-wise feed-forward network. + + Shape + ----- + Input : ``(batch, seq_len, hidden_dim)`` + Output : ``(batch, seq_len, hidden_dim)`` + """ + + def __init__( + self, + config: CyberSLMConfig, + layer_idx: int = 0, + rope=None, + ) -> None: + super().__init__() + self.layer_idx = layer_idx + + # Pre-norm before attention. + self.attn_norm = RMSNorm(config.hidden_dim, eps=config.norm_eps) + # Multi-head self attention (owns RoPE + causal mask buffers). + self.attn = MultiHeadSelfAttention(config, rope=rope) + + # Pre-norm before FFN. + self.ffn_norm = RMSNorm(config.hidden_dim, eps=config.norm_eps) + # SwiGLU feed-forward. + self.ffn = SwiGLUFeedForward(config) + + def forward( + self, + x: Tensor, + attention_mask: Optional[Tensor] = None, + return_attn_weights: bool = False, + kv_cache: Optional[Tuple[Tensor, Tensor]] = None, + use_cache: bool = False, + ) -> Tuple[Tensor, Optional[Tensor], Optional[Tuple[Tensor, Tensor]]]: + """ + Apply one Pre-Norm residual decoder block. + + Parameters + ---------- + x : Tensor + Residual stream of shape ``(batch, seq_len, hidden_dim)``. + attention_mask : Optional[Tensor] + Key-padding mask ``(batch, seq_len)`` (1=keep, 0=pad), forwarded + to the attention sub-layer. ``None`` for packed/unpadded batches. + return_attn_weights : bool + Propagated to the attention sub-layer. + + Returns + ------- + x : Tensor + Updated residual stream, same shape as input. + attn_weights : Optional[Tensor] + Attention weights if requested, else None. + """ + # ---- Attention sub-layer ----------------------------------------- # + attn_out, attn_weights, present = self.attn( + self.attn_norm(x), + attention_mask=attention_mask, + return_attn_weights=return_attn_weights, + kv_cache=kv_cache, + use_cache=use_cache, + ) + x = x + attn_out + + # ---- FFN sub-layer ------------------------------------------------ # + x = x + self.ffn(self.ffn_norm(x)) + + return x, attn_weights, present + + def extra_repr(self) -> str: + return f"layer_idx={self.layer_idx}" diff --git a/cyberslm/model/config.py b/cyberslm/model/config.py new file mode 100644 index 0000000..547a41e --- /dev/null +++ b/cyberslm/model/config.py @@ -0,0 +1,234 @@ +""" +CyberSLM Model Configuration +============================= +Defines the complete, validated configuration for the CyberSLM decoder-only +transformer. All hyperparameters are frozen at construction time and validated +for mathematical consistency before any model component is instantiated. + +Architecture summary +-------------------- +- Hidden dim : 384 +- Decoder layers : 12 +- Attention heads : 6 +- Head dim : 64 (hidden_dim / num_heads = 384 / 6 = 64) +- FFN inner dim : 1024 (SwiGLU gate + value = 2 × 1024 → projects back to 384) +- Vocab size : 32 000 +- Max context : 4 096 +- Approx params : 33.53 M +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Optional + + +@dataclass(frozen=True) +class CyberSLMConfig: + """ + Immutable configuration for the CyberSLM decoder-only transformer. + + All fields are set once at construction; the frozen dataclass guarantees + no accidental mutation during training. Call :meth:`validate` immediately + after construction or use the convenience constructor + :func:`default_config`. + + Attributes + ---------- + vocab_size : int + Number of tokens in the SentencePiece BPE vocabulary. + max_seq_len : int + Maximum token sequence length (context window). + hidden_dim : int + Embedding dimension ``d_model``. + num_layers : int + Number of stacked transformer decoder blocks. + num_heads : int + Number of attention heads. Must evenly divide ``hidden_dim``. + head_dim : int + Dimension of each attention head. Must equal ``hidden_dim // num_heads``. + ffn_hidden_dim : int + Inner dimension of the SwiGLU feed-forward network. + The gate and value projections each map hidden_dim → ffn_hidden_dim, + and the output projection maps ffn_hidden_dim → hidden_dim. + rope_base : int + Base for Rotary Position Embedding frequency computation (θ = 10 000). + norm_eps : float + Epsilon added inside RMSNorm to prevent division by zero. + tie_weights : bool + When True the output projection shares weights with the token embedding. + bias : bool + When True linear layers include a bias term (False = modern practice). + dropout : float + Residual / feed-forward dropout probability (0.0 = disabled). + attn_dropout : float + Attention weight dropout probability (0.0 = disabled). + pad_token_id : Optional[int] + Token ID used for padding; None if the dataset never pads. + bos_token_id : Optional[int] + Beginning-of-sequence token ID. + eos_token_id : Optional[int] + End-of-sequence token ID. + """ + + # ------------------------------------------------------------------ # + # Vocabulary & sequence # + # ------------------------------------------------------------------ # + vocab_size: int = 32_000 + max_seq_len: int = 4_096 + + # ------------------------------------------------------------------ # + # Transformer dimensions # + # ------------------------------------------------------------------ # + hidden_dim: int = 384 + num_layers: int = 12 + num_heads: int = 6 + head_dim: int = 64 # must equal hidden_dim // num_heads + ffn_hidden_dim: int = 1_024 + + # ------------------------------------------------------------------ # + # Positional encoding # + # ------------------------------------------------------------------ # + rope_base: int = 10_000 + + # ------------------------------------------------------------------ # + # Normalization # + # ------------------------------------------------------------------ # + norm_eps: float = 1e-6 + + # ------------------------------------------------------------------ # + # Architecture flags # + # ------------------------------------------------------------------ # + tie_weights: bool = True + bias: bool = False + dropout: float = 0.0 + attn_dropout: float = 0.0 + + # ------------------------------------------------------------------ # + # Special token IDs (set by tokenizer integration layer) # + # ------------------------------------------------------------------ # + pad_token_id: Optional[int] = None + bos_token_id: Optional[int] = 2 # real SentencePiece BOS id + eos_token_id: Optional[int] = 3 # real SentencePiece EOS id + + # ------------------------------------------------------------------ # + # Validation # + # ------------------------------------------------------------------ # + def validate(self) -> "CyberSLMConfig": + """ + Assert mathematical consistency of every hyperparameter. + + Raises + ------ + ValueError + If any hyperparameter violates an architectural constraint. + + Returns + ------- + CyberSLMConfig + Self, to allow chaining: ``cfg = CyberSLMConfig().validate()``. + """ + errors: list[str] = [] + + # Positivity checks + for name, value in [ + ("vocab_size", self.vocab_size), + ("max_seq_len", self.max_seq_len), + ("hidden_dim", self.hidden_dim), + ("num_layers", self.num_layers), + ("num_heads", self.num_heads), + ("head_dim", self.head_dim), + ("ffn_hidden_dim", self.ffn_hidden_dim), + ("rope_base", self.rope_base), + ]: + if value <= 0: + errors.append(f"{name} must be positive, got {value}") + + # Attention head consistency + if self.hidden_dim % self.num_heads != 0: + errors.append( + f"hidden_dim ({self.hidden_dim}) must be divisible by " + f"num_heads ({self.num_heads})" + ) + expected_head_dim = self.hidden_dim // self.num_heads + if self.head_dim != expected_head_dim: + errors.append( + f"head_dim ({self.head_dim}) must equal " + f"hidden_dim // num_heads = {expected_head_dim}" + ) + + # RoPE requires even head_dim (pairs of sin/cos) + if self.head_dim % 2 != 0: + errors.append( + f"head_dim ({self.head_dim}) must be even for RoPE" + ) + + # Dropout bounds + for name, value in [("dropout", self.dropout), ("attn_dropout", self.attn_dropout)]: + if not (0.0 <= value < 1.0): + errors.append(f"{name} must be in [0, 1), got {value}") + + # norm_eps positivity + if self.norm_eps <= 0.0: + errors.append(f"norm_eps must be positive, got {self.norm_eps}") + + if errors: + raise ValueError( + "CyberSLMConfig validation failed:\n" + + "\n".join(f" • {e}" for e in errors) + ) + + return self + + # ------------------------------------------------------------------ # + # Derived properties # + # ------------------------------------------------------------------ # + @property + def total_attention_dim(self) -> int: + """``num_heads × head_dim`` — equals ``hidden_dim`` by construction.""" + return self.num_heads * self.head_dim + + @property + def rope_half_dim(self) -> int: + """Number of frequency pairs in RoPE (``head_dim // 2``).""" + return self.head_dim // 2 + + # ------------------------------------------------------------------ # + # Display # + # ------------------------------------------------------------------ # + def __str__(self) -> str: + lines = [ + "CyberSLMConfig", + "=" * 40, + f" vocab_size : {self.vocab_size:,}", + f" max_seq_len : {self.max_seq_len:,}", + f" hidden_dim : {self.hidden_dim}", + f" num_layers : {self.num_layers}", + f" num_heads : {self.num_heads}", + f" head_dim : {self.head_dim}", + f" ffn_hidden_dim : {self.ffn_hidden_dim}", + f" rope_base : {self.rope_base}", + f" norm_eps : {self.norm_eps}", + f" tie_weights : {self.tie_weights}", + f" bias : {self.bias}", + f" dropout : {self.dropout}", + f" attn_dropout : {self.attn_dropout}", + ] + return "\n".join(lines) + + +def default_config() -> CyberSLMConfig: + """ + Return the validated default CyberSLM configuration. + + This is the single source of truth for all training runs. + All hyperparameters match the finalized architecture specification. + + Returns + ------- + CyberSLMConfig + A validated, immutable configuration object. + """ + cfg = CyberSLMConfig() + cfg.validate() + return cfg diff --git a/cyberslm/model/ffn.py b/cyberslm/model/ffn.py new file mode 100644 index 0000000..4664218 --- /dev/null +++ b/cyberslm/model/ffn.py @@ -0,0 +1,127 @@ +""" +SwiGLU Feed-Forward Network (FFN) +=================================== +Reference: "GLU Variants Improve Transformer" (Noam Shazeer, 2020) + https://arxiv.org/abs/2002.05202 + +Mathematical definition +----------------------- +Standard FFN (for contrast): + FFN(x) = activation(x W₁) W₂ + +SwiGLU FFN: + SwiGLU(x) = (x W_gate ⊙ swish(x W_gate)) W₂ ← WRONG shorthand + +Correct form with separate gate and value projections: + gate(x) = x W_gate ∈ ℝ^{B×T×ffn_dim} + val(x) = x W_val ∈ ℝ^{B×T×ffn_dim} + hidden = swish(gate(x)) ⊙ val(x) + out = hidden W_out ∈ ℝ^{B×T×hidden_dim} + +where swish(z) = z · sigmoid(z) = z · σ(z). + +Why SwiGLU +---------- +- The gating mechanism (⊙) gives the network a multiplicative path to + control information flow — intuitively "how much" of each feature passes + through at each position. +- swish is smooth and non-monotonic, empirically outperforming ReLU and + GELU in large-scale experiments (PaLM, LLaMA, etc.). +- The factored W_gate / W_val structure adds one extra matrix but improves + quality relative to a standard 2-layer FFN of the same parameter budget. + +Parameter count +--------------- +Three matrices: W_gate, W_val, W_out + W_gate : hidden_dim × ffn_hidden_dim (384 × 1024 = 393 216) + W_val : hidden_dim × ffn_hidden_dim (384 × 1024 = 393 216) + W_out : ffn_hidden_dim × hidden_dim (1024 × 384 = 393 216) + Total per layer: 1 179 648 ≈ 1.18 M + +No bias on any projection (consistent with modern practice). + +Numerical stability +------------------- +- swish(z) = z · σ(z) is numerically safe for all real z. +- σ(z) = 1/(1+exp(−z)) in PyTorch uses a numerically stable implementation. +- The element-wise product of two bounded (after sigmoid) quantities does + not amplify values explosively. +""" + +from __future__ import annotations + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +from cyberslm.model.config import CyberSLMConfig + + +class SwiGLUFeedForward(nn.Module): + """ + SwiGLU Feed-Forward Network. + + Consists of three bias-free linear projections: + ``gate_proj``, ``val_proj``, and ``out_proj``. + + Parameters + ---------- + config : CyberSLMConfig + Validated model configuration. + + Shape + ----- + Input : ``(batch, seq_len, hidden_dim)`` + Output : ``(batch, seq_len, hidden_dim)`` + """ + + def __init__(self, config: CyberSLMConfig) -> None: + super().__init__() + self.hidden_dim = config.hidden_dim + self.ffn_hidden_dim = config.ffn_hidden_dim + + # Gate projection: produces the gating signal fed through swish. + self.gate_proj = nn.Linear( + config.hidden_dim, config.ffn_hidden_dim, bias=False + ) + # Value projection: produces the value signal gated element-wise. + self.val_proj = nn.Linear( + config.hidden_dim, config.ffn_hidden_dim, bias=False + ) + # Output projection: maps back to residual stream dimension. + self.out_proj = nn.Linear( + config.ffn_hidden_dim, config.hidden_dim, bias=False + ) + + def forward(self, x: Tensor) -> Tensor: + """ + Apply SwiGLU feed-forward transformation. + + Parameters + ---------- + x : Tensor + Input of shape ``(batch, seq_len, hidden_dim)``. + + Returns + ------- + Tensor + Output of shape ``(batch, seq_len, hidden_dim)``. + """ + # gate: (B, T, ffn_hidden_dim) + # val: (B, T, ffn_hidden_dim) + gate = self.gate_proj(x) + val = self.val_proj(x) + + # SwiGLU: swish(gate) ⊙ val + # F.silu is swish: silu(z) = z * sigmoid(z) + hidden = F.silu(gate) * val # (B, T, ffn_hidden_dim) + + # Project back to hidden_dim + return self.out_proj(hidden) # (B, T, hidden_dim) + + def extra_repr(self) -> str: + return ( + f"hidden_dim={self.hidden_dim}, " + f"ffn_hidden_dim={self.ffn_hidden_dim}" + ) diff --git a/cyberslm/model/model.py b/cyberslm/model/model.py new file mode 100644 index 0000000..d046e37 --- /dev/null +++ b/cyberslm/model/model.py @@ -0,0 +1,514 @@ +""" +CyberSLM — Complete Decoder-Only Language Model +================================================ +Assembles all components into the full model: + + Token Embedding → Decoder Stack (×12) → Final RMSNorm → LM Head + +Weight tying +------------ +The LM head (output projection that maps hidden_dim → vocab_size) shares its +weight matrix with the token embedding (vocab_size × hidden_dim). + +Mathematical justification: both the embedding matrix E and the unembedding +matrix U operate in the same semantic space. Tying U = Eᵀ forces consistency +("a token's output representation should be similar to its input representation"), +reduces parameters by vocab_size × hidden_dim = 32 000 × 384 ≈ 12.3 M, and +empirically improves perplexity on small models. + +Parameter count breakdown +-------------------------- +Component Params +---------------------------------------- ------ +Token embedding (vocab × hidden) 12 288 000 + ↳ shared with LM head (no extra cost) 0 +Decoder blocks × 12: + attn_norm (RMSNorm) 384 ×12 = 4 608 + attn Q/K/V/O proj 589 824 ×12 = 7 077 888 + ffn_norm (RMSNorm) 384 ×12 = 4 608 + ffn gate/val/out 1 179 648 ×12 = 14 155 776 +Final RMSNorm 384 +---------------------------------------- ------ +Total ≈ 33 531 264 (≈33.53 M) + +Note: RoPE buffers and causal mask buffers are NOT parameters. + +Initialisation +-------------- +- Embeddings: N(0, 0.02) — small but non-zero, standard practice. +- All linear weights: N(0, 0.02) +- All RMSNorm γ: 1.0 (already set by RMSNorm.__init__) +- Output projection weights = Embedding weights (weight tying). +- Scaled output projections: attention o_proj and FFN out_proj are + scaled by 1/√(2·num_layers) to prevent residual stream variance + from growing with depth (following GPT-2 / LLaMA init practice). +""" + +from __future__ import annotations + +import math +from typing import Dict, List, Optional, Tuple + +import torch +import torch.nn as nn +from torch import Tensor + +from cyberslm.model.config import CyberSLMConfig, default_config +from cyberslm.model.block import DecoderBlock +from cyberslm.model.norm import RMSNorm +from cyberslm.model.rope import RotaryPositionEmbedding + + +class CyberSLM(nn.Module): + """ + CyberSLM Decoder-Only Transformer. + + Parameters + ---------- + config : CyberSLMConfig + Validated model configuration. + + Attributes + ---------- + config : CyberSLMConfig + embedding : nn.Embedding + Token embedding table, shape ``(vocab_size, hidden_dim)``. + layers : nn.ModuleList[DecoderBlock] + Stack of ``num_layers`` decoder blocks. + final_norm : RMSNorm + Applied to the residual stream after the last block. + lm_head : nn.Linear + Projects hidden_dim → vocab_size. Weight tied to ``embedding``. + """ + + def __init__(self, config: CyberSLMConfig) -> None: + super().__init__() + config.validate() + self.config = config + + # ------------------------------------------------------------------ # + # Token embedding # + # ------------------------------------------------------------------ # + self.embedding = nn.Embedding(config.vocab_size, config.hidden_dim) + + # ------------------------------------------------------------------ # + # Decoder stack # + # ------------------------------------------------------------------ # + # One RoPE table shared by every layer (identical by construction). + self.rope = RotaryPositionEmbedding( + head_dim=config.head_dim, + max_seq_len=config.max_seq_len, + base=config.rope_base, + ) + self.layers = nn.ModuleList( + [ + DecoderBlock(config, layer_idx=i, rope=self.rope) + for i in range(config.num_layers) + ] + ) + + # ------------------------------------------------------------------ # + # Final normalisation # + # ------------------------------------------------------------------ # + self.final_norm = RMSNorm(config.hidden_dim, eps=config.norm_eps) + + # ------------------------------------------------------------------ # + # Language model head # + # ------------------------------------------------------------------ # + # bias=False: unembedding never needs a bias term. + self.lm_head = nn.Linear(config.hidden_dim, config.vocab_size, bias=False) + + # ------------------------------------------------------------------ # + # Weight tying: lm_head.weight ≡ embedding.weight # + # ------------------------------------------------------------------ # + if config.tie_weights: + self.lm_head.weight = self.embedding.weight + + # ------------------------------------------------------------------ # + # Parameter initialisation # + # ------------------------------------------------------------------ # + self._init_weights() + + # ---------------------------------------------------------------------- # + # Initialisation # + # ---------------------------------------------------------------------- # + + def _init_weights(self) -> None: + """ + Initialise all parameters with production-quality values. + + Strategy + -------- + - Embedding : N(0, 0.02) + - All Linear weights : N(0, 0.02) + - o_proj and out_proj: scaled down by 1/√(2·L) where L = num_layers + to stabilise the residual stream variance at initialisation. + - All RMSNorm γ : 1.0 (already set in RMSNorm.__init__) + - All biases : 0.0 (none exist in this config) + """ + std = 0.02 + scaled_std = std / math.sqrt(2.0 * self.config.num_layers) + + for name, module in self.named_modules(): + if isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0.0, std=std) + elif isinstance(module, nn.Linear): + # Scaled init for residual output projections. + if name.endswith("o_proj") or name.endswith("out_proj"): + nn.init.normal_(module.weight, mean=0.0, std=scaled_std) + else: + nn.init.normal_(module.weight, mean=0.0, std=std) + if module.bias is not None: + nn.init.zeros_(module.bias) + + # Weight tying must be re-applied after init because _init_weights + # initialised embedding.weight; lm_head.weight already points to the + # same tensor (Python object reference), so no extra step needed. + # Verify it is still tied. + if self.config.tie_weights: + assert self.lm_head.weight is self.embedding.weight, ( + "Weight tying broken after _init_weights" + ) + + # ---------------------------------------------------------------------- # + # Forward pass # + # ---------------------------------------------------------------------- # + + def forward( + self, + input_ids: Tensor, + attention_mask: Optional[Tensor] = None, + return_all_attn_weights: bool = False, + ) -> Tuple[Tensor, List[Optional[Tensor]]]: + """ + Run the full forward pass. + + Parameters + ---------- + input_ids : Tensor + Long tensor of shape ``(batch, seq_len)`` with token IDs in + ``[0, vocab_size)``. + attention_mask : Optional[Tensor] + Optional key-padding mask ``(batch, seq_len)`` (1=keep, 0=pad). + Pass this when batches contain right-padded sequences so padded + positions do not corrupt real tokens; leave ``None`` for packed, + unpadded training batches. + return_all_attn_weights : bool + If True, collect and return attention weights from every layer. + Disabled by default for training efficiency. + + Returns + ------- + logits : Tensor + Shape ``(batch, seq_len, vocab_size)``. Raw (pre-softmax) scores. + all_attn_weights : List[Optional[Tensor]] + One entry per decoder block; each is either the attention weight + tensor or ``None``. + + Notes + ----- + For language model training the standard loss is: + loss = cross_entropy(logits[:, :-1].reshape(-1, V), + input_ids[:, 1:].reshape(-1)) + where we predict the next token at every position. + """ + # ------------------------------------------------------------------ # + # 1. Token embedding # + # ------------------------------------------------------------------ # + x = self.embedding(input_ids) # (B, T, hidden_dim) + + # ------------------------------------------------------------------ # + # 2. Decoder stack # + # ------------------------------------------------------------------ # + all_attn_weights: List[Optional[Tensor]] = [] + for block in self.layers: + # use_cache=False -> `present` is None; forward() deliberately keeps + # its (logits, attn_weights) return signature unchanged. Cached + # decoding lives in generate() instead of overloading this method. + x, attn_w, _ = block( + x, + attention_mask=attention_mask, + return_attn_weights=return_all_attn_weights, + ) + all_attn_weights.append(attn_w) + + # ------------------------------------------------------------------ # + # 3. Final normalisation # + # ------------------------------------------------------------------ # + x = self.final_norm(x) # (B, T, hidden_dim) + + # ------------------------------------------------------------------ # + # 4. LM head (weight-tied unembedding) # + # ------------------------------------------------------------------ # + logits = self.lm_head(x) # (B, T, vocab_size) + + return logits, all_attn_weights + + + # ---------------------------------------------------------------------- # + # Cached autoregressive generation # + # ---------------------------------------------------------------------- # + + @torch.no_grad() + def generate( + self, + input_ids: Tensor, + max_new_tokens: int = 256, + temperature: float = 0.0, + top_k: int = 0, + top_p: float = 1.0, + repetition_penalty: float = 1.0, + eos_id: Optional[int] = None, + ) -> Tensor: + """ + Generate continuations using a KV cache. + + Why this exists + --------------- + The previous generation loop re-ran the whole 12-layer stack over the + entire prefix for every single token, making decoding O(n^2) in + sequence length. With a cache each step attends over the cached keys and + only computes the new token, which is O(n) overall. + + Sampling is applied per row, so batched prompts are supported. Rows that + have emitted ``eos_id`` are frozen (further tokens are forced to + ``eos_id``) and generation stops once every row is finished. + + Parameters + ---------- + input_ids : Tensor ``(batch, prompt_len)`` of token ids. + temperature : 0.0 selects greedy argmax; >0 samples. + top_k / top_p : 0 and 1.0 respectively disable the filter. + repetition_penalty : >1.0 divides logits of already-present tokens. + eos_id : stop token; ``None`` means never stop early. + + Returns + ------- + Tensor ``(batch, prompt_len + generated)`` including the prompt. + """ + self.eval() + device = input_ids.device + B = input_ids.size(0) + max_ctx = self.config.max_seq_len + + if input_ids.size(1) >= max_ctx: + input_ids = input_ids[:, -(max_ctx - 1):] + + caches: List[Optional[tuple]] = [None] * len(self.layers) + finished = torch.zeros(B, dtype=torch.bool, device=device) + out = input_ids + cur = input_ids + + for _ in range(max_new_tokens): + if out.size(1) >= max_ctx: + break + + h = self.embedding(cur) + new_caches = [] + for block, layer_cache in zip(self.layers, caches): + h, _, present = block(h, kv_cache=layer_cache, use_cache=True) + new_caches.append(present) + caches = new_caches + logits = self.lm_head(self.final_norm(h))[:, -1, :].float() + + if repetition_penalty != 1.0: + for b in range(B): + seen = torch.unique(out[b]) + lg = logits[b, seen] + # Divide positives, multiply negatives, so the penalty always + # pushes a token DOWN regardless of its logit's sign. + logits[b, seen] = torch.where( + lg > 0, lg / repetition_penalty, lg * repetition_penalty + ) + + if temperature == 0.0: + nxt = logits.argmax(dim=-1) + else: + logits = logits / temperature + if top_k > 0: + k = min(top_k, logits.size(-1)) + thresh = torch.topk(logits, k, dim=-1).values[:, -1, None] + logits = logits.masked_fill(logits < thresh, float("-inf")) + if top_p < 1.0: + srt, idx = torch.sort(logits, descending=True, dim=-1) + probs = torch.softmax(srt, dim=-1) + cum = probs.cumsum(dim=-1) - probs # prob mass strictly before this token + srt = srt.masked_fill(cum > top_p, float("-inf")) + logits = torch.full_like(logits, float("-inf")).scatter(1, idx, srt) + nxt = torch.multinomial(torch.softmax(logits, dim=-1), 1).squeeze(-1) + + if eos_id is not None: + nxt = torch.where(finished, torch.full_like(nxt, eos_id), nxt) + finished = finished | (nxt == eos_id) + + cur = nxt.unsqueeze(1) + out = torch.cat([out, cur], dim=1) + + if eos_id is not None and bool(finished.all()): + break + + return out + + # ---------------------------------------------------------------------- # + # Convenience: next-token logits # + # ---------------------------------------------------------------------- # + + def get_next_token_logits(self, input_ids: Tensor) -> Tensor: + """ + Return logits for the next token after the last input position. + + Parameters + ---------- + input_ids : Tensor + Shape ``(batch, seq_len)``. + + Returns + ------- + Tensor + Shape ``(batch, vocab_size)``. + """ + logits, _ = self.forward(input_ids) + return logits[:, -1, :] # (B, vocab_size) + + +# --------------------------------------------------------------------------- # +# Parameter counting # +# --------------------------------------------------------------------------- # + +def count_parameters(model: nn.Module) -> Dict[str, int]: + """ + Count trainable and total parameters. + + Because of weight tying, lm_head.weight is counted only once + (it shares storage with embedding.weight). + + Parameters + ---------- + model : nn.Module + + Returns + ------- + dict with keys: + ``total`` — total parameter elements (no double-counting) + ``trainable`` — trainable parameter elements + """ + seen: set = set() + total = 0 + trainable = 0 + for param in model.parameters(): + # data_ptr() is unique per underlying storage tensor. + if param.data_ptr() in seen: + continue + seen.add(param.data_ptr()) + n = param.numel() + total += n + if param.requires_grad: + trainable += n + return {"total": total, "trainable": trainable} + + +# --------------------------------------------------------------------------- # +# Model summary # +# --------------------------------------------------------------------------- # + +def model_summary(model: CyberSLM) -> str: + """ + Return a human-readable model summary string. + + Parameters + ---------- + model : CyberSLM + + Returns + ------- + str + Formatted multi-line summary including per-component parameter counts. + """ + cfg = model.config + param_info = count_parameters(model) + + lines = [ + "=" * 60, + f" CyberSLM Model Summary", + "=" * 60, + f" Architecture : Decoder-only Transformer", + f" Hidden dim : {cfg.hidden_dim}", + f" Num layers : {cfg.num_layers}", + f" Num heads : {cfg.num_heads}", + f" Head dim : {cfg.head_dim}", + f" FFN hidden dim : {cfg.ffn_hidden_dim}", + f" Vocab size : {cfg.vocab_size:,}", + f" Max seq len : {cfg.max_seq_len:,}", + f" Weight tied : {cfg.tie_weights}", + f" RoPE base : {cfg.rope_base}", + "-" * 60, + f" Total params : {param_info['total']:>14,}", + f" Trainable : {param_info['trainable']:>14,}", + "-" * 60, + " Per-component:", + ] + + # Embedding + emb_p = model.embedding.weight.numel() + lines.append(f" Embedding : {emb_p:>12,}") + + # Per-block breakdown (just the first block, all identical) + block = model.layers[0] + attn_norm_p = sum(p.numel() for p in block.attn_norm.parameters()) + attn_p = sum(p.numel() for p in block.attn.parameters()) + ffn_norm_p = sum(p.numel() for p in block.ffn_norm.parameters()) + ffn_p = sum(p.numel() for p in block.ffn.parameters()) + block_total = attn_norm_p + attn_p + ffn_norm_p + ffn_p + lines.append(f" Decoder block (×{cfg.num_layers:2d}) : {block_total:>12,} per block") + lines.append(f" attn_norm : {attn_norm_p:>12,}") + lines.append(f" attention (Q/K/V/O) : {attn_p:>12,}") + lines.append(f" ffn_norm : {ffn_norm_p:>12,}") + lines.append(f" ffn (gate/val/out) : {ffn_p:>12,}") + lines.append(f" Decoder stack total : {block_total * cfg.num_layers:>12,}") + + # Final norm + final_norm_p = sum(p.numel() for p in model.final_norm.parameters()) + lines.append(f" Final RMSNorm : {final_norm_p:>12,}") + + # LM head — note: weight tied, so 0 additional params + lm_tied_note = " (weight-tied, no extra params)" if cfg.tie_weights else "" + lines.append(f" LM head : {0:>12,}{lm_tied_note}") + + lines.append("=" * 60) + return "\n".join(lines) + + +# --------------------------------------------------------------------------- # +# Model builder # +# --------------------------------------------------------------------------- # + +def build_model( + config: Optional[CyberSLMConfig] = None, + device: Optional[torch.device] = None, +) -> CyberSLM: + """ + Build, initialise, and optionally place the CyberSLM model. + + Parameters + ---------- + config : CyberSLMConfig, optional + Validated config. Uses :func:`default_config` if None. + device : torch.device, optional + Target device. Stays on CPU if None. + + Returns + ------- + CyberSLM + Fully initialised model ready for training. + """ + if config is None: + config = default_config() + else: + config.validate() + + model = CyberSLM(config) + + if device is not None: + model = model.to(device) + + return model diff --git a/cyberslm/model/norm.py b/cyberslm/model/norm.py new file mode 100644 index 0000000..b492d04 --- /dev/null +++ b/cyberslm/model/norm.py @@ -0,0 +1,114 @@ +""" +RMSNorm — Root Mean Square Layer Normalisation +=============================================== +Reference: "Root Mean Square Layer Normalization" (Zhang & Sennrich, 2019) + https://arxiv.org/abs/1910.07467 + +Mathematical definition +----------------------- +Given an input vector **x** ∈ ℝ^d: + + RMS(x) = sqrt( (1/d) * Σ xᵢ² + ε ) + + RMSNorm(x) = (x / RMS(x)) * γ + +where γ ∈ ℝ^d is a learned per-channel scale (initialised to 1.0) and +ε > 0 is a small constant for numerical stability. + +Key differences from LayerNorm +------------------------------- +- No mean subtraction (no re-centering step). +- No learned bias β (the bias-free variant). +- ~30 % fewer operations than LayerNorm, which matters across 12 layers. +- Empirically matches or exceeds LayerNorm in modern transformer training. + +Numerical stability +------------------- +- The RMS is computed in float32 regardless of input dtype. This prevents + underflow/overflow when activations are in bfloat16 or float16. For our + FP32 training runs this cast is a no-op but it is correct and future-proof. +- ε = 1e-6 (default) prevents division by zero even for near-zero inputs. +- The scale γ is cast back to the input dtype before multiplication. +""" + +from __future__ import annotations + +import torch +import torch.nn as nn +from torch import Tensor + + +class RMSNorm(nn.Module): + """ + Root Mean Square Layer Normalisation without mean-centering or bias. + + Parameters + ---------- + dim : int + Feature dimension to normalise over (last dimension of the input). + eps : float + Small constant added to the RMS denominator for numerical stability. + Defaults to 1e-6. + + Shape + ----- + Input : ``(*, dim)`` — any leading batch / sequence dimensions. + Output : ``(*, dim)`` — same shape as input. + + Examples + -------- + >>> norm = RMSNorm(384, eps=1e-6) + >>> x = torch.randn(2, 512, 384) + >>> y = norm(x) + >>> y.shape + torch.Size([2, 512, 384]) + """ + + def __init__(self, dim: int, eps: float = 1e-6) -> None: + super().__init__() + if dim <= 0: + raise ValueError(f"dim must be positive, got {dim}") + if eps <= 0.0: + raise ValueError(f"eps must be positive, got {eps}") + + self.dim = dim + self.eps = eps + # Learned per-channel scale, initialised to 1 (identity transform). + self.weight = nn.Parameter(torch.ones(dim)) + + def _compute_rms(self, x: Tensor) -> Tensor: + """ + Compute the RMS over the last dimension. + + Always promotes to float32 to prevent numerical issues with + reduced-precision dtypes. For FP32 training this is a no-op. + + Returns + ------- + Tensor + Shape ``(*, 1)`` — one RMS value per token position. + """ + return x.float().pow(2).mean(dim=-1, keepdim=True).add(self.eps).sqrt() + + def forward(self, x: Tensor) -> Tensor: + """ + Normalise ``x`` by its per-token root mean square. + + Parameters + ---------- + x : Tensor + Input of shape ``(*, dim)``. + + Returns + ------- + Tensor + Normalised output of the same shape and dtype as ``x``. + """ + rms = self._compute_rms(x) + # Normalise in float32, then cast back to original dtype. + x_normed = x.float() / rms + # Scale by learned weights (cast to input dtype for type safety). + return (x_normed * self.weight.float()).to(x.dtype) + + def extra_repr(self) -> str: + return f"dim={self.dim}, eps={self.eps}" diff --git a/cyberslm/model/rope.py b/cyberslm/model/rope.py new file mode 100644 index 0000000..7222c0e --- /dev/null +++ b/cyberslm/model/rope.py @@ -0,0 +1,291 @@ +""" +Rotary Position Embedding (RoPE) +================================= +Reference: "RoFormer: Enhanced Transformer with Rotary Position Embedding" + (Su et al., 2021) — https://arxiv.org/abs/2104.09864 + +Mathematical definition +----------------------- +For a query (or key) vector **q** at position ``m`` with head dimension ``d``: + + 1. Partition **q** into pairs: (q₁, q₂), (q₃, q₄), ..., (q_{d-1}, q_d). + + 2. For each pair index ``i ∈ {0, 1, ..., d/2 - 1}`` define the frequency: + + θᵢ = 1 / base^(2i / d) (base = 10 000) + + 3. Apply a 2-D rotation to each pair at position ``m``: + + R(m, θᵢ) · (q_{2i}, q_{2i+1}) = + (q_{2i} cos(m·θᵢ) − q_{2i+1} sin(m·θᵢ), + q_{2i} sin(m·θᵢ) + q_{2i+1} cos(m·θᵢ)) + +This is equivalent to multiplying **q** (viewed as complex numbers) by +``exp(i · m · θ)``, which preserves the inner product of relative positions: + + ⟨R(m)q, R(n)k⟩ depends only on (m − n), + +giving translation-equivariant attention without absolute position tokens. + +Efficient implementation +------------------------ +The rotation can be expressed without complex arithmetic: + + q_rot = [q_even · cos − q_odd · sin, + q_even · sin + q_odd · cos] + +where ``q_even = q[..., 0::2]``, ``q_odd = q[..., 1::2]``. + +Numerically interleaved form (even/odd) vs. split-half form (first/second half) +are equivalent; we use the interleaved form for clarity. + +Precomputation +-------------- +``cos`` and ``sin`` tensors of shape ``(max_seq_len, head_dim // 2)`` are +computed once and registered as non-parameter buffers so they move with the +module (CPU ↔ GPU) and are not included in ``state_dict`` checkpoints. + +Stability notes +--------------- +- Frequencies are computed in float64 then cast to float32 to minimise + floating-point error in ``pow`` and ``arange``. +- All rotations are executed in float32 to prevent loss of precision. +""" + +from __future__ import annotations + +import math +from typing import Tuple + +import torch +import torch.nn as nn +from torch import Tensor + + +class RotaryPositionEmbedding(nn.Module): + """ + Precomputed Rotary Position Embedding cache. + + Registers ``cos`` and ``sin`` buffers of shape + ``(max_seq_len, head_dim // 2)`` at construction time. Applying RoPE + to a query or key tensor costs only element-wise multiplications and + additions — no matrix multiplications. + + Parameters + ---------- + head_dim : int + Dimension of each attention head. Must be even. + max_seq_len : int + Maximum sequence length to pre-compute. Sequences longer than this + will raise an error at runtime. + base : int + RoPE base frequency (10 000 in the original paper). + + Shape of ``apply`` + ------------------ + Input : ``(batch, num_heads, seq_len, head_dim)`` + Output : ``(batch, num_heads, seq_len, head_dim)`` + """ + + def __init__( + self, + head_dim: int, + max_seq_len: int, + base: int = 10_000, + ) -> None: + super().__init__() + + if head_dim <= 0 or head_dim % 2 != 0: + raise ValueError( + f"head_dim must be a positive even integer, got {head_dim}" + ) + if max_seq_len <= 0: + raise ValueError(f"max_seq_len must be positive, got {max_seq_len}") + if base <= 0: + raise ValueError(f"base must be positive, got {base}") + + self.head_dim = head_dim + self.max_seq_len = max_seq_len + self.base = base + + # Pre-compute and register buffers (not model parameters). + cos_cache, sin_cache = self._build_cache(head_dim, max_seq_len, base) + self.register_buffer("cos_cache", cos_cache, persistent=False) + self.register_buffer("sin_cache", sin_cache, persistent=False) + + @staticmethod + def _build_cache( + head_dim: int, + max_seq_len: int, + base: int, + ) -> Tuple[Tensor, Tensor]: + """ + Build ``(cos, sin)`` caches of shape ``(max_seq_len, head_dim // 2)``. + + Computation is performed in float64 for precision, then cast to + float32 for storage. + + Returns + ------- + Tuple[Tensor, Tensor] + ``cos_cache`` and ``sin_cache``, each of shape + ``(max_seq_len, head_dim // 2)``. + """ + half_dim = head_dim // 2 + + # θᵢ = 1 / base^(2i / head_dim) for i ∈ {0, ..., half_dim - 1} + # Computed in float64 to avoid precision loss in the exponent. + inv_freq = 1.0 / ( + base ** (torch.arange(0, head_dim, 2, dtype=torch.float64) / head_dim) + ) + # Shape: (half_dim,) + + # Position indices m ∈ {0, 1, ..., max_seq_len - 1} + positions = torch.arange(max_seq_len, dtype=torch.float64) + # Shape: (max_seq_len,) + + # Outer product: angles[m, i] = m * θᵢ + angles = torch.outer(positions, inv_freq) + # Shape: (max_seq_len, half_dim) + + cos_cache = angles.cos().to(torch.float32) + sin_cache = angles.sin().to(torch.float32) + + return cos_cache, sin_cache + + @staticmethod + def _rotate_half(x: Tensor) -> Tensor: + """ + Rotate the last dimension by interleaving even/odd pairs. + + For input ``x`` of shape ``(..., head_dim)``: + + x_even = x[..., 0::2] (positions 0, 2, 4, ...) + x_odd = x[..., 1::2] (positions 1, 3, 5, ...) + + Returns ``[-x_odd, x_even]`` interleaved back into ``(..., head_dim)``. + This is the standard rotation that implements the complex-number trick. + + Parameters + ---------- + x : Tensor + Shape ``(..., head_dim)`` where ``head_dim`` is even. + + Returns + ------- + Tensor + Same shape as ``x``. + """ + x_even = x[..., 0::2] # (..., head_dim // 2) + x_odd = x[..., 1::2] # (..., head_dim // 2) + # Interleave: stack along new dim then flatten. + rotated = torch.stack([-x_odd, x_even], dim=-1) + # Shape: (..., head_dim // 2, 2) → (..., head_dim) + return rotated.flatten(start_dim=-2) + + def apply(self, x: Tensor, offset: int = 0) -> Tensor: + """ + Apply Rotary Position Embeddings to ``x``. + + Parameters + ---------- + x : Tensor + Query or key tensor of shape + ``(batch, num_heads, seq_len, head_dim)``. + offset : int + Absolute position of ``x[..., 0, :]`` in the full sequence. + + This is what makes incremental decoding correct. With a KV cache the + model feeds one token at a time, so ``seq_len == 1`` and the naive + ``cos_cache[:1]`` would rotate every generated token as if it were at + position 0 -- destroying all positional information after the prompt. + Passing ``offset=len(cache)`` selects the true absolute position. + + Returns + ------- + Tensor + Rotated tensor with the same shape and dtype as ``x``. + + Raises + ------ + ValueError + If ``seq_len`` exceeds ``max_seq_len``. + """ + seq_len = x.size(2) + if offset < 0: + raise ValueError(f"offset must be >= 0, got {offset}") + if offset + seq_len > self.max_seq_len: + raise ValueError( + f"Positions [{offset}, {offset + seq_len}) exceed RoPE cache size " + f"{self.max_seq_len}. Re-instantiate with a larger max_seq_len." + ) + + # Retrieve cached values for this absolute position span. + # cos_cache: (seq_len, head_dim // 2) + # sin_cache: (seq_len, head_dim // 2) + cos = self.cos_cache[offset : offset + seq_len] # type: ignore[index] + sin = self.sin_cache[offset : offset + seq_len] # type: ignore[index] + + # Expand to broadcast over batch and head dimensions: + # (1, 1, seq_len, head_dim // 2) → broadcasts with (B, H, T, D/2) + cos = cos.unsqueeze(0).unsqueeze(0) + sin = sin.unsqueeze(0).unsqueeze(0) + + # Interleave cos and sin to match full head_dim. + # Each of (cos, sin) has shape (1, 1, T, D/2). + # We need (1, 1, T, D) by interleaving even positions with cos, + # odd positions with sin. The _rotate_half trick handles this: + # + # x_rot = x * cos_full + rotate_half(x) * sin_full + # + # where cos_full[..., 0::2] = cos and cos_full[..., 1::2] = cos, + # i.e. each cos value applies to both the even AND its paired odd slot. + # Achieved by repeating each half-dim value into both slots. + cos_full = cos.repeat_interleave(2, dim=-1) # (1, 1, T, D) + sin_full = sin.repeat_interleave(2, dim=-1) # (1, 1, T, D) + + # Work in float32 for stability, then restore original dtype. + x_fp32 = x.float() + x_rot = x_fp32 * cos_full + self._rotate_half(x_fp32) * sin_full + return x_rot.to(x.dtype) + + def forward(self, x: Tensor, offset: int = 0) -> Tensor: + """Alias for :meth:`apply` to support ``nn.Sequential`` usage.""" + return self.apply(x, offset=offset) + + def extra_repr(self) -> str: + return ( + f"head_dim={self.head_dim}, " + f"max_seq_len={self.max_seq_len}, " + f"base={self.base}" + ) + + +# --------------------------------------------------------------------------- +# Functional helper +# --------------------------------------------------------------------------- + +def apply_rope( + q: Tensor, + k: Tensor, + rope: RotaryPositionEmbedding, + offset: int = 0, +) -> Tuple[Tensor, Tensor]: + """ + Apply the same RoPE instance to both query and key tensors. + + Parameters + ---------- + q : Tensor + Query tensor of shape ``(batch, num_heads, seq_len, head_dim)``. + k : Tensor + Key tensor of shape ``(batch, num_heads, seq_len, head_dim)``. + rope : RotaryPositionEmbedding + Pre-built RoPE module (carries the cos/sin cache on the correct device). + + Returns + ------- + Tuple[Tensor, Tensor] + ``(q_rot, k_rot)`` — rotated queries and keys. + """ + return rope.apply(q, offset=offset), rope.apply(k, offset=offset) diff --git a/cyberslm_sft/configs/__init__.py b/cyberslm_sft/configs/__init__.py new file mode 100644 index 0000000..a55a0ad --- /dev/null +++ b/cyberslm_sft/configs/__init__.py @@ -0,0 +1,4 @@ +# configs/__init__.py +from configs.sft_config import SFTConfig, default_config, save_config, load_config + +__all__ = ["SFTConfig", "default_config", "save_config", "load_config"] diff --git a/cyberslm_sft/configs/sft_config.py b/cyberslm_sft/configs/sft_config.py new file mode 100644 index 0000000..c9f63e3 --- /dev/null +++ b/cyberslm_sft/configs/sft_config.py @@ -0,0 +1,311 @@ +""" +CyberSLM SFT Configuration +=========================== +Centralised dataclass for all hyperparameters and paths used +in the Supervised Instruction Fine-Tuning pipeline. + +Path wiring +----------- +All default paths are resolved as absolute paths relative to the +``cyberslm_sft/`` project root so the pipeline works regardless of +where you invoke it from. + +Quick setup — copy Stage 1 artifacts into the SFT project:: + + # 1. Copy tokenizer + cp "SLm Dataset/tokenizer/tokenizer_output/tokenizer.model" \\ + "SLm Dataset/cyberslm_sft/tokenizer/tokenizer.model" + + # 2. Copy pretrained weights + cp "SLm Dataset/cyberslm/checkpoints/best.pt" \\ + "SLm Dataset/cyberslm_sft/checkpoints/pretrained/model.pt" + + # 3. Put your instruction dataset in data/ + # data/train.jsonl (required) + # data/val.jsonl (optional) +""" + +from __future__ import annotations + +import json +import os +from dataclasses import asdict, dataclass, field +from pathlib import Path +from typing import Optional + +# --------------------------------------------------------------------------- +# Project root resolution +# --------------------------------------------------------------------------- +# _PROJECT_ROOT = .../cyberslm_sft/ +_PROJECT_ROOT = Path(__file__).resolve().parent.parent + +# Stage 1 dataset root (one level above cyberslm_sft/) — used only as fallback +_DATASET_ROOT = _PROJECT_ROOT.parent # .../SLm Dataset/ + + +# --------------------------------------------------------------------------- +# Model Architecture (must match pretrained checkpoint — never modify here) +# --------------------------------------------------------------------------- + +@dataclass +class ModelConfig: + """ + Mirrors the pretrained CyberSLM architecture exactly. + These values are frozen; do NOT change them for SFT. + """ + vocab_size: int = 32_000 + hidden_size: int = 384 + num_layers: int = 12 + num_heads: int = 6 + head_dim: int = 64 + ffn_size: int = 1024 + max_seq_len: int = 2048 # MUST match the pretrained checkpoint + weight_tying: bool = True + bias: bool = False + dropout: float = 0.0 + norm_eps: float = 1e-6 # RMSNorm epsilon — must match pretraining + + +# --------------------------------------------------------------------------- +# Tokenizer +# --------------------------------------------------------------------------- + +@dataclass +class TokenizerConfig: + """ + Paths and special token ids for the SentencePiece BPE tokenizer. + + model_path + Absolute path to ``tokenizer.model``. + Default: ``/tokenizer/tokenizer.model`` + (auto-falls back to Stage 1 canonical path if the local copy is absent) + """ + model_path: str = str(_PROJECT_ROOT / "tokenizer" / "tokenizer.model") + # Real SentencePiece token ids — verified against tokenizer.model + pad_id: int = 0 + bos_id: int = 2 # (NOT 1) + eos_id: int = 3 # (NOT 2) + unk_id: int = 1 # + + +# --------------------------------------------------------------------------- +# Data +# --------------------------------------------------------------------------- + +@dataclass +class DataConfig: + """Dataset paths and processing knobs.""" + train_path: str = str(_PROJECT_ROOT / "data" / "SFT.jsonl") + val_path: str = "" # empty = auto-split from train_path using val_split + + # Maximum token length per sample (hard-truncate if exceeded). + # Must be <= ModelConfig.max_seq_len. + max_seq_len: int = 2048 + + # Fraction of training data to use as validation when no val_path + # is provided (ignored if val_path exists). + val_split: float = 0.05 + + # DataLoader workers (set 0 for debugging) + num_workers: int = 0 + prefetch_factor: Optional[int] = None + + # Whether to shuffle the training set each epoch + shuffle: bool = True + + # Seed used for the val split and shuffling + seed: int = 42 + + # Cap on total samples loaded (-1 = no cap); useful for quick smoke tests + max_samples: int = -1 + + +# --------------------------------------------------------------------------- +# Training +# --------------------------------------------------------------------------- + +@dataclass +class TrainConfig: + """ + Optimizer, scheduler, and loop settings. + + LR is intentionally much lower than typical pretraining (~1e-3 to 3e-4). + SFT is refinement, not relearning. + """ + # ---- Optimiser ---- + learning_rate: float = 2e-5 # Peak LR after warmup + weight_decay: float = 0.01 + beta1: float = 0.9 + beta2: float = 0.95 + eps: float = 1e-8 + + # ---- Gradient ---- + max_grad_norm: float = 1.0 + gradient_accumulation_steps: int = 4 # Effective batch = batch_size × accum + + # ---- Batch ---- + per_device_batch_size: int = 4 + + # ---- Schedule ---- + num_epochs: int = 3 + warmup_ratio: float = 0.03 # Fraction of total steps for warmup + lr_schedule: str = "cosine" # "cosine" | "linear" | "constant" + min_lr_ratio: float = 0.1 # min_lr = learning_rate × min_lr_ratio + + # ---- Precision ---- + dtype: str = "bfloat16" # bf16 on GPU; auto-disabled on CPU + + # ---- Reproducibility ---- + seed: int = 42 + + # ---- Logging ---- + log_every_n_steps: int = 10 + eval_every_n_steps: int = 200 # 0 = eval only at epoch end + save_every_n_steps: int = 500 # 0 = save only at epoch end + + # ---- Output ---- + output_dir: str = str(_PROJECT_ROOT / "checkpoints") + run_name: str = "cyberslm-instruct" + + # ---- Resume ---- + resume_from_checkpoint: Optional[str] = None # Path to checkpoint dir + + # ---- Base model ---- + pretrained_checkpoint: str = str( + _PROJECT_ROOT / "checkpoints" / "pretrained" / "model.pt" + ) + + # ---- Inference sanity check ---- + run_inference_test: bool = True + max_new_tokens: int = 256 + temperature: float = 0.7 + top_p: float = 0.9 + + +# --------------------------------------------------------------------------- +# Prompt / Template +# --------------------------------------------------------------------------- + +@dataclass +class TemplateConfig: + """ + Controls which prompt format is used to wrap instruction samples. + The SFT loss is only computed on the *response* portion. + """ + # Token that separates instruction/input from the response. + # Loss masking begins immediately after this string (inclusive of the + # newline that follows it). + response_prefix: str = "### Response:\n" + + # NOTE: end-of-sequence is handled as a real token id (```` == 3), + # NOT a literal string. Appending the characters ``""`` would train + # the model to emit text that the sampler never stops on. Kept as an empty + # string so no literal EOS text is injected anywhere in the pipeline. + eos_string: str = "" + + # Whether to strip leading/trailing whitespace from each field + strip_fields: bool = True + + +# --------------------------------------------------------------------------- +# Master Config +# --------------------------------------------------------------------------- + +@dataclass +class SFTConfig: + """ + Top-level configuration. Pass this object through the entire pipeline. + + Usage:: + + from configs.sft_config import SFTConfig, load_config, save_config + + cfg = SFTConfig() + # or + cfg = load_config("my_run/sft_config.json") + """ + model: ModelConfig = field(default_factory=ModelConfig) + tokenizer: TokenizerConfig = field(default_factory=TokenizerConfig) + data: DataConfig = field(default_factory=DataConfig) + train: TrainConfig = field(default_factory=TrainConfig) + template: TemplateConfig = field(default_factory=TemplateConfig) + + def __post_init__(self) -> None: + # Ensure max_seq_len for data never exceeds the model's context window + if self.data.max_seq_len > self.model.max_seq_len: + raise ValueError( + f"DataConfig.max_seq_len ({self.data.max_seq_len}) exceeds " + f"ModelConfig.max_seq_len ({self.model.max_seq_len}). " + "Truncate to the model context window or below." + ) + + +# --------------------------------------------------------------------------- +# Serialisation helpers +# --------------------------------------------------------------------------- + +def save_config(cfg: SFTConfig, path: str | Path) -> None: + """Serialise an SFTConfig to a JSON file.""" + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + with open(path, "w", encoding="utf-8") as fh: + json.dump(asdict(cfg), fh, indent=2) + + +def load_config(path: str | Path) -> SFTConfig: + """ + Deserialise an SFTConfig from a JSON file produced by ``save_config``. + Provides forward-compatibility: unknown keys in the file are silently + ignored so old checkpoints can still be loaded after field additions. + """ + with open(path, "r", encoding="utf-8") as fh: + raw: dict = json.load(fh) + + def _filter(dc_type, d: dict) -> dict: + """Return only keys that exist in the target dataclass.""" + valid = {f.name for f in dc_type.__dataclass_fields__.values()} # type: ignore[attr-defined] + return {k: v for k, v in d.items() if k in valid} + + model_cfg = ModelConfig(**_filter(ModelConfig, raw.get("model", {}))) + tok_cfg = TokenizerConfig(**_filter(TokenizerConfig, raw.get("tokenizer", {}))) + data_cfg = DataConfig(**_filter(DataConfig, raw.get("data", {}))) + train_cfg = TrainConfig(**_filter(TrainConfig, raw.get("train", {}))) + tmpl_cfg = TemplateConfig(**_filter(TemplateConfig, raw.get("template", {}))) + + return SFTConfig( + model=model_cfg, + tokenizer=tok_cfg, + data=data_cfg, + train=train_cfg, + template=tmpl_cfg, + ) + + +def default_config() -> SFTConfig: + """ + Return a default SFTConfig with all paths resolved to absolute paths + inside the ``cyberslm_sft/`` project root. + + Tokenizer fallback + ------------------ + If ``tokenizer/tokenizer.model`` doesn't exist locally, the function + automatically falls back to the canonical Stage 1 path at + ``../tokenizer/tokenizer_output/tokenizer.model``. + """ + cfg = SFTConfig() + + # Auto-fallback to Stage 1 tokenizer if local copy is absent + local_tok = Path(cfg.tokenizer.model_path) + canonical_tok = _DATASET_ROOT / "tokenizer" / "tokenizer_output" / "tokenizer.model" + if not local_tok.exists() and canonical_tok.exists(): + import warnings + warnings.warn( + f"Local tokenizer not found at {local_tok}.\n" + f"Falling back to Stage 1 tokenizer at {canonical_tok}.\n" + "Copy it with:\n" + f" cp '{canonical_tok}' '{local_tok}'", + stacklevel=2, + ) + cfg.tokenizer.model_path = str(canonical_tok) + + return cfg diff --git a/cyberslm_sft/data/__init__.py b/cyberslm_sft/data/__init__.py new file mode 100644 index 0000000..92d3ebb --- /dev/null +++ b/cyberslm_sft/data/__init__.py @@ -0,0 +1,9 @@ +# data/__init__.py (inference-only subset) +# +# The full package also re-exports the dataset loader, validator, collator and +# loss masking. Those are training-only and are deliberately not shipped here, +# so importing them would fail. Only the prompt formatter is needed to build a +# prompt the model recognises. +from data.prompt_formatter import PromptFormatter, Tokenizer, IGNORE_INDEX + +__all__ = ["PromptFormatter", "Tokenizer", "IGNORE_INDEX"] diff --git a/cyberslm_sft/data/prompt_formatter.py b/cyberslm_sft/data/prompt_formatter.py new file mode 100644 index 0000000..7bcbad0 --- /dev/null +++ b/cyberslm_sft/data/prompt_formatter.py @@ -0,0 +1,348 @@ +""" +CyberSLM SFT — Prompt Formatter +================================ +Converts normalised raw samples into tokenised ``(input_ids, labels)`` +pairs ready for the data collator. + +Tokenisation strategy (segment-based) +------------------------------------- +A sample is decomposed into an ordered list of ``(text, is_loss_target)`` +segments (see ``ConversationTemplate`` for the canonical layout). Each +segment is encoded independently and the resulting token id lists are +concatenated to form ``input_ids``. ``labels`` mirror ``input_ids`` but with +non-target (prompt) positions set to ``IGNORE_INDEX`` (-100). + +Why segment-based (and not char-offset) masking +------------------------------------------------ +Locating the response boundary by re-encoding a prefix string and comparing +token counts is fragile: with ``add_dummy_prefix`` (and BPE merges across the +boundary) ``len(encode(prefix))`` need not equal the number of full-sequence +tokens covering that prefix, so the mask can drift by 1-2 tokens per turn. +Encoding each segment and concatenating gives an **exact** boundary because +``input_ids`` and ``labels`` are built from the very same token lists. + +Special tokens (critical) +-------------------------- +Special tokens are referenced by **id**, never as literal strings. The real +SentencePiece control ids are ``=2`` and ``=3``. Every assistant +response is terminated with ``eos_id`` as a genuine token and that ``eos_id`` +is **left unmasked** so the model learns to stop. ``bos_id`` is prepended once +at the start of the sequence (masked). This replaces the previous, broken +approach of appending the literal characters ``""`` (which SentencePiece +encodes as ``<``,``/``,``s``,``>`` — never id 3), which prevented the model +from ever learning an end-of-sequence signal. +""" + +from __future__ import annotations + +import logging +from typing import List, Optional, Tuple + +from configs.sft_config import SFTConfig, TemplateConfig + +logger = logging.getLogger(__name__) + +# Tokens with this label are excluded from the cross-entropy loss. +IGNORE_INDEX: int = -100 + +# One segment of the rendered prompt: (text, is_loss_target). +Segment = Tuple[str, bool] + + +# --------------------------------------------------------------------------- +# Segment builders (string level, EOS handled at token level downstream) +# --------------------------------------------------------------------------- + +def build_alpaca_segments( + instruction: str, + output: str, + input_text: str = "", + tmpl: Optional[TemplateConfig] = None, + include_response: bool = True, +) -> List[Segment]: + """ + Build the ordered ``(text, is_loss_target)`` segments for an alpaca sample. + + The response text is a loss target; everything else (instruction, input, + the response header) is masked. No EOS string is injected here — the + end-of-sequence token is appended as a real token id by + :func:`encode_segments`. + """ + if tmpl is None: + tmpl = TemplateConfig() + + if tmpl.strip_fields: + instruction = instruction.strip() + input_text = input_text.strip() + output = output.strip() + + if input_text: + prompt = ( + f"### Instruction:\n{instruction}\n\n" + f"### Input:\n{input_text}\n\n" + f"{tmpl.response_prefix}" + ) + else: + prompt = ( + f"### Instruction:\n{instruction}\n\n" + f"{tmpl.response_prefix}" + ) + + segments: List[Segment] = [(prompt, False)] + if include_response: + segments.append((output, True)) + return segments + + +def build_conversation_segments( + messages: List[dict], + tmpl: Optional[TemplateConfig] = None, + include_final_response: bool = True, +) -> List[Segment]: + """ + Build ordered ``(text, is_loss_target)`` segments for a multi-turn + conversation. Only assistant response bodies are loss targets. + """ + if tmpl is None: + tmpl = TemplateConfig() + + def _clean(s: str) -> str: + return s.strip() if tmpl.strip_fields else s + + segments: List[Segment] = [] + + # Optional leading system message. + body = messages + if messages and messages[0].get("role") == "system": + sys_content = _clean(messages[0]["content"]) + if sys_content: + segments.append((f"System: {sys_content}\n\n", False)) + body = messages[1:] + + i = 0 + n = len(body) + while i < n: + role = body[i]["role"] + content = _clean(body[i]["content"]) + + if role == "user": + segments.append((f"### User:\n{content}\n\n", False)) + # Pair with the following assistant turn, if present. + if i + 1 < n and body[i + 1]["role"] == "assistant": + asst = _clean(body[i + 1]["content"]) + is_last = (i + 2 >= n) + segments.append(("### Assistant:\n", False)) + if not (is_last and not include_final_response): + segments.append((asst, True)) + segments.append(("\n\n", False)) + i += 2 + else: + # Dangling user turn with no assistant reply -- the normal + # generation case. The assistant header MUST still be emitted: + # it is the cue the model was trained to continue from. Omitting + # it (the previous behaviour) handed the model a prompt shaped + # unlike anything in its training distribution. + segments.append(("### Assistant:\n", False)) + i += 1 + + elif role == "assistant": + asst = content + segments.append(("### Assistant:\n", False)) + segments.append((asst, True)) + segments.append(("\n\n", False)) + i += 1 + + else: + # Mid-conversation system message: treat as masked context. + segments.append((f"### System:\n{content}\n\n", False)) + i += 1 + + return segments + + +# --------------------------------------------------------------------------- +# Tokeniser wrapper +# --------------------------------------------------------------------------- + +class Tokenizer: + """ + Thin wrapper around a SentencePiece model that exposes only what the + formatter and collator need. + + SentencePiece is not imported at module level so the formatter module can + be imported in unit tests without requiring sentencepiece to be installed. + """ + + def __init__(self, model_path: str) -> None: + try: + import sentencepiece as spm # type: ignore + except ImportError as exc: + raise ImportError( + "sentencepiece is required for the tokenizer. " + "Install with: pip install sentencepiece" + ) from exc + + self._sp = spm.SentencePieceProcessor() + self._sp.Load(model_path) + + self.bos_id: int = self._sp.bos_id() + self.eos_id: int = self._sp.eos_id() + self.pad_id: int = self._sp.pad_id() + self.vocab_size: int = self._sp.GetPieceSize() + + # Fail loudly if the tokenizer's control ids do not match the ids the + # rest of the pipeline (and the base model config) assume. A silent + # mismatch here corrupts training and breaks generation stopping. + if self.eos_id < 0: + raise ValueError( + "Tokenizer has no EOS id (eos_id < 0). The SFT pipeline " + "requires a real end-of-sequence token." + ) + + # ------------------------------------------------------------------ + def encode( + self, + text: str, + add_bos: bool = False, + add_eos: bool = False, + ) -> List[int]: + ids: List[int] = self._sp.Encode(text, out_type=int) + if add_bos and self.bos_id >= 0: + ids = [self.bos_id] + ids + if add_eos and self.eos_id >= 0: + ids = ids + [self.eos_id] + return ids + + def decode(self, ids: List[int]) -> str: + return self._sp.Decode(ids) + + def __len__(self) -> int: + return self.vocab_size + + +# --------------------------------------------------------------------------- +# Segment → token id encoding +# --------------------------------------------------------------------------- + +def encode_segments( + segments: List[Segment], + tokenizer: Tokenizer, + max_seq_len: int, + add_bos: bool = True, +) -> Tuple[List[int], List[int]]: + """ + Encode ``(text, is_loss_target)`` segments into ``(input_ids, labels)``. + + * ``input_ids`` is the concatenation of each segment's token ids. + * A real ``eos_id`` token is appended immediately after every loss-target + (assistant) segment and is itself a loss target — the model must learn + to emit it. + * ``bos_id`` is prepended once at the start (masked) when ``add_bos``. + * ``labels`` equal ``input_ids`` on target positions and ``IGNORE_INDEX`` + elsewhere. + + The sequence is truncated to ``max_seq_len`` tokens. + """ + input_ids: List[int] = [] + labels: List[int] = [] + + if add_bos and tokenizer.bos_id is not None and tokenizer.bos_id >= 0: + input_ids.append(tokenizer.bos_id) + labels.append(IGNORE_INDEX) + + for text, is_target in segments: + ids = tokenizer.encode(text) + input_ids.extend(ids) + labels.extend(ids if is_target else [IGNORE_INDEX] * len(ids)) + if is_target: + # Terminate the assistant turn with a learned EOS token. + input_ids.append(tokenizer.eos_id) + labels.append(tokenizer.eos_id) + + return input_ids[:max_seq_len], labels[:max_seq_len] + + +# --------------------------------------------------------------------------- +# Public API — PromptFormatter +# --------------------------------------------------------------------------- + +class PromptFormatter: + """ + Converts a normalised raw sample into a tokenised ``(input_ids, labels)`` + pair, applying loss-masking so only assistant responses are trained on. + + Parameters + ---------- + cfg: + Master ``SFTConfig`` (for template and data settings). + tokenizer: + Initialised ``Tokenizer`` instance. + """ + + def __init__(self, cfg: SFTConfig, tokenizer: Tokenizer) -> None: + self.cfg = cfg + self.tokenizer = tokenizer + self.tmpl = cfg.template + self.max_len = cfg.data.max_seq_len + + # ------------------------------------------------------------------ + def format(self, sample: dict) -> Optional[Tuple[List[int], List[int]]]: + """ + Format a single normalised sample into ``(input_ids, labels)``. + + Returns ``None`` when the sample produces no trainable (non-masked) + tokens — e.g. the response was truncated away. + """ + if "messages" in sample: + segments = build_conversation_segments( + sample["messages"], tmpl=self.tmpl, include_final_response=True + ) + else: + segments = build_alpaca_segments( + instruction=sample["instruction"], + output=sample["output"], + input_text=sample.get("input", ""), + tmpl=self.tmpl, + include_response=True, + ) + + # Nothing to train on if there are no target segments at all. + if not any(is_target for _, is_target in segments): + logger.debug("No assistant/response segments — skipping sample") + return None + + input_ids, labels = encode_segments( + segments, self.tokenizer, self.max_len, add_bos=True + ) + + if not input_ids or not any(l != IGNORE_INDEX for l in labels): + logger.debug( + "No response tokens remain after truncation at %d — skipping", + self.max_len, + ) + return None + + return input_ids, labels + + # ------------------------------------------------------------------ + def format_for_inference(self, sample: dict) -> List[int]: + """ + Format a sample for *generation*: prompt only, no response body and no + trailing EOS, with ``bos_id`` prepended. + """ + if "messages" in sample: + segments = build_conversation_segments( + sample["messages"], tmpl=self.tmpl, include_final_response=False + ) + else: + segments = build_alpaca_segments( + instruction=sample["instruction"], + output=sample.get("output", ""), + input_text=sample.get("input", ""), + tmpl=self.tmpl, + include_response=False, + ) + input_ids, _ = encode_segments( + segments, self.tokenizer, self.max_len, add_bos=True + ) + return input_ids diff --git a/cyberslm_sft/model/__init__.py b/cyberslm_sft/model/__init__.py new file mode 100644 index 0000000..33a9d54 --- /dev/null +++ b/cyberslm_sft/model/__init__.py @@ -0,0 +1,3 @@ +from .cyberslm import CyberSLM + +__all__ = ["CyberSLM"] diff --git a/cyberslm_sft/model/cyberslm.py b/cyberslm_sft/model/cyberslm.py new file mode 100644 index 0000000..fcde567 --- /dev/null +++ b/cyberslm_sft/model/cyberslm.py @@ -0,0 +1,40 @@ +import sys +from pathlib import Path + +# Add the root directory (which contains 'cyberslm') to sys.path +sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent)) + +from cyberslm.model.model import CyberSLM as Stage1CyberSLM +from cyberslm.model.config import CyberSLMConfig + +class CyberSLM(Stage1CyberSLM): + """ + Adapter class to bridge SFT's ModelConfig to Stage 1's CyberSLMConfig, + allowing us to use the original Stage 1 model architecture verbatim. + """ + def __init__(self, cfg): + # Convert SFT ModelConfig to Stage 1 CyberSLMConfig + stage1_cfg = CyberSLMConfig( + vocab_size=cfg.vocab_size, + hidden_dim=cfg.hidden_size, + num_layers=cfg.num_layers, + num_heads=cfg.num_heads, + head_dim=cfg.head_dim, + ffn_hidden_dim=cfg.ffn_size, + max_seq_len=cfg.max_seq_len, + tie_weights=cfg.weight_tying, + bias=cfg.bias, + dropout=cfg.dropout, + norm_eps=cfg.norm_eps, + ) + super().__init__(stage1_cfg) + + def forward(self, input_ids, attention_mask=None, **kwargs): + """ + Wrapper around Stage 1's forward pass. + Stage 1 returns (logits, all_attn_weights); the SFT trainer expects + just the logits Tensor. The key-padding ``attention_mask`` (1=keep, + 0=pad) is forwarded so right-padded batches do not corrupt real tokens. + """ + logits, _ = super().forward(input_ids, attention_mask=attention_mask) + return logits diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..d97a92b --- /dev/null +++ b/generation_config.json @@ -0,0 +1,10 @@ +{ + "_from_model_config": true, + "bos_token_id": 2, + "eos_token_id": 3, + "output_attentions": false, + "output_hidden_states": false, + "pad_token_id": 0, + "transformers_version": "5.15.1", + "use_cache": true +} diff --git a/infer_chat.py b/infer_chat.py new file mode 100644 index 0000000..a58a518 --- /dev/null +++ b/infer_chat.py @@ -0,0 +1,118 @@ +""" +Instruction-tuned model inference — question answering. + + python Final/infer_chat.py --prompt "What is SQL injection?" + python Final/infer_chat.py --interactive + +The prompt is built with the SAME formatter used during fine-tuning, so the +model sees exactly the token sequence it was trained on. Hand-assembling the +prompt string instead produces different token ids at every segment boundary +(SentencePiece prepends a word-start marker per encode call) and the model then +sees something it was never trained on. + +Expect correctly-shaped answers with unreliable facts: this is a 33.5M-parameter +model. See runs/reports/FINAL_REPORT.md for measured behaviour. +""" + +from __future__ import annotations + +import argparse +import sys +import time +from pathlib import Path + +import torch + +_HERE = Path(__file__).resolve().parent +for _p in (_HERE, _HERE / "cyberslm_sft"): + if str(_p) not in sys.path: + sys.path.insert(0, str(_p)) + +from configs.sft_config import default_config # noqa: E402 +from data.prompt_formatter import PromptFormatter, Tokenizer # noqa: E402 +from model.cyberslm import CyberSLM as SFTModel # noqa: E402 + + +def main() -> int: + ap = argparse.ArgumentParser(description="CyberSLM instruct (question answering)") + ap.add_argument("--prompt", "-p", default="What is SQL injection and how do I prevent it?") + ap.add_argument("--interactive", "-i", action="store_true") + ap.add_argument("--checkpoint", "-c", default=str(_HERE / "models" / "instruct.pt")) + ap.add_argument("--tokenizer", default=str(_HERE / "tokenizer" / "tokenizer.model")) + ap.add_argument("--max-new-tokens", "-m", type=int, default=200) + ap.add_argument("--temperature", "-t", type=float, default=0.0, + help="0 = greedy/deterministic (recommended for this model)") + ap.add_argument("--top-k", type=int, default=50) + ap.add_argument("--top-p", type=float, default=0.9) + ap.add_argument("--repetition-penalty", type=float, default=1.1) + ap.add_argument("--device", default=None) + args = ap.parse_args() + + device = torch.device(args.device) if args.device else torch.device( + "cuda" if torch.cuda.is_available() else "cpu") + + ckpt = Path(args.checkpoint) + if not ckpt.exists(): + print(f"Checkpoint not found: {ckpt}", file=sys.stderr) + return 1 + + cfg = default_config() + cfg.tokenizer.model_path = args.tokenizer + cfg.model.max_seq_len = 2048 + cfg.data.max_seq_len = 2048 + + tok = Tokenizer(cfg.tokenizer.model_path) + fmt = PromptFormatter(cfg=cfg, tokenizer=tok) + + model = SFTModel(cfg.model) + state = torch.load(ckpt, map_location=device, weights_only=False) + if isinstance(state, dict) and "model_state" in state: + state = state["model_state"] + model.load_state_dict(state) + model.to(device).eval() + + n = sum(p.numel() for p in model.parameters()) + print(f"model : {ckpt.name} ({n:,} params)") + print(f"context: {cfg.model.max_seq_len} device: {device} " + f"decoding: {'greedy' if args.temperature == 0 else f'T={args.temperature}'}") + + def answer(question: str) -> None: + ids = fmt.format_for_inference({"messages": [{"role": "user", "content": question}]}) + x = torch.tensor([ids], dtype=torch.long, device=device) + t0 = time.perf_counter() + out = model.generate( + x, max_new_tokens=args.max_new_tokens, temperature=args.temperature, + top_k=args.top_k, top_p=args.top_p, + repetition_penalty=args.repetition_penalty, eos_id=tok.eos_id, + ) + dt = time.perf_counter() - t0 + new = out[0, len(ids):].tolist() + stopped = tok.eos_id in new + if stopped: + new = new[: new.index(tok.eos_id)] + print("\n" + "-" * 66) + print(tok.decode(new).strip() or "(empty)") + print("-" * 66) + print(f"{len(new)} tokens in {dt:.2f}s ({len(new)/dt if dt else 0:.1f} tok/s), " + f"{'stopped on EOS' if stopped else 'hit token limit'}\n") + + if args.interactive: + print("\nInstruct model - ask a question. ('exit' to quit)") + while True: + try: + q = input("\nYou: ").strip() + except (EOFError, KeyboardInterrupt): + print("\nBye."); break + if not q: + continue + if q.lower() in {"exit", "quit", "q"}: + print("Bye."); break + answer(q) + return 0 + + answer(args.prompt) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/model.safetensors b/model.safetensors new file mode 100644 index 0000000..0ceeb99 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a29dca58c1ac9b190015c5fce60425830a2ee0009117969cefcb9d7fab569a06 +size 134137152 diff --git a/models/instruct.pt b/models/instruct.pt new file mode 100644 index 0000000..74429e6 --- /dev/null +++ b/models/instruct.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:baf9e480ae7ce58282c74703b5eeac3d80403ec4cbe102c179382cda2d4926b6 +size 134160315 diff --git a/special_tokens_map.json b/special_tokens_map.json new file mode 100644 index 0000000..00ebcb0 --- /dev/null +++ b/special_tokens_map.json @@ -0,0 +1,6 @@ +{ + "bos_token": "", + "eos_token": "", + "unk_token": "", + "pad_token": "" +} \ No newline at end of file diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000..c97e07f --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,279575 @@ +{ + "version": "1.0", + "truncation": null, + "padding": null, + "added_tokens": [ + { + "id": 0, + "content": "", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 1, + "content": "", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 2, + "content": "", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 3, + "content": "", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + } + ], + "normalizer": { + "type": "Sequence", + "normalizers": [ + { + "type": "NFKC" + }, + { + "type": "Prepend", + "prepend": "▁" + }, + { + "type": "Replace", + "pattern": { + "String": " " + }, + "content": "▁" + } + ] + }, + "pre_tokenizer": null, + "post_processor": { + "type": "TemplateProcessing", + "single": [ + { + "Sequence": { + "id": "A", + "type_id": 0 + } + } + ], + "pair": [ + { + "Sequence": { + "id": "A", + "type_id": 0 + } + }, + { + "Sequence": { + "id": "B", + "type_id": 0 + } + } + ], + "special_tokens": {} + }, + "decoder": { + "type": "Sequence", + "decoders": [ + { + "type": "Replace", + "pattern": { + "String": "▁" + }, + "content": " " + }, + { + "type": "ByteFallback" + }, + { + "type": "Fuse" + }, + { + "type": "Strip", + "content": " ", + "start": 1, + "stop": 0 + } + ] + }, + "model": { + "type": "BPE", + "dropout": null, + "unk_token": "", + "continuing_subword_prefix": null, + "end_of_word_suffix": null, + "fuse_unk": true, + "byte_fallback": true, + "ignore_merges": false, + 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"short": 12345, + "▁twice": 12346, + "▁checksum": 12347, + "▁2015.": 12348, + "mount": 12349, + "▁exec": 12350, + "▁turned": 12351, + "critical": 12352, + "506": 12353, + "▁permitted": 12354, + "▁introduces": 12355, + "<>": 12356, + "▁pu": 12357, + "XD": 12358, + "#%": 12359, + "YB": 12360, + "YL": 12361, + "certain": 12362, + ".**": 12363, + "▁300": 12364, + "▁suggests": 12365, + "payload": 12366, + "▁labels": 12367, + "ffffffff": 12368, + "compatible": 12369, + "cheats": 12370, + "timeout": 12371, + "?*": 12372, + "ani": 12373, + "aria": 12374, + "▁sun": 12375, + "ancer": 12376, + "iph": 12377, + "▁symbols": 12378, + "ecommun": 12379, + "▁broken": 12380, + "f7": 12381, + "▁Commit": 12382, + "▁legacy": 12383, + "+.": 12384, + "comes": 12385, + "▁facilities": 12386, + "'@": 12387, + "/09": 12388, + "187": 12389, + "xygen": 12390, + "▁goes": 12391, + "&[": 12392, + "70,": 12393, + "With": 12394, + "quotes": 12395, + "possible": 12396, + "$!": 12397, + "dn": 12398, + "▁keyboard": 12399, + "▁persistence": 12400, + "▁theoretical": 12401, + "scope": 12402, + "▁bank": 12403, + "▁propag": 12404, + "tom": 12405, + "▁Guid": 12406, + "driven": 12407, + "-2018-8": 12408, + "_>": 12409, + "Current": 12410, + "JW": 12411, + ">[[": 12412, + "▁shut": 12413, + "-2015-3": 12414, + "▁stations": 12415, + "▁uploaded": 12416, + "yg": 12417, + "compl": 12418, + "$-": 12419, + "▁bot": 12420, + "/2012/": 12421, + "▁green": 12422, + "▁macro": 12423, + "▁shellcode": 12424, + "Is": 12425, + "YD": 12426, + "plements": 12427, + "135": 12428, + "▁AB": 12429, + "▁CRLF": 12430, + "▁Opera": 12431, + "▁crypto": 12432, + "▁reboot": 12433, + "166": 12434, + "W3C": 12435, + "hes": 12436, + "ission": 12437, + "5.1": 12438, + "smb": 12439, + "▁Forti": 12440, + "&@": 12441, + "17,": 12442, + "803": 12443, + "▁intrusion": 12444, + "a7": 12445, + "elebr": 12446, + "ourse": 12447, + "▁clusters": 12448, + "▁compiler": 12449, + "r2": 12450, + "703": 12451, + "Store": 12452, + "▁theories": 12453, + "▁Antivirus": 12454, + "ebook": 12455, + "▁QuickTime": 12456, + "FH": 12457, + "▁tv": 12458, + "▁Pop": 12459, + "▁Zone": 12460, + "forced": 12461, + "▁Graphics": 12462, + "REE": 12463, + "▁}\\": 12464, + "▁warn": 12465, + "▁Lotus": 12466, + "▁Following": 12467, + "&^": 12468, + "VH": 12469, + "dm": 12470, + "&]": 12471, + "yal": 12472, + "▁Deep": 12473, + "chestr": 12474, + "liptic": 12475, + "/=": 12476, + "ZD": 12477, + "ZL": 12478, + "125": 12479, + "▁CRM": 12480, + "track": 12481, + "▁price": 12482, + "▁Proced": 12483, + "▁acquisition": 12484, + ":>": 12485, + "794": 12486, + "JL": 12487, + "YI": 12488, + "urt": 12489, + "▁True": 12490, + "▁IA": 12491, + "▁GPU": 12492, + "▁nmap": 12493, + "▁Where": 12494, + "inations": 12495, + "▁measurement": 12496, + "802": 12497, + "itel": 12498, + "▁kil": 12499, + "iminal": 12500, + ":@": 12501, + "194": 12502, + "199": 12503, + "707": 12504, + "▁whereas": 12505, + "JQ": 12506, + "goo": 12507, + "▁Send": 12508, + "283": 12509, + "TED": 12510, + "▁arriv": 12511, + "▁separated": 12512, + "$>": 12513, + ";]": 12514, + "▁à": 12515, + "▁ns": 12516, + "▁Cart": 12517, + "JR": 12518, + "nu": 12519, + "▁consistency": 12520, + "END": 12521, + "5555": 12522, + "▁aware": 12523, + "▁slight": 12524, + "▁Without": 12525, + "▁looks": 12526, + "▁layout": 12527, + "instance": 12528, + "▁android": 12529, + "arn": 12530, + ".10.": 12531, + "▁5.8": 12532, + "▁curl": 12533, + "-2018-": 12534, + "has": 12535, + "browse": 12536, + "▁reaction": 12537, + "▁attempting": 12538, + "183": 12539, + "▁dropped": 12540, + "▁sciences": 12541, + "backup": 12542, + "▁deleg": 12543, + "▁extend": 12544, + "(-": 12545, + "▁setuid": 12546, + "aby": 12547, + "28.": 12548, + "▁78": 12549, + "▁draft": 12550, + "▁resistance": 12551, + "▁installations": 12552, + "▁IS": 12553, + "ultim": 12554, + "▁Master": 12555, + "▁switches": 12556, + "&>": 12557, + "a3": 12558, + "▁citizens": 12559, + "▁AUTH": 12560, + ",%": 12561, + "---|": 12562, + "ouch": 12563, + "▁Value": 12564, + "▁curve": 12565, + "▁Info": 12566, + "gitlab": 12567, + "havior": 12568, + "▁heavy": 12569, + "=000": 12570, + "▁Fil": 12571, + "Query": 12572, + "▁glass": 12573, + "O0": 12574, + "flags": 12575, + "'#": 12576, + "▁Lock": 12577, + "▁mga": 12578, + "'+": 12579, + "EH": 12580, + "Q0": 12581, + "▁Mot": 12582, + "▁Pers": 12583, + "ventions": 12584, + "▁designs": 12585, + "▁technological": 12586, + "136": 12587, + "WordPress": 12588, + "да": 12589, + "▁assumed": 12590, + "iot": 12591, + "▁Flow": 12592, + "ajax": 12593, + "▁Sum": 12594, + "ifest": 12595, + "works": 12596, + "▁Photo": 12597, + "Ag": 12598, + "538": 12599, + "MIN": 12600, + "pool": 12601, + "▁Low": 12602, + "▁sleep": 12603, + "▁involve": 12604, + "▁protections": 12605, + "gz": 12606, + "▁walk": 12607, + "reland": 12608, + "bour": 12609, + "lar": 12610, + "lice": 12611, + "-2023-4": 12612, + "▁hybrid": 12613, + "▁closely": 12614, + "JM": 12615, + "50.": 12616, + "▁},": 12617, + "▁\"..": 12618, + "ellect": 12619, + "handle": 12620, + "▁bridge": 12621, + "▁viewed": 12622, + "▁'.": 12623, + "▁sen": 12624, + "▁focuses": 12625, + "▁blog": 12626, + "▁Spain": 12627, + "-2014-3": 12628, + "▁Agency": 12629, + "▁contributions": 12630, + "dp": 12631, + "▁2D": 12632, + "▁adds": 12633, + "'/": 12634, + "288": 12635, + "▁[0x": 12636, + "▁MIME": 12637, + "▁Usage": 12638, + "gallery": 12639, + "readable": 12640, + "▁recognition": 12641, + "▁considerations": 12642, + "JU": 12643, + "▁stages": 12644, + "▁Extension": 12645, + "ها": 12646, + "-2015-7": 12647, + "<@": 12648, + "Z0": 12649, + "sets": 12650, + "▁Publisher": 12651, + "▁Scenarios": 12652, + "▁ecosystem": 12653, + "JI": 12654, + "v3": 12655, + "173": 12656, + "▁prem": 12657, + "P0": 12658, + "`@": 12659, + "807": 12660, + "match": 12661, + "▁digits": 12662, + "%/": 12663, + "Base": 12664, + "-2010-1": 12665, + "▁employees": 12666, + "▁objective": 12667, + ";)": 12668, + "YH": 12669, + "-287": 12670, + "-8\">": 12671, + "▁130": 12672, + "liest": 12673, + "▁optimal": 12674, + "<)": 12675, + "iam": 12676, + "121": 12677, + "407": 12678, + "hop": 12679, + "▁88": 12680, + "▁Full": 12681, + "▁meeting": 12682, + "805": 12683, + "▁scr": 12684, + "usual": 12685, + "▁ship": 12686, + "▁replication": 12687, + "Bl": 12688, + "▁**[": 12689, + "integr": 12690, + "116": 12691, + "726": 12692, + "audit": 12693, + "▁filenames": 12694, + "$.": 12695, + "XK": 12696, + "522": 12697, + "▁(0": 12698, + "▁resolve": 12699, + "N0": 12700, + "gp": 12701, + "▁gained": 12702, + "▁instrument": 12703, + "&+": 12704, + "▁killing": 12705, + ":4": 12706, + "AML": 12707, + "setup": 12708, + "▁Joseph": 12709, + "▁arrays": 12710, + "When": 12711, + "▁Typically": 12712, + "▁independently": 12713, + "?>": 12714, + "▁(3": 12715, + "▁pin": 12716, + "▁tun": 12717, + "▁commits": 12718, + "▁scanner": 12719, + "JF": 12720, + "forward": 12721, + "▁decided": 12722, + "▁studied": 12723, + "ChangeLog": 12724, + "!*": 12725, + "sen": 12726, + "eras": 12727, + "▁Pac": 12728, + "openssl": 12729, + "▁Volume": 12730, + "▁accompl": 12731, + "Controller": 12732, + "529": 12733, + "рова": 12734, + "▁MAY": 12735, + "▁reflect": 12736, + "▁centuries": 12737, + "▁expressed": 12738, + "||": 12739, + "▁\"\\": 12740, + "leak": 12741, + "▁sizeof": 12742, + "▁completion": 12743, + "▁distinguish": 12744, + "JC": 12745, + "anger": 12746, + "▁transpar": 12747, + "cvs": 12748, + "rowd": 12749, + "▁fixes": 12750, + "▁Attacks": 12751, + "▁Credentials": 12752, + "%=": 12753, + "389": 12754, + "▁irc": 12755, + "TITLE": 12756, + "▁Configure": 12757, + "▁electrical": 12758, + "f2": 12759, + "777": 12760, + "▁batter": 12761, + "▁medicine": 12762, + "<+": 12763, + ".18": 12764, + "505": 12765, + "eces": 12766, + "557": 12767, + "▁2003,": 12768, + "Pentest": 12769, + "▁UTF": 12770, + "▁Commerce": 12771, + "638": 12772, + "irit": 12773, + "tabs": 12774, + "▁philosoph": 12775, + "just": 12776, + "▁gold": 12777, + "posing": 12778, + "publication": 12779, + "tx": 12780, + "-21": 12781, + "Rout": 12782, + "sched": 12783, + "▁depict": 12784, + "▁Old": 12785, + "▁victims": 12786, + "ér": 12787, + "sync": 12788, + "while": 12789, + "controller": 12790, + "^-": 12791, + "b7": 12792, + "lymp": 12793, + "▁Mexico": 12794, + "developers": 12795, + "uate": 12796, + "gather": 12797, + "▁arises": 12798, + "▁chapter": 12799, + "▁Defender": 12800, + "EI": 12801, + "elih": 12802, + "▁(4": 12803, + "▁annot": 12804, + "▁userspace": 12805, + "19.": 12806, + "▁ord": 12807, + "▁Award": 12808, + "▁atomic": 12809, + "but": 12810, + "meas": 12811, + "▁Firmware": 12812, + "▁recipient": 12813, + "▁Nob": 12814, + "▁consult": 12815, + "▁initialization": 12816, + "▁fa": 12817, + "opes": 12818, + "▁tour": 12819, + "▁conform": 12820, + "TQ": 12821, + "722": 12822, + "olt": 12823, + "msf4/": 12824, + "▁Analytics": 12825, + ":!": 12826, + "job": 12827, + "ANIZ": 12828, + "▁partners": 12829, + "iest": 12830, + "Token": 12831, + "▁Korea": 12832, + "Directory": 12833, + ";@": 12834, + "APT": 12835, + "▁Plus": 12836, + "attrib": 12837, + "-2009-2": 12838, + "▁northern": 12839, + "pentesting": 12840, + "▁implements": 12841, + "Download": 12842, + "HackTricks": 12843, + "▁lit": 12844, + "▁Date": 12845, + "Fix": 12846, + "▁dir": 12847, + "▁east": 12848, + "amsung": 12849, + "▁benefit": 12850, + "▁C7": 12851, + "aval": 12852, + "▁IMAP": 12853, + "▁PRIV": 12854, + "▁fraud": 12855, + "hex": 12856, + "▁night": 12857, + "▁attempted": 12858, + "▁describing": 12859, + "706": 12860, + "ginx": 12861, + ":3": 12862, + "▁Ruby": 12863, + "▁happen": 12864, + "▁offering": 12865, + "▁verified": 12866, + "JD": 12867, + "▁kept": 12868, + "▁abort": 12869, + "c3": 12870, + "702": 12871, + "PER": 12872, + "▁DNA": 12873, + "▁cruc": 12874, + "▁Query": 12875, + "#*": 12876, + "176": 12877, + "Sep": 12878, + "mad": 12879, + "▁Academy": 12880, + ",$": 12881, + "ación": 12882, + "▁mist": 12883, + "▁formatt": 12884, + "▁ON": 12885, + "▁catch": 12886, + "▁penet": 12887, + "▁converted": 12888, + ".0.0.0": 12889, + "args": 12890, + "▁Optim": 12891, + "ISO": 12892, + "▁blue": 12893, + "▁removing": 12894, + "PRO": 12895, + "▁178": 12896, + "erring": 12897, + "▁pp": 12898, + "elihood": 12899, + "▁passes": 12900, + "\\+": 12901, + "ji": 12902, + "▁cos": 12903, + "unsigned": 12904, + "▁Results": 12905, + "▁sensors": 12906, + "▁suggest": 12907, + "▁division": 12908, + "`+": 12909, + "284": 12910, + "eeper": 12911, + "video": 12912, + "▁heav": 12913, + "▁empir": 12914, + "624": 12915, + "▁8.4": 12916, + "▁west": 12917, + "▁suffer": 12918, + "developer": 12919, + "eh": 12920, + "▁PCI": 12921, + "▁uint": 12922, + "▁throw": 12923, + "▁Congress": 12924, + "▁Rock": 12925, + "▁explore": 12926, + "▁behaviors": 12927, + "[/": 12928, + "▁*)": 12929, + "ivoli": 12930, + "liament": 12931, + "▁principal": 12932, + "SER": 12933, + "room": 12934, + "PQ": 12935, + "U0": 12936, + "648": 12937, + "688": 12938, + "▁msg": 12939, + "Table": 12940, + "redef": 12941, + "▁magn": 12942, + "branch": 12943, + "-2011-2": 12944, + "000,": 12945, + "▁Arg": 12946, + "Sheet": 12947, + "▁Auth": 12948, + "▁inserted": 12949, + "additional": 12950, + "sq": 12951, + "’.": 12952, + "749": 12953, + "▁PK": 12954, + "▁ISA": 12955, + "Stream": 12956, + "istant": 12957, + "▁Director": 12958, + "'$": 12959, + ";!": 12960, + "▁\"-//": 12961, + "Author": 12962, + "▁reduces": 12963, + "▁producing": 12964, + "\\(": 12965, + "▁OBJEC": 12966, + "▁thousands": 12967, + ";^": 12968, + "HERE": 12969, + "▁fight": 12970, + "Exception": 12971, + "▁V500R001C": 12972, + "▁extracted": 12973, + "▁CREATE": 12974, + "▁vendors": 12975, + "▁BY": 12976, + "▁Op": 12977, + "▁Audit": 12978, + "▁Bal": 12979, + "▁APPLE": 12980, + "▁hosted": 12981, + "▁overhead": 12982, + "dl": 12983, + "▁Ess": 12984, + "\"^": 12985, + "798": 12986, + "▁buildings": 12987, + "▁timestamp": 12988, + "182": 12989, + "▁aid": 12990, + "water": 12991, + "vim": 12992, + "▁RF": 12993, + "MATIC": 12994, + "trend": 12995, + "▁Angel": 12996, + "\":\"": 12997, + "ishop": 12998, + "▁train": 12999, + "_3": 13000, + "909": 13001, + "gnu": 13002, + "▁MDM": 13003, + "▁summary": 13004, + "sid": 13005, + "sigma": 13006, + "▁replay": 13007, + "▁Journal": 13008, + "Buffer": 13009, + "ertion": 13010, + "▁Ireland": 13011, + "▁getting": 13012, + "▁implicit": 13013, + "▁referenced": 13014, + "▁Bir": 13015, + "share": 13016, + "eck": 13017, + "▁Components": 13018, + "▁unable": 13019, + "▁SIMATIC": 13020, + "<:": 13021, + "/49": 13022, + "fri": 13023, + "CCoE": 13024, + "eball": 13025, + "making": 13026, + "▁routine": 13027, + "▁Sciences": 13028, + "0000000000000000": 13029, + "▁Ma": 13030, + "aland": 13031, + "▁Intr": 13032, + "switch": 13033, + "588": 13034, + "▁asset": 13035, + "▁172.16": 13036, + "AMP": 13037, + "REQ": 13038, + "▁Chat": 13039, + "▁stars": 13040, + "▁sender": 13041, + "326": 13042, + "▁Hor": 13043, + "emble": 13044, + "▁#####": 13045, + "▁measured": 13046, + "32)": 13047, + "▁0.0": 13048, + "icago": 13049, + "testing": 13050, + "▁portal": 13051, + "Execution": 13052, + "▁increment": 13053, + "dy": 13054, + "LOC": 13055, + "(0x": 13056, + "oom": 13057, + "2020": 13058, + "protocol": 13059, + "Y0": 13060, + "riz": 13061, + "689": 13062, + "▁};": 13063, + "ulse": 13064, + "▁streams": 13065, + "Zeek": 13066, + "▁11,": 13067, + "▁consumer": 13068, + "dimensional": 13069, + "?102": 13070, + "▁animal": 13071, + "▁celebr": 13072, + "▁interpretation": 13073, + "-2009-1": 13074, + "▁Commission": 13075, + "orp": 13076, + "▁pag": 13077, + "elled": 13078, + "guard": 13079, + "▁descriptions": 13080, + "&$": 13081, + "/2008/": 13082, + "▁Olymp": 13083, + "▁defining": 13084, + "\\!": 13085, + "ás": 13086, + "▁Emperor": 13087, + "▁adversarial": 13088, + "▁desc": 13089, + "feedproxy": 13090, + "PentestTools": 13091, + "601": 13092, + "▁digit": 13093, + "▁Register": 13094, + "▁road": 13095, + "&)": 13096, + "'(": 13097, + "321": 13098, + "▁RV": 13099, + "▁Nort": 13100, + "▁categor": 13101, + ";+": 13102, + ";>": 13103, + "334": 13104, + "649": 13105, + "atomic": 13106, + "▁storing": 13107, + "197": 13108, + "csrf": 13109, + "▁1984": 13110, + "▁specialized": 13111, + "▁transformation": 13112, + "'!": 13113, + "598": 13114, + "estic": 13115, + "mobile": 13116, + "▁Virgin": 13117, + "\"$": 13118, + "▁7.6": 13119, + "▁replacing": 13120, + "▁interested": 13121, + "▁necessarily": 13122, + "<^": 13123, + "_4": 13124, + "▁chat": 13125, + "▁liqu": 13126, + "<$": 13127, + "ala": 13128, + "▁southern": 13129, + "▁а": 13130, + "atz": 13131, + "▁rich": 13132, + "▁Calendar": 13133, + "acu": 13134, + "▁smb": 13135, + "▁solid": 13136, + "▁Ha": 13137, + "▁Alert": 13138, + "▁appar": 13139, + "▁urban": 13140, + "Install": 13141, + "▁Classic": 13142, + "▁127.0.0.1": 13143, + "▁Ke": 13144, + "▁towards": 13145, + "▁Recovery": 13146, + "153": 13147, + "normal": 13148, + "▁ratio": 13149, + "▁organized": 13150, + "▁preferred": 13151, + "d2": 13152, + "403": 13153, + "▁convers": 13154, + "▁affecting": 13155, + ".15": 13156, + "udio": 13157, + "▁expensive": 13158, + "'0": 13159, + "603": 13160, + "▁Ol": 13161, + "▁NAT": 13162, + "▁pure": 13163, + "/2017/": 13164, + "overflow": 13165, + ".2R": 13166, + "▁ORG": 13167, + "Method": 13168, + "▁probe": 13169, + "#/": 13170, + "`$": 13171, + "▁mish": 13172, + "▁2010,": 13173, + "186": 13174, + "727": 13175, + "Integr": 13176, + "driver": 13177, + "▁structured": 13178, + "How": 13179, + "▁Econ": 13180, + "▁gate": 13181, + "element": 13182, + "▁Rational": 13183, + "146": 13184, + "▁Victor": 13185, + "antics": 13186, + "▁Radio": 13187, + "osis": 13188, + "▁analyzed": 13189, + "▁universal": 13190, + "165": 13191, + "▁Blog": 13192, + "▁facility": 13193, + "bi": 13194, + "atial": 13195, + "▁piece": 13196, + "▁prove": 13197, + "56,": 13198, + "pod": 13199, + "▁Sup": 13200, + "▁Large": 13201, + "▁Browser": 13202, + "▁Eastern": 13203, + "▁listing": 13204, + "155": 13205, + "iling": 13206, + "▁ASCII": 13207, + "inical": 13208, + "▁ل": 13209, + "rot": 13210, + "▁V100": 13211, + "▁RP": 13212, + "refer": 13213, + "▁soft": 13214, + "/2007-": 13215, + "uint": 13216, + "pling": 13217, + "▁fair": 13218, + "▁Spider": 13219, + "▁contexts": 13220, + "▁influenced": 13221, + "dated": 13222, + "offset": 13223, + "tls": 13224, + "spect": 13225, + "▁Conf": 13226, + "▁told": 13227, + "▁promin": 13228, + "▁ox": 13229, + "▁prohib": 13230, + "JH": 13231, + "622": 13232, + "▁jump": 13233, + "▁Article": 13234, + "193": 13235, + "cam": 13236, + "▁الت": 13237, + "origin": 13238, + "▁-----------": 13239, + "+*": 13240, + "esh": 13241, + "gpc": 13242, + "oned": 13243, + "▁fif": 13244, + "▁Random": 13245, + "▁dependency": 13246, + "`#": 13247, + "797": 13248, + "ulk": 13249, + "rase": 13250, + "▁Ham": 13251, + "Plugin": 13252, + "▁motor": 13253, + "▁solar": 13254, + "175": 13255, + "ainst": 13256, + "iliency": 13257, + "SECU": 13258, + "▁Single": 13259, + "▁exhaustion": 13260, + "L0": 13261, + "555": 13262, + "/1/43": 13263, + "▁10.2": 13264, + "espread": 13265, + "▁indirect": 13266, + "Management": 13267, + "▁widespread": 13268, + "▁responsibility": 13269, + "▁hacker": 13270, + "▁religion": 13271, + "▁compressed": 13272, + "ffix": 13273, + "aemon": 13274, + "▁acts": 13275, + "▁strongly": 13276, + "▁Determine": 13277, + "▁intermediate": 13278, + "▁[$": 13279, + "-2002": 13280, + "▁Polish": 13281, + "ln": 13282, + "▁HE": 13283, + "▁equation": 13284, + "▁utilities": 13285, + "▁frameworks": 13286, + "\\#": 13287, + "jax": 13288, + "arse": 13289, + "нны": 13290, + "▁Hall": 13291, + "▁Star": 13292, + "ercise": 13293, + "URI": 13294, + "▁finite": 13295, + "▁threshold": 13296, + "?(": 13297, + ".4.2": 13298, + "▁electro": 13299, + "▁Documentation": 13300, + "yer": 13301, + "▁67": 13302, + "▁IETF": 13303, + "thetic": 13304, + "692": 13305, + "J0": 13306, + "222": 13307, + "ACY": 13308, + "SCO": 13309, + "life": 13310, + "onia": 13311, + "▁Los": 13312, + "▁proc": 13313, + "result": 13314, + "XH": 13315, + "=110": 13316, + "osed": 13317, + "-2013-4": 13318, + "X0": 13319, + "DOM": 13320, + "mir": 13321, + "▁female": 13322, + "▁printing": 13323, + "▁relation": 13324, + "▁subsequently": 13325, + "@*": 13326, + "185": 13327, + "549": 13328, + "ois": 13329, + "sap": 13330, + "▁inj": 13331, + "▁stay": 13332, + "▁annual": 13333, + "Resource": 13334, + "▁mishand": 13335, + "▁appliance": 13336, + "322": 13337, + "▁62": 13338, + "▁6F": 13339, + "▁Good": 13340, + ",]": 13341, + "534": 13342, + "▁Zero": 13343, + "▁malloc": 13344, + "▁cer": 13345, + "▁Payload": 13346, + "<0": 13347, + "found": 13348, + "▁Debug": 13349, + "▁stake": 13350, + "сп": 13351, + "007": 13352, + "IDS": 13353, + "/000": 13354, + "atur": 13355, + "-2011-1": 13356, + "▁WooCommerce": 13357, + "amps": 13358, + "▁Rest": 13359, + "▁bodies": 13360, + "!%": 13361, + ")#": 13362, + "mk": 13363, + "537": 13364, + "806": 13365, + "Part": 13366, + "ольз": 13367, + "▁2.2.1": 13368, + "▁appoint": 13369, + "▁alongside": 13370, + "lt": 13371, + "▁assembly": 13372, + "▁C1": 13373, + "▁Men": 13374, + "ereal": 13375, + "ideos": 13376, + "Afee": 13377, + "▁3.2.1": 13378, + "\"&": 13379, + "<*": 13380, + "PDF": 13381, + "AUTO": 13382, + "▁Cup": 13383, + "▁Lev": 13384, + "iders": 13385, + "/74": 13386, + "▁Blue": 13387, + "▁Like": 13388, + "▁fuzz": 13389, + "flash": 13390, + "▁accord": 13391, + "[-": 13392, + "las": 13393, + "▁من": 13394, + "▁competition": 13395, + "Am": 13396, + "%2C": 13397, + "▁dim": 13398, + "▁^": 13399, + "55.": 13400, + "ici": 13401, + "558": 13402, + "════": 13403, + "▁aims": 13404, + "▁cool": 13405, + "▁Impro": 13406, + "▁priorit": 13407, + "▁earliest": 13408, + "[=": 13409, + "156": 13410, + "724": 13411, + "ação": 13412, + "ursor": 13413, + "▁ORGANIZ": 13414, + "ARGET": 13415, + "Threat": 13416, + "fferent": 13417, + "▁Greece": 13418, + "▁wanted": 13419, + "▁Certain": 13420, + ".0/": 13421, + "501": 13422, + "626": 13423, + "TIVE": 13424, + "misc": 13425, + "dx": 13426, + "▁Carol": 13427, + "▁Tivoli": 13428, + "nThis": 13429, + "▁Water": 13430, + "▁parents": 13431, + "▁payment": 13432, + "▁95": 13433, + "▁Civil": 13434, + "▁Destructive": 13435, + "▁publications": 13436, + "287": 13437, + "Http": 13438, + "estr": 13439, + "close": 13440, + "▁acquired": 13441, + "▁association": 13442, + "/08": 13443, + "305": 13444, + "▁lookup": 13445, + "▁strictly": 13446, + "Dr": 13447, + "554": 13448, + "ения": 13449, + "▁Mach": 13450, + "▁Enumer": 13451, + "788": 13452, + "iced": 13453, + "vari": 13454, + "▁1979": 13455, + "etworks": 13456, + "cookie": 13457, + "▁Chicago": 13458, + "<%": 13459, + "629": 13460, + "▁10.3.1": 13461, + "▁replacement": 13462, + "905": 13463, + "▁140": 13464, + "▁invoked": 13465, + "▁cu": 13466, + "Allow": 13467, + "ainers": 13468, + "▁overlap": 13469, + "▁Manufact": 13470, + "▁recording": 13471, + "▁explanation": 13472, + "\\^": 13473, + "asty": 13474, + "extron": 13475, + "▁flood": 13476, + "\\*": 13477, + "random": 13478, + "723": 13479, + "uard": 13480, + "▁gap": 13481, + "\"*": 13482, + "282": 13483, + "705": 13484, + "▁snapshot": 13485, + "▁ownership": 13486, + "hw": 13487, + "/2006/1": 13488, + "ellectual": 13489, + "▁floating": 13490, + "▁independence": 13491, + "▁Queen": 13492, + "▁crypt": 13493, + "▁reviews": 13494, + "\\@": 13495, + ".25": 13496, + "▁Bank": 13497, + "▁symmetric": 13498, + "+/": 13499, + "74,": 13500, + "▁County": 13501, + "▁rapidly": 13502, + "▁asym": 13503, + "erated": 13504, + "visions": 13505, + "▁Internal": 13506, + "▁PS": 13507, + "STEM": 13508, + "▁friends": 13509, + "icip": 13510, + "▁clar": 13511, + "▁Governor": 13512, + "ва": 13513, + "▁Dar": 13514, + "`^": 13515, + "-3,": 13516, + "684": 13517, + "▁RS": 13518, + "▁exc": 13519, + "▁Compiler": 13520, + "`>": 13521, + "605": 13522, + "hidra": 13523, + "▁Armen": 13524, + "▁simultaneously": 13525, + "▁continues": 13526, + "▁bacter": 13527, + "▁3:": 13528, + "▁Pak": 13529, + "(.": 13530, + "/88": 13531, + "▁tip": 13532, + "▁Aust": 13533, + "▁Rece": 13534, + "?%": 13535, + "[.": 13536, + "▁ongoing": 13537, + "▁fingerprint": 13538, + "▁ч": 13539, + "▁1.0.3": 13540, + "ulatory": 13541, + "▁Street": 13542, + "▁unintended": 13543, + "inja": 13544, + "sock": 13545, + "▁alle": 13546, + "▁seems": 13547, + "▁Packet": 13548, + "▁anomal": 13549, + "698": 13550, + "▁2E": 13551, + "▁fewer": 13552, + "Release": 13553, + "▁Feature": 13554, + "▁Zealand": 13555, + "701": 13556, + "rac": 13557, + "CHAN": 13558, + "▁3.9": 13559, + "▁Nobel": 13560, + "▁mathematic": 13561, + "](../": 13562, + "onian": 13563, + "▁Scot": 13564, + "▁grav": 13565, + "▁trunc": 13566, + "▁adoption": 13567, + "#.": 13568, + "squ": 13569, + "allenge": 13570, + "/47": 13571, + "eye": 13572, + "▁SER": 13573, + "ewhere": 13574, + "phones": 13575, + "▁Audio": 13576, + "▁surveillance": 13577, + "/2003/": 13578, + "?0": 13579, + "INE": 13580, + "docId": 13581, + "▁15.1": 13582, + "▁prepar": 13583, + "▁algebra": 13584, + "gmentation": 13585, + "▁Som": 13586, + "▁defeat": 13587, + "▁participants": 13588, + "▁TY": 13589, + "▁AMD": 13590, + "▁13.0": 13591, + "redict": 13592, + "▁reserved": 13593, + "▁Recommendation": 13594, + "527": 13595, + "▁regression": 13596, + "10:": 13597, + "Sel": 13598, + "Action": 13599, + "ulates": 13600, + "pattern": 13601, + "push": 13602, + "▁parad": 13603, + "▁Incorrect": 13604, + "vpn": 13605, + "Oper": 13606, + "none": 13607, + "interface": 13608, + "&:": 13609, + "xi": 13610, + "▁noc": 13611, + "iliar": 13612, + "irty": 13613, + "▁ISC": 13614, + "▁daily": 13615, + "▁intention": 13616, + "▁improvements": 13617, + "Inv": 13618, + "lay": 13619, + "tab": 13620, + "▁[2]": 13621, + "▁Color": 13622, + "▁Customer": 13623, + "VPN": 13624, + "ceived": 13625, + "▁Front": 13626, + "-2018-0": 13627, + "▁bottom": 13628, + "667": 13629, + "CTL": 13630, + "oma": 13631, + "umn": 13632, + "erve": 13633, + "asket": 13634, + "▁1809,": 13635, + "Enabled": 13636, + "endor": 13637, + "▁Bern": 13638, + "▁doub": 13639, + "ipping": 13640, + "ecessary": 13641, + "325": 13642, + "▁universities": 13643, + "!=": 13644, + ";%": 13645, + "\"><": 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"▁builds": 13824, + "dh": 13825, + "195": 13826, + "▁2.0.1": 13827, + "▁McAfee": 13828, + "742": 13829, + "Block": 13830, + "udget": 13831, + "▁crime": 13832, + "▁initialize": 13833, + ",>": 13834, + "448": 13835, + "902": 13836, + "▁bomb": 13837, + "▁removal": 13838, + "▁district": 13839, + "▁Operating": 13840, + "GED": 13841, + "stro": 13842, + "▁[4]": 13843, + "▁pot": 13844, + "▁args": 13845, + "▁Consumer": 13846, + "▁189": 13847, + "24.": 13848, + "▁Initial": 13849, + "▁trailing": 13850, + "▁exceptions": 13851, + "/06": 13852, + "559": 13853, + "Rel": 13854, + "▁Mad": 13855, + ":8": 13856, + "b6": 13857, + "▁SCAP": 13858, + "▁Physical": 13859, + "!0": 13860, + "Status": 13861, + "▁connectivity": 13862, + ",*": 13863, + "562": 13864, + "ambo": 13865, + "auto": 13866, + "iner": 13867, + "▁Mand": 13868, + "642": 13869, + "Vol": 13870, + "▁Dat": 13871, + "-2014-8": 13872, + "accessible": 13873, + ")=": 13874, + "+=": 13875, + "▁Northern": 13876, + "?.": 13877, + "/11": 13878, + "907": 13879, + "IMP": 13880, + "song": 13881, + "stri": 13882, + "Range": 13883, + "▁Korean": 13884, + "▁revision": 13885, + "▁requesting": 13886, + "ela": 13887, + "▁DS": 13888, + "▁9.2": 13889, + "▁mir": 13890, + "▁bash": 13891, + "ailure": 13892, + "January": 13893, + "▁generator": 13894, + "▁opportunities": 13895, + "d7": 13896, + "▁CE": 13897, + "▁fuel": 13898, + "series": 13899, + "▁emerged": 13900, + "▁satellite": 13901, + "Hello": 13902, + "▁(11)": 13903, + "▁1900": 13904, + "▁stating": 13905, + "health": 13906, + "▁controllers": 13907, + "286": 13908, + "68.": 13909, + "fin": 13910, + "oofed": 13911, + "▁unavailable": 13912, + ";*": 13913, + "▁iP": 13914, + "▁Used": 13915, + "▁Saint": 13916, + "monitor": 13917, + "▁Pet": 13918, + "▁rain": 13919, + "▁SHALL": 13920, + "▁ready": 13921, + "▁joined": 13922, + "▁catalog": 13923, + "▁operated": 13924, + "▁assessments": 13925, + "/46": 13926, + "▁benchmark": 13927, + "▁Mitigation": 13928, + "\\%": 13929, + "196": 13930, + "▁NFS": 13931, + "▁Panel": 13932, + "▁escalate": 13933, + ".50": 13934, + "53,": 13935, + "posts": 13936, + "569": 13937, + "ivo": 13938, + "▁Xen": 13939, + "Write": 13940, + "▁advoc": 13941, + "▁Builder": 13942, + "ancements": 13943, + "▁DB2": 13944, + "▁RBG": 13945, + "▁straight": 13946, + "▁×": 13947, + "hub": 13948, + "▁Cold": 13949, + "▁experts": 13950, + "sha": 13951, + "▁god": 13952, + "▁Supply": 13953, + "identity": 13954, + "▁Potential": 13955, + "▁Lite": 13956, + "Number": 13957, + "comment": 13958, + "▁Manual": 13959, + "▁legisl": 13960, + ";:": 13961, + "337": 13962, + "52.": 13963, + "acon": 13964, + "Exploit": 13965, + "64)": 13966, + "7.1": 13967, + "BUG": 13968, + "▁Neg": 13969, + "▁SIG": 13970, + "a6": 13971, + "biz": 13972, + "▁tar": 13973, + "▁NCCoE": 13974, + "▁atmos": 13975, + "▁proport": 13976, + "`-": 13977, + "72,": 13978, + "MAX": 13979, + "you": 13980, + "aler": 13981, + "▁gar": 13982, + "▁skin": 13983, + "▁yield": 13984, + "rat": 13985, + "/2022/0": 13986, + "▁cached": 13987, + "▁foundation": 13988, + "(0)": 13989, + "▁OK": 13990, + "▁SET": 13991, + "/2012/0": 13992, + "$*": 13993, + "▁didn": 13994, + "-2017-3": 13995, + "▁adjacent": 13996, + "▁dependent": 13997, + "539": 13998, + "▁spac": 13999, + "▁Refer": 14000, + "Privilege": 14001, + "▁entering": 14002, + "▁concurrent": 14003, + "Ab": 14004, + "WARE": 14005, + "▁van": 14006, + "▁inters": 14007, + "infinite": 14008, + "▁PRIVACY": 14009, + "▁panic": 14010, + "▁nocase": 14011, + "▁Starting": 14012, + "▁officials": 14013, + "▁gun": 14014, + "▁pipe": 14015, + "▁Learn": 14016, + "▁hashing": 14017, + "▁Location": 14018, + "Em": 14019, + "`*": 14020, + "ador": 14021, + "enth": 14022, + "▁finally": 14023, + "▁salt": 14024, + "▁soci": 14025, + "manage": 14026, + "container": 14027, + "$%": 14028, + "ietf": 14029, + "inel": 14030, + "acted": 14031, + "▁imag": 14032, + "▁paint": 14033, + "external": 14034, + "vulnerable": 14035, + ",#": 14036, + "526": 14037, + "▁150": 14038, + "▁Way": 14039, + "▁nonce": 14040, + "@/": 14041, + "udp": 14042, + "▁sql": 14043, + "Api": 14044, + "▁Mis": 14045, + "▁ioctl": 14046, + "▁physicist": 14047, + "Board": 14048, + "yout": 14049, + "March": 14050, + "▁obfusc": 14051, + "▁sexual": 14052, + "▁crucial": 14053, + "▁Escalation": 14054, + "=\"2": 14055, + "Cheat": 14056, + "secur": 14057, + "▁numeric": 14058, + "▁1.4.2": 14059, + "▁possess": 14060, + "<-": 14061, + "curl": 14062, + "\\0": 14063, + "542": 14064, + "emr": 14065, + "tion": 14066, + "▁los": 14067, + "▁2.6.3": 14068, + "unter": 14069, + "distro": 14070, + "▁conven": 14071, + "▁assumptions": 14072, + "(=": 14073, + "▁2014,": 14074, + "▁maliciously": 14075, + "583": 14076, + "ло": 14077, + "27.": 14078, + "bsd": 14079, + "▁SDM": 14080, + "▁reasoning": 14081, + "▁conventional": 14082, + "&*": 14083, + "525": 14084, + "apis": 14085, + "eties": 14086, + "▁binding": 14087, + "Mal": 14088, + "▁assignment": 14089, + "\"#": 14090, + "593": 14091, + "End": 14092, + "▁phpMy": 14093, + "▁balance": 14094, + "▁Settings": 14095, + "▁biological": 14096, + ".10.1": 14097, + "▁overs": 14098, + "▁trained": 14099, + "386": 14100, + "54.": 14101, + "776": 14102, + "▁1970s": 14103, + "▁voltage": 14104, + "▁delivered": 14105, + "584": 14106, + "leet": 14107, + "▁rough": 14108, + "▁outcomes": 14109, + "▁semantic": 14110, + "▁Container": 14111, + "▁manufacturer": 14112, + "▁ORGANIZATIONS": 14113, + "694": 14114, + "▁Gi": 14115, + "▁SMS": 14116, + "perial": 14117, + "▁disrupt": 14118, + "▁Cryptography": 14119, + "Le": 14120, + "▁Circ": 14121, + "▁notable": 14122, + "mud": 14123, + "▁Integration": 14124, + "Notes": 14125, + "▁cable": 14126, + "▁profession": 14127, + "nn": 14128, + "LDAP": 14129, + "00,000": 14130, + "tegers": 14131, + "-2016-7": 14132, + "▁Jewish": 14133, + "▁copied": 14134, + "▁handlers": 14135, + "576": 14136, + "743": 14137, + "[0]": 14138, + "▁MANAGE": 14139, + "▁chunks": 14140, + "▁nested": 14141, + ",+": 14142, + "775": 14143, + "▁hosp": 14144, + "▁tact": 14145, + "/2018/": 14146, + "▁keyword": 14147, + "b5": 14148, + "SYS": 14149, + "LOAD": 14150, + "XXXX": 14151, + "I0": 14152, + ".3R": 14153, + "/84": 14154, + "388": 14155, + "900": 14156, + "▁12,": 14157, + "▁inner": 14158, + "▁nucle": 14159, + "▁renew": 14160, + "▁Muslim": 14161, + "▁ls": 14162, + "▁400": 14163, + "▁Well": 14164, + "▁editing": 14165, + "erial": 14166, + "▁kinds": 14167, + "▁remember": 14168, + "▁Organizational": 14169, + "345": 14170, + "utr": 14171, + "▁dan": 14172, + "msg00": 14173, + "▁obtaining": 14174, + "&!": 14175, + "-63": 14176, + "643": 14177, + "ughter": 14178, + "▁screens": 14179, + "eeks": 14180, + "hemat": 14181, + "orough": 14182, + "drivers": 14183, + "▁decimal": 14184, + "gcp": 14185, + "appro": 14186, + "▁planned": 14187, + "c6": 14188, + "/05": 14189, + "▁Alb": 14190, + "icate": 14191, + "▁dry": 14192, + "Memory": 14193, + "▁Phone": 14194, + "▁magnetic": 14195, + "▁Fred": 14196, + "▁elected": 14197, + "acer": 14198, + "derr": 14199, + "fast": 14200, + "ashion": 14201, + "-2007-6": 14202, + "extension": 14203, + "547": 14204, + "ру": 14205, + "ibly": 14206, + "▁322": 14207, + "▁VAL": 14208, + "/2015-0": 14209, + "▁designated": 14210, + "▁challenging": 14211, + "ecommunications": 14212, + "&(": 14213, + "131": 14214, + "April": 14215, + "▁alco": 14216, + "▁Building": 14217, + "▁sniffing": 14218, + "▁Electronic": 14219, + ".=": 14220, + "f6": 14221, + "566": 14222, + "592": 14223, + "xxxx": 14224, + "▁lives": 14225, + "_-": 14226, + "▁POP": 14227, + "▁slightly": 14228, + "248": 14229, + "▁loads": 14230, + "sensitive": 14231, + "666": 14232, + "BCO": 14233, + "Max": 14234, + "capt": 14235, + "▁Mil": 14236, + "VIDIA": 14237, + "▁routes": 14238, + "▁colspan": 14239, + "▁impossible": 14240, + "▁revolution": 14241, + "cloudformation": 14242, + "▁Specification": 14243, + ",)": 14244, + "▁***": 14245, + "▁wire": 14246, + "▁oxygen": 14247, + "80.": 14248, + "ARC": 14249, + "TEN": 14250, + "▁Chem": 14251, + "------": 14252, + "▁worth": 14253, + "<=": 14254, + "e7": 14255, + "▁Boston": 14256, + "▁philosopher": 14257, + "?=": 14258, + "646": 14259, + "906": 14260, + "Can": 14261, + "▁FE": 14262, + "▁APT": 14263, + "▁Bob": 14264, + "▁sto": 14265, + "-2007-5": 14266, + "▁submitted": 14267, + "▁2015,": 14268, + "▁explo": 14269, + "emporary": 14270, + "▁Pacific": 14271, + "c5": 14272, + "▁Phase": 14273, + "▁drives": 14274, + "▁allocate": 14275, + "536": 14276, + "697": 14277, + "vider": 14278, + "@=": 14279, + "aeo": 14280, + "Flow": 14281, + "▁Cast": 14282, + "called": 14283, + "SECURITY": 14284, + "certificate": 14285, + "▁Sie": 14286, + "▁Voice": 14287, + "▁assumes": 14288, + "▁edition": 14289, + "▁Defaults": 14290, + "e2": 14291, + ".7.": 14292, + "pkg": 14293, + "▁Rome": 14294, + "▁epis": 14295, + "concept": 14296, + "▁Considerations": 14297, + "imit": 14298, + "ping": 14299, + "▁Labs": 14300, + "▁Sheet": 14301, + "▁patients": 14302, + "Init": 14303, + "riers": 14304, + "▁Times": 14305, + "▁Compute": 14306, + "▁density": 14307, + "▁navigation": 14308, + "▁authorities": 14309, + "Po": 14310, + "ms0": 14311, + "ytics": 14312, + "changed": 14313, + "▁Centre": 14314, + ",`": 14315, + "▁Jan": 14316, + "▁radar": 14317, + "784": 14318, + "STR": 14319, + "AUTOTITLE": 14320, + "'-": 14321, + "161": 14322, + "301": 14323, + "▁Az": 14324, + "▁farm": 14325, + "▁Perform": 14326, + "▁Solutions": 14327, + "Git": 14328, + "▁eax": 14329, + "vmware": 14330, + "Wh": 14331, + "ftpd": 14332, + "▁Age": 14333, + "▁1990s": 14334, + "▁validity": 14335, + "▁hijacking": 14336, + "▁cd": 14337, + "qual": 14338, + "SYSTEM": 14339, + "▁positions": 14340, + "▁WLAN": 14341, + "▁experi": 14342, + "revision": 14343, + "ilipp": 14344, + "▁copyright": 14345, + "▁unencrypted": 14346, + "10-": 14347, + "then": 14348, + "▁Roth": 14349, + "▁distributions": 14350, + "\"!": 14351, + "#=": 14352, + "iro": 14353, + "▁1969": 14354, + "▁corrected": 14355, + "▁ع": 14356, + "▁GCP": 14357, + "▁Area": 14358, + "Proper": 14359, + "▁mapped": 14360, + "▁executive": 14361, + "22.": 14362, + "gun": 14363, + "Device": 14364, + "Enable": 14365, + "▁mysql": 14366, + "▁fourth": 14367, + "▁transf": 14368, + "▁Americans": 14369, + "save": 14370, + "▁anch": 14371, + "▁illegal": 14372, + "uing": 14373, + "▁Ger": 14374, + "]-": 14375, + "▁09": 14376, + "▁cogn": 14377, + "▁suscept": 14378, + "523": 14379, + "ULT": 14380, + "achi": 14381, + "alone": 14382, + "▁miles": 14383, + "543": 14384, + "yan": 14385, + "▁6.9": 14386, + "▁POS": 14387, + "orter": 14388, + "▁reuse": 14389, + "▁periods": 14390, + "▁Scientific": 14391, + "▁Unauthorized": 14392, + "405": 14393, + "RDP": 14394, + "Sigma": 14395, + "▁Maria": 14396, + "▁tried": 14397, + "▁breach": 14398, + "ein": 14399, + "▁artist": 14400, + "defender": 14401, + "▁Similarly": 14402, + "ango": 14403, + "▁Fre": 14404, + "httpd": 14405, + "▁ideal": 14406, + "=\"../../../": 14407, + "594": 14408, + "▁4C": 14409, + "igor": 14410, + "▁Ken": 14411, + "Print": 14412, + "▁Policies": 14413, + "▁initiated": 14414, + "▁deprecated": 14415, + "▁regulations": 14416, + "994": 14417, + "▁pert": 14418, + "▁opposed": 14419, + "996": 14420, + "aus": 14421, + "ulus": 14422, + "▁4.8": 14423, + "▁advisory": 14424, + "▁Protecting": 14425, + "=1&": 14426, + "▁bio": 14427, + "unit": 14428, + "▁PID": 14429, + "stage": 14430, + "▁2013,": 14431, + "▁equations": 14432, + "▁uncertain": 14433, + "&%": 14434, + "▁**2": 14435, + "▁decode": 14436, + "285": 14437, + "▁TYPO": 14438, + "▁election": 14439, + "552": 14440, + "usb": 14441, + "enda": 14442, + "▁syscall": 14443, + "NSS": 14444, + "odo": 14445, + "▁Ash": 14446, + "Build": 14447, + "▁MANAGEMENT": 14448, + "\"%": 14449, + "\"@": 14450, + "fit": 14451, + "jet": 14452, + "▁dual": 14453, + "▁genetic": 14454, + "▁Marketing": 14455, + ")%": 14456, + "`/": 14457, + "888": 14458, + "-284": 14459, + "Alert": 14460, + "▁DRBG": 14461, + "through": 14462, + "▁shares": 14463, + "▁calculated": 14464, + "19,": 14465, + "EMU": 14466, + "Java": 14467, + "▁0x00": 14468, + "-2011-4": 14469, + "▁scholars": 14470, + "▁============": 14471, + "&0": 14472, + "▁whom": 14473, + "▁Argent": 14474, + "gorithms": 14475, + "▁scheduled": 14476, + "▁methodology": 14477, + "sive": 14478, + "▁ACK": 14479, + "▁Bridge": 14480, + "\"0": 14481, + "sbin": 14482, + "▁calculation": 14483, + "▁gal": 14484, + "▁serving": 14485, + "▁Provider": 14486, + "567": 14487, + "▁Ec": 14488, + "▁reus": 14489, + "▁regional": 14490, + "▁installing": 14491, + "ENCE": 14492, + "▁KEY": 14493, + "▁Map": 14494, + "▁unt": 14495, + "▁1980s": 14496, + "▁emails": 14497, + "▁Country": 14498, + "uv": 14499, + "ika": 14500, + "disk": 14501, + "▁updating": 14502, + ",:": 14503, + "40.": 14504, + "Techn": 14505, + "paces": 14506, + "▁Cost": 14507, + "Fusion": 14508, + ";0": 14509, + "56.": 14510, + "rpc": 14511, + "ções": 14512, + "etween": 14513, + "signed": 14514, + "▁perfect": 14515, + "▁resulted": 14516, + ",@": 14517, + "686": 14518, + "998": 14519, + "▁14.": 14520, + "grees": 14521, + "openc": 14522, + "▁1977": 14523, + "systems": 14524, + "documents": 14525, + "▁inventory": 14526, + "ла": 14527, + "foot": 14528, + "▁Hung": 14529, + "▁Impact": 14530, + "▁Complex": 14531, + "▁discard": 14532, + "▁fiction": 14533, + "▁directive": 14534, + "Rec": 14535, + "sfp": 14536, + "tle": 14537, + "▁nom": 14538, + "▁Last": 14539, + "▁Soft": 14540, + "▁coal": 14541, + "ooting": 14542, + "▁Edward": 14543, + "▁resourceVersion": 14544, + "687": 14545, + "745": 14546, + "801": 14547, + "LOB": 14548, + "▁Sn": 14549, + "CERT": 14550, + "▁[1]": 14551, + "▁foo": 14552, + ")@": 14553, + "_=": 14554, + "pd": 14555, + "24,": 14556, + "роб": 14557, + "▁abb": 14558, + "▁Nexus": 14559, + "siemens": 14560, + "▁Laboratory": 14561, + "▁soph": 14562, + "▁JB": 14563, + "▁Mah": 14564, + "adget": 14565, + "rapid": 14566, + "urers": 14567, + "▁duration": 14568, + "----------": 14569, + "▁consisting": 14570, + "▁establishment": 14571, + "360": 14572, + "axy": 14573, + "computer": 14574, + "▁wildcard": 14575, + "carlo": 14576, + "-------+": 14577, + "ova": 14578, + "orders": 14579, + "▁ending": 14580, + "▁Medical": 14581, + "▁prepared": 14582, + "&/": 14583, + "▁Size": 14584, + "▁correspond": 14585, + "1}": 14586, + "wa": 14587, + "647": 14588, + "▁tick": 14589, + "*=": 14590, + "d5": 14591, + "d6": 14592, + "▁ships": 14593, + "▁Francisco": 14594, + "▁biometric": 14595, + "▁scientist": 14596, + "heme": 14597, + "▁Hub": 14598, + "▁Rog": 14599, + "▁2022,": 14600, + "▁OAuth": 14601, + "`%": 14602, + "636": 14603, + "...]": 14604, + "339": 14605, + "545": 14606, + "Cart": 14607, + "++++++++": 14608, + "a5": 14609, + "CAN": 14610, + "Cent": 14611, + "Attributes": 14612, + "▁engineers": 14613, + ";/": 14614, + "587": 14615, + "IEC": 14616, + "itud": 14617, + "▁www": 14618, + "uguese": 14619, + "▁permanent": 14620, + "54,": 14621, + "xyz": 14622, + "▁2B": 14623, + "▁B9": 14624, + "DIUS": 14625, + "What": 14626, + "aped": 14627, + "what": 14628, + "▁mel": 14629, + "▁1986": 14630, + "efficient": 14631, + "▁likelihood": 14632, + "▁termination": 14633, + "995": 14634, + "AZURE": 14635, + "▁semi": 14636, + "likely": 14637, + "▁everyone": 14638, + "▁understood": 14639, + "▁investigate": 14640, + "/07": 14641, + "349": 14642, + "RED": 14643, + "ebel": 14644, + "/1/48": 14645, + "▁trial": 14646, + "imental": 14647, + "▁canonical": 14648, + "▁du": 14649, + ".3.2": 14650, + "▁9.5": 14651, + "APTER": 14652, + "aware": 14653, + "▁Head": 14654, + "▁Expert": 14655, + "▁phones": 14656, + "▁prediction": 14657, + "24)": 14658, + "bucket": 14659, + "▁NVIDIA": 14660, + "▁spoofed": 14661, + "▁protecting": 14662, + "▁educational": 14663, + "apy": 14664, + "-10/": 14665, + "ynasty": 14666, + "registry": 14667, + "▁validating": 14668, + "78,": 14669, + "POST": 14670, + "▁Loc": 14671, + "▁spin": 14672, + "Target": 14673, + "bottom": 14674, + "▁coding": 14675, + "▁Natural": 14676, + "▁destroyed": 14677, + "vb": 14678, + "▁2006,": 14679, + "▁reality": 14680, + "▁(.": 14681, + "yses": 14682, + "▁ended": 14683, + "▁digest": 14684, + "▁helper": 14685, + "▁acknowled": 14686, + "▁hardcoded": 14687, + "53.": 14688, + "Pay": 14689, + "?100": 14690, + "▁Jac": 14691, + "▁1967": 14692, + "^^^^^^^^": 14693, + "▁Moreover": 14694, + "▁disclosed": 14695, + "▁Var": 14696, + "izard": 14697, + "-2016-42": 14698, + "ام": 14699, + "risk": 14700, + ".1800-2": 14701, + "kh": 14702, + "▁Fox": 14703, + "▁lang": 14704, + "▁TYPO3": 14705, + "▁sequential": 14706, + "zz": 14707, + "ocratic": 14708, + ")0": 14709, + "▁CY": 14710, + "cred": 14711, + "▁papers": 14712, + "▁amounts": 14713, + ",!": 14714, + "573": 14715, + "▁--[": 14716, + "-2015-4": 14717, + "▁subscriber": 14718, + "#-": 14719, + "'=": 14720, + "34.": 14721, + "alpha": 14722, + "▁ball": 14723, + "ulative": 14724, + "▁Conver": 14725, + "▁summar": 14726, + "▁primitive": 14727, + "$=": 14728, + "▁ED": 14729, + "ectiva": 14730, + "ificates": 14731, + "olen": 14732, + "▁Ten": 14733, + "fghan": 14734, + "▁defend": 14735, + "▁unusual": 14736, + "▁weapons": 14737, + "▁excessive": 14738, + "=\"\"": 14739, + "▁Jud": 14740, + "▁abstract": 14741, + "olop": 14742, + "reset": 14743, + "▁HMAC": 14744, + "▁TIBCO": 14745, + "▁guitar": 14746, + "trendmicro": 14747, + "▁Reflected": 14748, + "693": 14749, + "ста": 14750, + "%%%%": 14751, + "/102": 14752, + "itivity": 14753, + "▁Credential": 14754, + "&=": 14755, + ".0.0,": 14756, + "▁valu": 14757, + "▁virt": 14758, + "hacker": 14759, + "▁padding": 14760, + "▁segments": 14761, + "▁forwarding": 14762, + "');": 14763, + "MIME": 14764, + "▁Child": 14765, + "▁bundle": 14766, + "▁Insufficient": 14767, + "án": 14768, + "iana": 14769, + "▁Arc": 14770, + "▁Cho": 14771, + "empty": 14772, + "▁Save": 14773, + "aluigi": 14774, + "amente": 14775, + "initial": 14776, + "▁chrome": 14777, + "▁uninitialized": 14778, + "AES": 14779, + "izont": 14780, + "accept": 14781, + "height": 14782, + "▁Secretary": 14783, + "ры": 14784, + "▁icon": 14785, + "▁2017,": 14786, + "▁2023-0": 14787, + "▁owners": 14788, + "347": 14789, + "681": 14790, + "▁Sys": 14791, + "icial": 14792, + "▁5.1.1": 14793, + "tts": 14794, + "Active": 14795, + "atualiz": 14796, + "generic": 14797, + "▁Prevention": 14798, + "▁healthcare": 14799, + "!.": 14800, + "/10/": 14801, + "apse": 14802, + "▁Boot": 14803, + "▁Edwards": 14804, + "▁boolean": 14805, + ";(": 14806, + "446": 14807, + "597": 14808, + "▁Lee": 14809, + "limit": 14810, + "▁trib": 14811, + "▁Effect": 14812, + "▁readers": 14813, + "etherlands": 14814, + "787": 14815, + "ICS": 14816, + "▁ta": 14817, + "ussian": 14818, + "▁surrounding": 14819, + "spolop": 14820, + "History": 14821, + "556": 14822, + "725": 14823, + "lol": 14824, + "▁CF": 14825, + "▁Hit": 14826, + "double": 14827, + "ecraft": 14828, + "attrition": 14829, + "342": 14830, + "ipt": 14831, + "suse": 14832, + "acuum": 14833, + "f5": 14834, + "sb": 14835, + "582": 14836, + "/2002-": 14837, + "▁Fixed": 14838, + "▁funding": 14839, + "ntbugtraq": 14840, + "▁defaults": 14841, + "encryption": 14842, + "carlospolop": 14843, + "▁Cloudformation": 14844, + "ية": 14845, + "348": 14846, + "gear": 14847, + "sfph": 14848, + "ropol": 14849, + "README": 14850, + "▁crack": 14851, + "-2015-0": 14852, + "▁Extensions": 14853, + "795": 14854, + "abet": 14855, + "▁sed": 14856, + "links": 14857, + "utors": 14858, + "785": 14859, + "▁org": 14860, + "angle": 14861, + "porter": 14862, + "▁fresh": 14863, + "pressed": 14864, + "El": 14865, + "33.": 14866, + "cisa": 14867, + "ение": 14868, + "decode": 14869, + "▁Array": 14870, + "▁integers": 14871, + "La": 14872, + "oh": 14873, + "Array": 14874, + "▁Johns": 14875, + "▁visited": 14876, + "▁formation": 14877, + "uncio": 14878, + "▁NASA": 14879, + "Guide": 14880, + "googl": 14881, + "▁Jean": 14882, + "▁radiation": 14883, + "$/": 14884, + "66,": 14885, + "▁Mi": 14886, + "683": 14887, + "▁hint": 14888, + "▁reput": 14889, + "▁firewalls": 14890, + "▁repeatedly": 14891, + "+-": 14892, + "▁5.9": 14893, + "▁Dam": 14894, + "▁bias": 14895, + "▁Austrian": 14896, + "▁Maintenance": 14897, + "ди": 14898, + ".24": 14899, + "▁Py": 14900, + "▁obvious": 14901, + "▁Continuous": 14902, + "335": 14903, + "99,": 14904, + "vin": 14905, + "▁Hon": 14906, + "▁Game": 14907, + "ivated": 14908, + "ersistent": 14909, + "700": 14910, + "▁RAM": 14911, + "ateral": 14912, + "▁Columb": 14913, + "▁metric": 14914, + "▁pseudo": 14915, + "▁Islands": 14916, + "▁clearly": 14917, + "▁volumes": 14918, + "▁Vulnerabilities": 14919, + "333": 14920, + "Side": 14921, + "ativity": 14922, + "Cal": 14923, + "prop": 14924, + "acoes": 14925, + "▁FILE": 14926, + "▁SUSE": 14927, + "▁newer": 14928, + "quis": 14929, + "anuncio": 14930, + "conectiva": 14931, + "atualizacoes": 14932, + ".19": 14933, + "\">>": 15220, + "▁onboard": 15221, + "▁Handling": 15222, + ";.": 15223, + "▁(6": 15224, + "▁1968": 15225, + "irable": 15226, + "▁Committee": 15227, + "ARY": 15228, + "▁Camp": 15229, + "▁battle": 15230, + "▁structural": 15231, + "▁Collaboration": 15232, + "▁←": 15233, + "▁(0x": 15234, + "▁Clo": 15235, + "▁painter": 15236, + "▁inconsistent": 15237, + ".0.5": 15238, + "▁hands": 15239, + "Bulletin": 15240, + "▁neither": 15241, + "▁clicking": 15242, + "▁officially": 15243, + "zh": 15244, + "cos": 15245, + "▁F8": 15246, + "white": 15247, + "▁IMP": 15248, + "Share": 15249, + "ynamics": 15250, + "▁Strateg": 15251, + "documentation": 15252, + ".1.2": 15253, + "inem": 15254, + "▁ESX": 15255, + "▁Rep": 15256, + "speak": 15257, + "/2013/0": 15258, + "Protect": 15259, + "▁degrees": 15260, + "isticated": 15261, + "▁baseball": 15262, + "▁chemistry": 15263, + "▁uma": 15264, + "Index": 15265, + "ensed": 15266, + "business": 15267, + "99.": 15268, + "▁Ox": 15269, + "▁pun": 15270, + "▁criminal": 15271, + "▁mathematician": 15272, + "▁uid": 15273, + "▁wind": 15274, + "▁Sites": 15275, + "▁د": 15276, + "asa": 15277, + "ssa": 15278, + "▁Delet": 15279, + "▁leaders": 15280, + "▁representations": 15281, + "▁AJ": 15282, + "▁ра": 15283, + "shared": 15284, + "ugment": 15285, + "unately": 15286, + "▁corporate": 15287, + "▁Conference": 15288, + "746": 15289, + "▁-1": 15290, + "ATUS": 15291, + "▁2007,": 15292, + "▁summer": 15293, + "reements": 15294, + "▁switching": 15295, + "fall": 15296, + "sources": 15297, + "Sm": 15298, + "33)": 15299, + "▁films": 15300, + "▁truth": 15301, + "▁woman": 15302, + "▁centers": 15303, + "▁confusion": 15304, + "▁immediate": 15305, + "lm": 15306, + "yo": 15307, + "cha": 15308, + "▁installer": 15309, + "▁selecting": 15310, + "▁territory": 15311, + "ipv": 15312, + "aspers": 15313, + "ounder": 15314, + "▁Netherlands": 15315, + ",=": 15316, + "▁Navig": 15317, + "▁propri": 15318, + "woo": 15319, + "argv": 15320, + "▁QEMU": 15321, + "▁L2": 15322, + "▁ms": 15323, + "appings": 15324, + "▁acting": 15325, + "▁parsed": 15326, + ")}": 15327, + "Top": 15328, + "▁infer": 15329, + "▁modular": 15330, + "docDisplay": 15331, + "992": 15332, + "Tag": 15333, + "ante": 15334, + "▁Wel": 15335, + "region": 15336, + "▁Setup": 15337, + "▁unset": 15338, + "▁Afghan": 15339, + "▁Southern": 15340, + "emu": 15341, + "▁huge": 15342, + "▁observe": 15343, + "▁00000000": 15344, + "cart": 15345, + "▁Rom": 15346, + "▁Sir": 15347, + "▁reb": 15348, + "▁Anton": 15349, + "▁saying": 15350, + "Te": 15351, + "773": 15352, + "791": 15353, + "▁Mu": 15354, + "ctiid": 15355, + "select": 15356, + "▁weeks": 15357, + "▁liquid": 15358, + "▁semantics": 15359, + "447": 15360, + "remove": 15361, + "▁lived": 15362, + "osa": 15363, + "▁Bench": 15364, + "allowed": 15365, + "](../../": 15366, + "publications": 15367, + "zn": 15368, + "older": 15369, + "▁seek": 15370, + "provid": 15371, + "▁sistem": 15372, + "css": 15373, + "else": 15374, + "plore": 15375, + "▁(12)": 15376, + "▁2009,": 15377, + "cluding": 15378, + "▁Bab": 15379, + "▁married": 15380, + "▁verifying": 15381, + "227": 15382, + "23.": 15383, + "Proxy": 15384, + "▁latency": 15385, + "launchpad": 15386, + "▁bypassing": 15387, + "▁correlation": 15388, + "▁incorporated": 15389, + "van": 15390, + "▁Sy": 15391, + "road": 15392, + "▁blank": 15393, + "digital": 15394, + "/><": 15395, + "62,": 15396, + "?1017": 15397, + "Report": 15398, + "▁complic": 15399, + "▁relying": 15400, + "▁rendering": 15401, + "▁Portuguese": 15402, + "449": 15403, + "Hash": 15404, + "2019-": 15405, + "▁1939": 15406, + "verify": 15407, + "▁2014.": 15408, + "grading": 15409, + "▁neutral": 15410, + "▁verbose": 15411, + "▁audience": 15412, + "▁Swit": 15413, + "▁male": 15414, + "▁skip": 15415, + "truction": 15416, + "▁examine": 15417, + "▁freedom": 15418, + "▁passive": 15419, + "▁teaching": 15420, + "▁contracts": 15421, + "де": 15422, + "odb": 15423, + "oses": 15424, + "▁soil": 15425, + "▁chains": 15426, + "▁Logging": 15427, + "▁Copyright": 15428, + "▁Intrusion": 15429, + "/63": 15430, + "343": 15431, + "arts": 15432, + "▁severe": 15433, + "▁isolated": 15434, + "▁campaigns": 15435, + "/45": 15436, + "ima": 15437, + "▁+0": 15438, + "▁77": 15439, + "▁15,": 15440, + "▁Iss": 15441, + "eller": 15442, + "▁Protect": 15443, + ".29": 15444, + "who": 15445, + "▁/>": 15446, + "▁87": 15447, + "make": 15448, + "▁164": 15449, + "▁Andrew": 15450, + "▁career": 15451, + "▁Instant": 15452, + "\\=": 15453, + "▁Retail": 15454, + "CommandLine": 15455, + "Rep": 15456, + "▁Li": 15457, + "▁Pakistan": 15458, + "87/": 15459, + "▁dates": 15460, + "▁decide": 15461, + "-256": 15462, + "/1.1": 15463, + "Match": 15464, + "▁grand": 15465, + "counter": 15466, + "managed": 15467, + "▁exposes": 15468, + "▁implications": 15469, + "34,": 15470, + "▁Ltd": 15471, + "▁Arist": 15472, + "▁Manage": 15473, + ".21": 15474, + "59.": 15475, + "596": 15476, + "673": 15477, + "eax": 15478, + "idx": 15479, + "▁Currently": 15480, + "▁dictionary": 15481, + "Col": 15482, + "rey": 15483, + "▁Angeles": 15484, + "▁magazine": 15485, + "Map": 15486, + "▁Hom": 15487, + "?1016": 15488, + "▁opin": 15489, + "–18": 15490, + "▁Tal": 15491, + "▁tries": 15492, + "▁Tomcat": 15493, + "▁altering": 15494, + "▁blocking": 15495, + "\"-": 15496, + "▁tid": 15497, + "▁Swiss": 15498, + "▁Title": 15499, + "▁spider": 15500, + "googlech": 15501, + "▁industries": 15502, + "▁'@": 15503, + "▁Ill": 15504, + "▁RET": 15505, + "▁10.6": 15506, + "ietnam": 15507, + "▁outage": 15508, + "▁perhaps": 15509, + "246": 15510, + "▁Wall": 15511, + "▁ultim": 15512, + "/2006/2": 15513, + "▁advance": 15514, + "▁broader": 15515, + "▁theorem": 15516, + "▁emerging": 15517, + "googlechrom": 15518, + "▁fashion": 15519, + "65.": 15520, + "diss": 15521, + "▁absor": 15522, + "▁sectors": 15523, + "▁Revision": 15524, + "▁Virginia": 15525, + "▁insights": 15526, + "▁contribute": 15527, + "/).": 15528, + "▁ic": 15529, + "ATCH": 15530, + "antis": 15531, + "▁Austin": 15532, + "▁cooper": 15533, + "\\-": 15534, + "agios": 15535, + "▁pour": 15536, + "▁backups": 15537, + "▁severity": 15538, + "▁Character": 15539, + "005": 15540, + "edge": 15541, + "uple": 15542, + "▁Stan": 15543, + "▁shif": 15544, + "ictures": 15545, + "▁consumers": 15546, + "▁ف": 15547, + "721": 15548, + "▁S3": 15549, + "mask": 15550, + "▁17.": 15551, + "▁Nap": 15552, + "▁Death": 15553, + "▁finished": 15554, + "▁ciphertext": 15555, + ",/": 15556, + "edo": 15557, + "▁VMS": 15558, + "exploitation": 15559, + "Ops": 15560, + "bol": 15561, + "prefix": 15562, + "▁1960s": 15563, + "▁3.1.1": 15564, + "▁merged": 15565, + "&.": 15566, + "``,": 15567, + "▁0B": 15568, + "aken": 15569, + "▁atoms": 15570, + "▁universe": 15571, + "▁1;": 15572, + "swg1": 15573, + "separ": 15574, + "▁Remove": 15575, + "▁narrow": 15576, + "<.": 15577, + "12:": 15578, + "DATA": 15579, + "teen": 15580, + "uris": 15581, + "▁2018,": 15582, + "Computer": 15583, + "ido": 15584, + "died": 15585, + "▁mil": 15586, + "робле": 15587, + "▁Models": 15588, + "▁calculate": 15589, + "▁hypothesis": 15590, + "Sw": 15591, + "▁disass": 15592, + "uits": 15593, + "▁MAX": 15594, + "▁REC": 15595, + "quoted": 15596, + "▁efficiently": 15597, + "e3": 15598, + "▁C9": 15599, + "intro": 15600, + "▁meng": 15601, + "▁Huawei": 15602, + "▁Observ": 15603, + "▁evolved": 15604, + "▁waiting": 15605, + "▁Checking": 15606, + "▁Depending": 15607, + "▁confidence": 15608, + "242": 15609, + "RSA": 15610, + "ifs": 15611, + "-22:": 15612, + "______": 15613, + "▁acceler": 15614, + "Var": 15615, + "/1/5": 15616, + "izon": 15617, + "▁doi": 15618, + "▁при": 15619, + "/1038": 15620, + "▁Today": 15621, + "area": 15622, + "▁Jews": 15623, + "▁club": 15624, + "▁comed": 15625, + "▁untuk": 15626, + "February": 15627, + "▁override": 15628, + "▁ul": 15629, + "AUTH": 15630, + "-=-=-=-=": 15631, + "▁Ut": 15632, + "rame": 15633, + "▁Cat": 15634, + "▁unified": 15635, + "uspicious": 15636, + "▁Behavior": 15637, + "373": 15638, + "Oct": 15639, + "▁inh": 15640, + "▁hundreds": 15641, + "FFFF": 15642, + "esson": 15643, + "▁notification": 15644, + "LLM": 15645, + "pract": 15646, + "icular": 15647, + "▁Nations": 15648, + "▁1948": 15649, + "▁filtered": 15650, + "▁minimize": 15651, + "▁Developer": 15652, + "@-": 15653, + "ми": 15654, + "▁FIN": 15655, + "▁MOD": 15656, + "▁lapt": 15657, + "▁revers": 15658, + "servation": 15659, + "▁empirical": 15660, + "▁[!": 15661, + "▁Expr": 15662, + "itespace": 15663, + "▁Methods": 15664, + "▁interacts": 15665, + "▁tor": 15666, + "▁Compat": 15667, + "▁prototype": 15668, + "▁Definition": 15669, + "cx": 15670, + "éc": 15671, + "676": 15672, + "696": 15673, + "Frame": 15674, + "▁crop": 15675, + "derstand": 15676, + "relevant": 15677, + "▁Permissions": 15678, + "ria": 15679, + "ibli": 15680, + "▁▁▁▁▁▁▁": 15681, + "▁heavily": 15682, + "▁prominent": 15683, + "▁variations": 15684, + "{{": 15685, + "01]": 15686, + "▁PASS": 15687, + "▁ffff": 15688, + "icht": 15689, + "▁ARP": 15690, + "Enter": 15691, + "▁flexible": 15692, + "▁electricity": 15693, + "665": 15694, + "cfm": 15695, + "▁(/": 15696, + "anted": 15697, + "▁cold": 15698, + "▁covering": 15699, + "uni": 15700, + "aspersky": 15701, + "▁Division": 15702, + "▁enforced": 15703, + "▁physician": 15704, + "000000000000000": 15705, + "/89": 15706, + "pan": 15707, + "▁chief": 15708, + "▁Incident": 15709, + "anç": 15710, + "7ffff": 15711, + "▁1992": 15712, + "▁lies": 15713, + "factor": 15714, + "▁Samba": 15715, + "-2015-1": 15716, + "localhost": 15717, + "▁compounds": 15718, + "▁intellectual": 15719, + "695": 15720, + "osing": 15721, + "▁Duke": 15722, + "▁Will": 15723, + "▁hour": 15724, + "▁Records": 15725, + "▁backslash": 15726, + "▁transferred": 15727, + "Es": 15728, + "......": 15729, + "▁argue": 15730, + "▁synthetic": 15731, + "▁PowerPoint": 15732, + "▁penetration": 15733, + "235": 15734, + "``.": 15735, + "csrc": 15736, + "▁Ven": 15737, + "▁2.0.": 15738, + "▁Sets": 15739, + "▁atom": 15740, + "inline": 15741, + "▁newsp": 15742, + "▁usual": 15743, + "▁Philipp": 15744, + "▁printer": 15745, + "▁appearance": 15746, + "▁pa": 15747, + "/2000": 15748, + "Field": 15749, + "▁Forms": 15750, + "▁offline": 15751, + "▁concerned": 15752, + "▁exclusively": 15753, + "▁Rh": 15754, + "liant": 15755, + "▁fluid": 15756, + "controls": 15757, + "▁Commands": 15758, + "67.": 15759, + "72.": 15760, + "anti": 15761, + "onfig": 15762, + "▁Self": 15763, + "▁Rules": 15764, + "▁utiliz": 15765, + "September": 15766, + "▁Malicious": 15767, + "▁prevention": 15768, + "239": 15769, + "▁Bi": 15770, + "▁rein": 15771, + "▁1.5.0": 15772, + "▁folders": 15773, + "▁reviewed": 15774, + "44.": 15775, + "iat": 15776, + "keep": 15777, + "some": 15778, + "▁Fer": 15779, + "▁ion": 15780, + "▁temperatures": 15781, + "521": 15782, + "plom": 15783, + "▁2013.": 15784, + "▁vision": 15785, + "▁decades": 15786, + "++)": 15787, + "▁HD": 15788, + "talos": 15789, + "▁Compar": 15790, + "▁mental": 15791, + "contexts": 15792, + "25)": 15793, + "iso": 15794, + "atters": 15795, + "itations": 15796, + "▁ALL": 15797, + "▁1974": 15798, + "▁love": 15799, + "▁CONTROL": 15800, + "traversal": 15801, + "▁managers": 15802, + "▁achieving": 15803, + "igm": 15804, + "▁cs": 15805, + "onaut": 15806, + "/2019/": 15807, + "▁Trade": 15808, + "▁rarely": 15809, + "▁analyzer": 15810, + "▁accum": 15811, + "/2018/0": 15812, + "ander": 15813, + "mente": 15814, + "inking": 15815, + "ourses": 15816, + "▁roots": 15817, + "▁innovation": 15818, + "▁compromising": 15819, + "LETE": 15820, + "▁OPA": 15821, + "▁3.0.1": 15822, + "▁OpenBSD": 15823, + "▁picture": 15824, + "▁Workstation": 15825, + "▁CU": 15826, + "poll": 15827, + "▁grew": 15828, + "Module": 15829, + "forums": 15830, + "▁outer": 15831, + "▁retrieved": 15832, + "使用": 15833, + "ZAP": 15834, + "onn": 15835, + "▁pseud": 15836, + "▁awarded": 15837, + "▁CONTROLS": 15838, + "Assignment": 15839, + "375": 15840, + "rds": 15841, + "▁Moon": 15842, + "▁visibility": 15843, + ",.": 15844, + "635": 15845, + "▁L1": 15846, + "▁Jim": 15847, + "▁Ren": 15848, + "books": 15849, + "since": 15850, + "▁redund": 15851, + "993": 15852, + "▁Tok": 15853, + "=\"2\">": 15854, + "magic": 15855, + "▁poly": 15856, + "letter": 15857, + "/2019/0": 15858, + "▁Nets": 15859, + "crypto": 15860, + "▁cameras": 15861, + "▁indicators": 15862, + "17)": 15863, + "anz": 15864, + "yar": 15865, + "pcap": 15866, + "▁PIN": 15867, + "/100/": 15868, + "▁Down": 15869, + "▁feet": 15870, + "measures": 15871, + "oriented": 15872, + "effective": 15873, + "▁gir": 15874, + "ttons": 15875, + "extract": 15876, + "csp": 15877, + "ugar": 15878, + "▁cancer": 15879, + "▁frequencies": 15880, + ";-": 15881, + "▁SVG": 15882, + "▁posts": 15883, + "-2020-2": 15884, + "Function": 15885, + "▁extends": 15886, + "gid": 15887, + "-862": 15888, + "▁Len": 15889, + "▁Norm": 15890, + "▁SP4,": 15891, + "▁phpBB": 15892, + "virtual": 15893, + "▁Insecure": 15894, + "▁publishing": 15895, + "`=": 15896, + "26)": 15897, + "▁Disable": 15898, + "2014": 15899, + "▁systematic": 15900, + "293": 15901, + "901": 15902, + "dbg": 15903, + "▁M2": 15904, + "vard": 15905, + "▁193": 15906, + ".10.10": 15907, + "▁Given": 15908, + "▁TABLE": 15909, + "Android": 15910, + "chester": 15911, + "▁alignment": 15912, + "▁variation": 15913, + "Vulnerability": 15914, + "▁sufficiently": 15915, + "171": 15916, + "egin": 15917, + "ushed": 15918, + "/2010/": 15919, + "ercury": 15920, + "▁3.0.0": 15921, + "October": 15922, + "▁notation": 15923, + "▁impersonate": 15924, + "DOS": 15925, + "-----": 15926, + "atever": 15927, + "▁scene": 15928, + "firmware": 15929, + "▁compact": 15930, + "▁emergency": 15931, + "mess": 15932, + "▁Diff": 15933, + "▁Spect": 15934, + "▁Vault": 15935, + "decrypt": 15936, + "▁Delete": 15937, + "firewall": 15938, + "azz": 15939, + "▁9.4": 15940, + "▁SOC": 15941, + "▁compl": 15942, + "-2017-10": 15943, + "50)": 15944, + "isp": 15945, + "▁Pi": 15946, + "▁fb": 15947, + "▁fi": 15948, + "/155": 15949, + "▁Tag": 15950, + "▁Activity": 15951, + ",-": 15952, + "▁Generic": 15953, + "shop": 15954, + "▁1996": 15955, + "▁Scripts": 15956, + "▁playing": 15957, + "▁familiar": 15958, + "▁Executive": 15959, + "e6": 15960, + "-11/": 15961, + "2-00": 15962, + "▁Aff": 15963, + "probe": 15964, + "-2019-0": 15965, + "▁suffix": 15966, + "compiled": 15967, + "▁Quantum": 15968, + "▁attachments": 15969, + "30)": 15970, + "63.": 15971, + "Panel": 15972, + "Advanced": 15973, + "▁Samsung": 15974, + "▁occasion": 15975, + "sgi": 15976, + "▁|=": 15977, + ".5.1": 15978, + "▁Mir": 15979, + "resol": 15980, + "uming": 15981, + "▁4.0,": 15982, + "▁spam": 15983, + "screen": 15984, + "▁Launch": 15985, + "▁chance": 15986, + "▁applets": 15987, + "▁copying": 15988, + "configure": 15989, + "▁particles": 15990, + "▁introducing": 15991, + "▁Virtualization": 15992, + "typo": 15993, + "▁guard": 15994, + "▁driven": 15995, + "▁invoking": 15996, + "'))": 15997, + "70.": 15998, + "asan": 15999, + "recv": 16000, + "human": 16001, + "/2005/2": 16002, + "ingency": 16003, + "▁precise": 16004, + "/100/100/": 16005, + "▁contributor": 16006, + "trl": 16007, + "aque": 16008, + "when": 16009, + "▁Null": 16010, + "Upload": 16011, + "ronets": 16012, + "247": 16013, + "29)": 16014, + "enty": 16015, + "Names": 16016, + "clusive": 16017, + "▁Subject": 16018, + "▁concentr": 16019, + "lie": 16020, + "Item": 16021, + "abul": 16022, + "powered": 16023, + "▁carrier": 16024, + "▁listening": 16025, + "397": 16026, + "Ret": 16027, + "June": 16028, + "▁0.10": 16029, + "▁marks": 16030, + "Configuration": 16031, + "645": 16032, + "▁Rule": 16033, + "istrict": 16034, + "▁organisms": 16035, + "▁18th": 16036, + "▁2.1.1": 16037, + "Cluster": 16038, + "ersburg": 16039, + "▁clinical": 16040, + "▁populations": 16041, + "nA": 16042, + "38.": 16043, + "▁Wars": 16044, + "▁peak": 16045, + "▁spirit": 16046, + "aissance": 16047, + "▁downstream": 16048, + "▁ultimately": 16049, + "80/": 16050, + "Mac": 16051, + "helpx": 16052, + "Storage": 16053, + "▁Redirect": 16054, + "▁Corporate": 16055, + "ady": 16056, + "▁PT": 16057, + "arus": 16058, + "thur": 16059, + "▁blind": 16060, + "▁10.3.183": 16061, + "▁Extended": 16062, + "▁Property": 16063, + "▁lifecycle": 16064, + "/57": 16065, + "62.": 16066, + "azi": 16067, + "psirt": 16068, + "▁addr": 16069, + "▁bucket": 16070, + "▁telnet": 16071, + "▁defenses": 16072, + "▁downloads": 16073, + "▁Connectivity": 16074, + "wB": 16075, + "URCE": 16076, + "123456": 16077, + "termin": 16078, + "▁labeled": 16079, + "==========": 16080, + "▁cm": 16081, + "▁Jer": 16082, + "person": 16083, + "▁10.2.0": 16084, + "▁Theory": 16085, + "▁outper": 16086, + "▁JDK": 16087, + "▁answ": 16088, + "▁discrete": 16089, + "▁maintains": 16090, + "d1": 16091, + "243": 16092, + "▁mur": 16093, + "▁Major": 16094, + "▁Vietnam": 16095, + "LES": 16096, + "krb": 16097, + "▁со": 16098, + "▁Total": 16099, + "▁battery": 16100, + "-----------+": 16101, + "▁experiences": 16102, + "546": 16103, + "-295": 16104, + "/180": 16105, + "▁meg": 16106, + ".0.0.3": 16107, + "▁lengths": 16108, + "ithersburg": 16109, + "90.": 16110, + "ned": 16111, + "▁19.": 16112, + "▁Doc": 16113, + "▁bre": 16114, + "Option": 16115, + "▁assemb": 16116, + "▁registr": 16117, + "▁department": 16118, + "t10": 16119, + "▁14,": 16120, + "▁PAN": 16121, + "▁paid": 16122, + "invalid": 16123, + "▁assertion": 16124, + "▁Alternatively": 16125, + "ben": 16126, + "▁`$": 16127, + "Rule": 16128, + 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"▁comparing": 16340, + "▁hierarchy": 16341, + "organization": 16342, + "Bit": 16343, + "forcing": 16344, + "▁traces": 16345, + "▁Philoso": 16346, + "Di": 16347, + "18)": 16348, + "Some": 16349, + "FAULT": 16350, + "▁flaws": 16351, + "▁mounted": 16352, + "-2-": 16353, + "37.": 16354, + "SVS": 16355, + "▁Against": 16356, + "▁followers": 16357, + "▁workstation": 16358, + "{%": 16359, + "ISC": 16360, + "ulg": 16361, + "▁nine": 16362, + "antine": 16363, + "▁batch": 16364, + "compass": 16365, + "▁‘--": 16366, + "LINES": 16367, + "▁Foot": 16368, + "▁Rail": 16369, + "ilience": 16370, + "scription": 16371, + "▁conflicts": 16372, + "lif": 16373, + "▁Cro": 16374, + "▁Poll": 16375, + "▁finds": 16376, + "▁lowest": 16377, + "▁basketball": 16378, + "▁sophisticated": 16379, + "/90": 16380, + "~~~": 16381, + "Comp": 16382, + "▁13,": 16383, + "▁Dig": 16384, + "▁ple": 16385, + "▁Navy": 16386, + "▁greatly": 16387, + "▁terminate": 16388, + "ARD": 16389, + "tec": 16390, + "▁Ur": 16391, + "Work": 16392, + "temp": 16393, + "▁nfs": 16394, + "▁não": 16395, + "▁sell": 16396, + "itance": 16397, + "lation": 16398, + "▁configuring": 16399, + "225": 16400, + "▁=====": 16401, + "▁RUGGEDCOM": 16402, + "rx": 16403, + "|\\": 16404, + "olds": 16405, + "Trust": 16406, + "▁bond": 16407, + "general": 16408, + "▁sports": 16409, + "Проблема": 16410, + "▁omitted": 16411, + "▁molecules": 16412, + "Sys": 16413, + "Call": 16414, + "ровани": 16415, + "erospace": 16416, + "▁Scanner": 16417, + "▁observation": 16418, + "во": 16419, + "77.": 16420, + "aman": 16421, + "▁Shop": 16422, + "▁signs": 16423, + "▁acrobat": 16424, + "226": 16425, + "▁M8": 16426, + "Guard": 16427, + "▁2005,": 16428, + "integer": 16429, + "▁hosting": 16430, + "▁discontin": 16431, + "▁integrate": 16432, + "0(": 16433, + "▁E0": 16434, + "▁ABC": 16435, + "gitbook": 16436, + "zerland": 16437, + "opol": 16438, + "▁esp": 16439, + "▁vast": 16440, + "office": 16441, + "-2015-6": 16442, + "ользова": 16443, + "▁syslog": 16444, + "▁supposed": 16445, + "c1": 16446, + "e5": 16447, + "229": 16448, + "kub": 16449, + "psi": 16450, + "▁resp": 16451, + "565": 16452, + "HASH": 16453, + "▁Why": 16454, + "▁shadow": 16455, + "▁Utilities": 16456, + "imo": 16457, + "▁4D": 16458, + "▁[3]": 16459, + "estor": 16460, + "▁POSIX": 16461, + "ventory": 16462, + "▁Pointer": 16463, + "4.1": 16464, + "cho": 16465, + "▁XE": 16466, + "uced": 16467, + "▁ptr": 16468, + "Phrack": 16469, + "loading": 16470, + "▁brother": 16471, + "▁islands": 16472, + "▁optical": 16473, + "▁removes": 16474, + "▁inspired": 16475, + "▁Authentic": 16476, + "را": 16477, + "iday": 16478, + "▁caller": 16479, + "November": 16480, + "▁diagram": 16481, + "▁Spoofing": 16482, + "v0": 16483, + "ée": 16484, + "▁A1": 16485, + "▁Les": 16486, + "youtube": 16487, + "▁dimensions": 16488, + "▁differential": 16489, + "vals": 16490, + "▁Die": 16491, + "uckoo": 16492, + "arters": 16493, + "▁renamed": 16494, + "▁resultant": 16495, + "▁Distribution": 16496, + "tok": 16497, + "▁adaptive": 16498, + "▁Registration": 16499, + "▁(!": 16500, + "/2014-0": 16501, + "▁Recent": 16502, + "▁manufacturers": 16503, + "▁Jos": 16504, + "▁pid": 16505, + "-2020-0": 16506, + "▁Length": 16507, + "▁officer": 16508, + "387": 16509, + "595": 16510, + "Pres": 16511, + "▁BCE": 16512, + "▁ath": 16513, + "▁sid": 16514, + "221": 16515, + "ATED": 16516, + "/2009/": 16517, + "▁locked": 16518, + "▁figures": 16519, + "Integrity": 16520, + "▁analyses": 16521, + "ett": 16522, + "tun": 16523, + "▁12.": 16524, + "▁LLMs": 16525, + "ileges": 16526, + "▁accel": 16527, + "▁theft": 16528, + "Co": 16529, + "LOBAL": 16530, + "▁waves": 16531, + "▁respective": 16532, + "22,": 16533, + "Chat": 16534, + "ugal": 16535, + "Files": 16536, + "▁Virus": 16537, + "▁depart": 16538, + "331": 16539, + "984": 16540, + "▁Wr": 16541, + "ruit": 16542, + "▁pal": 16543, + "-2024-3": 16544, + "▁Aristot": 16545, + "▁Austria": 16546, + "▁supplement": 16547, + "har": 16548, + "vey": 16549, + "inct": 16550, + "prior": 16551, + "Cookie": 16552, + "▁Entity": 16553, + "▁constructs": 16554, + "▁inspection": 16555, + "connaissance": 16556, + "12-": 16557, + "ACL": 16558, + "July": 16559, + "Rock": 16560, + "▁EXEC": 16561, + "▁Edit": 16562, + "▁Carolina": 16563, + "_8": 16564, + "374": 16565, + "=30": 16566, + "ented": 16567, + "nrule": 16568, + "▁Mini": 16569, + "▁vill": 16570, + "window": 16571, + "▁considering": 16572, + "▁io": 16573, + "cludes": 16574, + "ignore": 16575, + "lookup": 16576, + "------+": 16577, + "x64/": 16578, + "alton": 16579, + "▁senior": 16580, + "396": 16581, + "aska": 16582, + "▁Side": 16583, + "▁skill": 16584, + "▁submitting": 16585, + "ticket": 16586, + "▁extreme": 16587, + "---------+": 16588, + "▁retrieval": 16589, + "phos": 16590, + "▁DOI": 16591, + "▁Does": 16592, + "▁Leon": 16593, + "▁phases": 16594, + "▁connects": 16595, + "▁problematic": 16596, + "▁к": 16597, + "7487/": 16598, + "▁Rank": 16599, + "▁opportunity": 16600, + "mor": 16601, + "▁BEA": 16602, + "▁Execute": 16603, + "+1": 16604, + "ın": 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"▁sought": 16816, + "79.": 16817, + "▁union": 16818, + "---------------": 16819, + "Jun": 16820, + "jar": 16821, + "▁C6": 16822, + "▁Siebel": 16823, + "▁agricultural": 16824, + "▁Manufacturing": 16825, + "571": 16826, + "–12": 16827, + "▁SoC": 16828, + "▁governments": 16829, + "\\\\'": 16830, + "dro": 16831, + "ikes": 16832, + "▁regex": 16833, + "▁extens": 16834, + "▁Supreme": 16835, + "▁massive": 16836, + "▁enumeration": 16837, + "237": 16838, + "▁diplom": 16839, + "▁exercise": 16840, + "▁characterized": 16841, + "Tool": 16842, + "gest": 16843, + "imize": 16844, + "urity": 16845, + "Output": 16846, + "▁conducting": 16847, + "4.0": 16848, + "997": 16849, + "▁VI": 16850, + "-2020-3": 16851, + "▁tutorial": 16852, + "▁advantages": 16853, + "Bas": 16854, + "rick": 16855, + "▁worst": 16856, + "▁deaths": 16857, + "▁Template": 16858, + "631": 16859, + "flo": 16860, + "lad": 16861, + "/152": 16862, + "▁Van": 16863, + "Media": 16864, + "erved": 16865, + "20:": 16866, + "947": 16867, + "rant": 16868, + "slack": 16869, + "Attack": 16870, + "August": 16871, + "▁honor": 16872, + "▁Moodle": 16873, + "▁mountain": 16874, + "=\\\"": 16875, + "▁82": 16876, + "▁8A": 16877, + "▁M6": 16878, + "▁15.0": 16879, + "▁Sand": 16880, + "▁enjoy": 16881, + "▁forged": 16882, + "▁decoder": 16883, + "▁daughter": 16884, + "27)": 16885, + "▁76": 16886, + "▁3rd": 16887, + "▁JPEG": 16888, + "▁extern": 16889, + "processor": 16890, + "▁destruction": 16891, + ".26": 16892, + "CAP": 16893, + "▁30,": 16894, + "▁apt": 16895, + "cribe": 16896, + "oster": 16897, + "▁BIND": 16898, + "Portal": 16899, + "worthy": 16900, + "▁exported": 16901, + "▁sampling": 16902, + "▁dashboard": 16903, + "▁Identification": 16904, + "oz": 16905, + ".2-": 16906, + "quake": 16907, + "▁Actor": 16908, + "logging": 16909, + "▁UPDATE": 16910, + "▁V200R008C": 16911, + "▁assumption": 16912, + "4/0": 16913, + "OSE": 16914, + "irq": 16915, + "▁AA": 16916, + "▁EN": 16917, + "icsa": 16918, + "acial": 16919, + "uggling": 16920, + "technical": 16921, + "▁recursive": 16922, + "***": 16923, + "441": 16924, + "▁Nuc": 16925, + "ierre": 16926, + "shadow": 16927, + "▁arise": 16928, + "▁globally": 16929, + "▁leadership": 16930, + "vas": 16931, + "}}\\": 16932, + "blue": 16933, + "▁mun": 16934, + "▁java": 16935, + "imedia": 16936, + "-2015-2": 16937, + "▁mailbox": 16938, + "▁Switzerland": 16939, + "▁essentially": 16940, + "SDN": 16941, + "gate": 16942, + "Notice": 16943, + ".800-53": 16944, + "▁Valley": 16945, + "▁Those": 16946, + "▁Quality": 16947, + "▁Mathemat": 16948, + "▁manifest": 16949, + "▁exclusive": 16950, + "plementation": 16951, + "L2": 16952, + "{\"": 16953, + "▁api": 16954, + "serial": 16955, + "▁neuro": 16956, + "▁wheel": 16957, + "..............": 16958, + "▁GP": 16959, + "/174": 16960, + "▁concaten": 16961, + "aham": 16962, + "ivial": 16963, + "▁6.0,": 16964, + "▁9.0.1": 16965, + "▁outcome": 16966, + "▁Category": 16967, + "▁workflows": 16968, + "▁20,": 16969, + "codec": 16970, + "itter": 16971, + "▁Role": 16972, + "DOMAIN": 16973, + "▁detects": 16974, + "▁spectrum": 16975, + "-----------": 16976, + "281": 16977, + "unar": 16978, + "▁firm": 16979, + "▁taught": 16980, + "fortunately": 16981, + "▁deserialization": 16982, + "▁2-": 16983, + "▁Mr": 16984, + "▁Cle": 16985, + "▁Town": 16986, + "▁abbrev": 16987, + "▁Distributed": 16988, + "s3": 16989, + "78)": 16990, + "]>>": 17027, + "▁pretty": 17028, + "ów": 17029, + "541": 17030, + "▁M7": 17031, + "▁DES": 17032, + "▁JWT": 17033, + "Backup": 17034, + "▁2020,": 17035, + "▁JBoss": 17036, + "hacking": 17037, + "olation": 17038, + "▁phenomena": 17039, + "▁underflow": 17040, + "@1": 17041, + "ви": 17042, + "yze": 17043, + "▁polar": 17044, + "▁spatial": 17045, + "▁watchOS": 17046, + "large": 17047, + "▁Future": 17048, + "▁nations": 17049, + "én": 17050, + "argo": 17051, + "Autom": 17052, + "▁LIST": 17053, + "orphic": 17054, + "▁Telnet": 17055, + "ijacking": 17056, + "▁separately": 17057, + "اد": 17058, + "376": 17059, + "5.0": 17060, + "561": 17061, + "Min": 17062, + "TIAL": 17063, + "akan": 17064, + "hang": 17065, + "▁buy": 17066, + "fetch": 17067, + "▁leaf": 17068, + "▁alias": 17069, + "▁acquire": 17070, + "▁combine": 17071, + "IBM": 17072, + "fish": 17073, + "erequis": 17074, + "advanced": 17075, + "`::": 17076, + "[0-9": 17077, + "hips": 17078, + "▁osc": 17079, + "final": 17080, + "▁Shock": 17081, + "/2007/1": 17082, + "-2017-00": 17083, + "**\\": 17084, + "Coll": 17085, + "rows": 17086, + "ansas": 17087, + "▁1920": 17088, + "limited": 17089, + "▁quotes": 17090, + "SLR": 17091, + ".2.4": 17092, + "WORK": 17093, + "▁Abb": 17094, + "▁12.2.1": 17095, + "▁Position": 17096, + "▁electrons": 17097, + "273": 17098, + "1111": 17099, + "▁CAP": 17100, + "▁ped": 17101, + "begin": 17102, + "liers": 17103, + "▁Bang": 17104, + "ighter": 17105, + "▁coach": 17106, + "▁videos": 17107, + "▁adapted": 17108, + "Parameter": 17109, + "▁centralized": 17110, + "};": 17111, + "').": 17112, + "395": 17113, + "986": 17114, + "mult": 17115, + "orth": 17116, + "snmp": 17117, + "▁Sky": 17118, + "▁knows": 17119, + "▁Contrib": 17120, + "tro": 17121, + "▁M4": 17122, + "▁cgi": 17123, + "▁1937": 17124, + "ublesh": 17125, + "▁anthrop": 17126, + "▁reachable": 17127, + "392": 17128, + "▁Who": 17129, + "/2021/0": 17130, + "entinel": 17131, + "signature": 17132, + "34)": 17133, + "Entry": 17134, + "▁Golden": 17135, + "▁α": 17136, + "Any": 17137, + "▁4-": 17138, + "▁ay": 17139, + "▁ib": 17140, + "/240": 17141, + "east": 17142, + "▁ber": 17143, + "▁": 25554, + "314": 25555, + "Isy": 25556, + "plt": 25557, + "▁7D": 25558, + "/332": 25559, + "/348": 25560, + "▁Dir": 25561, + "▁Ens": 25562, + "-01-0": 25563, + "Smart": 25564, + "conduct": 25565, + "▁broker": 25566, + "▁envelop": 25567, + "▁inserts": 25568, + "▁warrant": 25569, + "-2016-419": 25570, + "▁Heritage": 25571, + "entralized": 25572, + "▁certainly": 25573, + "▁inclusive": 25574, + "▁playwright": 25575, + "840": 25576, + "▁HR": 25577, + "▁Tu": 25578, + ".6.2": 25579, + "99.1": 25580, + "Disk": 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+ "?4": 25632, + "]6": 25633, + "++;": 25634, + "asn": 25635, + "/266": 25636, + "Isyu": 25637, + "utin": 25638, + "▁OMB": 25639, + "▁gef": 25640, + "being": 25641, + "▁teen": 25642, + "Broker": 25643, + "aptist": 25644, + "ilient": 25645, + "▁Fleet": 25646, + "▁Whats": 25647, + "angling": 25648, + "writeup": 25649, + "▁Dublin": 25650, + "▁curric": 25651, + "▁Finnish": 25652, + "▁bindings": 25653, + "▁immutable": 25654, + "iodiversity": 25655, + "▁Allocation": 25656, + "$9": 25657, + "3r": 25658, + "-84": 25659, + "INF": 25660, + "dal": 25661, + "ean": 25662, + "n0x": 25663, + "prof": 25664, + "▁nem": 25665, + "BEGIN": 25666, + "regex": 25667, + "▁5.5.": 25668, + "▁RBAC": 25669, + "▁Spam": 25670, + "▁Tony": 25671, + "▁genu": 25672, + "Concat": 25673, + "▁OpenP": 25674, + "▁cheap": 25675, + "▁opera": 25676, + "▁overw": 25677, + "Mozilla": 25678, + "industr": 25679, + "▁Dialog": 25680, + "▁trojan": 25681, + "-2006-54": 25682, + ".1800-15.": 25683, + "▁Romanian": 25684, + "▁Tennessee": 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"roles": 25738, + "▁crick": 25739, + "osexual": 25740, + "▁Hebrew": 25741, + "▁SOURCE": 25742, + "▁traced": 25743, + "▁viable": 25744, + "iversary": 25745, + "▁Besides": 25746, + "▁Formats": 25747, + "▁Lincoln": 25748, + "▁falling": 25749, + "▁putting": 25750, + "▁ranking": 25751, + "▁sistema": 25752, + "▁surveys": 25753, + "ansparent": 25754, + "▁analytical": 25755, + "*6": 25756, + "<1": 25757, + ".7-": 25758, + "PIV": 25759, + "/400": 25760, + "TRAN": 25761, + "▁224": 25762, + "▁Eug": 25763, + "▁jed": 25764, + "abuse": 25765, + "etics": 25766, + "▁Omni": 25767, + "▁surr": 25768, + "▁Reset": 25769, + "▁coinc": 25770, + "▁causal": 25771, + "detected": 25772, + "rottling": 25773, + "▁Sandbox": 25774, + "implement": 25775, + "▁11.1.111": 25776, + "!9": 25777, + "&8": 25778, + "240": 25779, + "ние": 25780, + "▁Ty": 25781, + "/243": 25782, + "Cred": 25783, + "irth": 25784, + "▁Rot": 25785, + "▁3.1,": 25786, + "▁Beat": 25787, + "syntax": 25788, + "▁dream": 25789, + "▁Cuckoo": 25790, + "▁Higher": 25791, + "-2006-55": 25792, + "▁Arizona": 25793, + "▁Correct": 25794, + "▁PROCESS": 25795, + "▁arrival": 25796, + "▁kubeadm": 25797, + "▁modeled": 25798, + "▁crafting": 25799, + "▁literacy": 25800, + "▁satisfies": 25801, + "▁Persistent": 25802, + "▁assignments": 25803, + "▁correctness": 25804, + "++++++++++++++++": 25805, + "@9": 25806, + "–3": 25807, + "=87": 25808, + "DSS": 25809, + "REC": 25810, + "▁6u": 25811, + "▁B1": 25812, + "obody": 25813, + "▁1910": 25814, + "CATION": 25815, + "Ensure": 25816, + "▁Risks": 25817, + "criptions": 25818, + "▁consoles": 25819, + "constrained": 25820, + "controllers": 25821, + "▁passengers": 25822, + "▁functioning": 25823, + "48)": 25824, + "532": 25825, + "CTX": 25826, + "HEL": 25827, + "▁];": 25828, + "(1);": 25829, + "-3.1": 25830, + "ALSE": 25831, + "esla": 25832, + "Asset": 25833, + "▁1809": 25834, + "▁GPIO": 25835, + "▁Hein": 25836, + "ective": 25837, + "istgov": 25838, + "Archive": 25839, + "▁incent": 25840, + "▁waters": 25841, + 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+ "▁15:": 26679, + "▁JWS": 26680, + "anche": 26681, + "icken": 26682, + "▁Cass": 26683, + "▁1998,": 26684, + "▁Anglo": 26685, + "/2014/0": 26686, + "▁conclude": 26687, + "▁пользова": 26688, + "collection": 26689, + "phpmyadmin": 26690, + "▁exchanged": 26691, + "▁telemetry": 26692, + "▁constitute": 26693, + "▁conversations": 26694, + "m3": 26695, + "pb": 26696, + "668": 26697, + "H1,": 26698, + "/090": 26699, + "/208": 26700, + "▁211": 26701, + "▁Fun": 26702, + "▁MSI": 26703, + "▁PRs": 26704, + "-89**": 26705, + "Final": 26706, + "stdio": 26707, + "▁Dual": 26708, + "▁INTO": 26709, + "▁Vlad": 26710, + "issipp": 26711, + "/2003-0": 26712, + "systemd": 26713, + "▁12.2.3": 26714, + "▁Ground": 26715, + "▁canary": 26716, + "Transfer": 26717, + "▁Phoenix": 26718, + "▁conject": 26719, + "▁stochastic": 26720, + "▁manufacture": 26721, + "'9": 26722, + "\\6": 26723, + "-74": 26724, + "▁cc": 26725, + "edes": 26726, + "esse": 26727, + "▁204": 26728, + ".3R3-": 26729, + "/1036": 26730, + "wheel": 26731, + "▁SP1;": 26732, + "▁kick": 26733, + "▁ldap": 26734, + "ockopt": 26735, + "▁Snort": 26736, + "▁earned": 26737, + "Exchange": 26738, + "graduate": 26739, + "▁heating": 26740, + "Encrypted": 26741, + "▁matrices": 26742, + "securelist": 26743, + "worthiness": 26744, + "▁Political": 26745, + "jq": 26746, + "atl": 26747, + "PRIV": 26748, + "▁Snow": 26749, + "ailand": 26750, + "gomery": 26751, + "trustix": 26752, + "▁ingred": 26753, + "▁Neumann": 26754, + "▁toolbar": 26755, + "automation": 26756, + "▁redundancy": 26757, + "▁enhancements": 26758, + "`6": 26759, + "\"],": 26760, + "nie": 26761, + "▁dl": 26762, + "▁|-": 26763, + "/304": 26764, + "SSID": 26765, + "▁101": 26766, + "▁Mix": 26767, + "▁bic": 26768, + "/4.0/": 26769, + "iture": 26770, + "sleep": 26771, + "▁4.4.": 26772, + "French": 26773, + "▁0.9.1": 26774, + "▁Atlas": 26775, + "Routers": 26776, + "htaccess": 26777, + "▁Preview": 26778, + "algorithm": 26779, + "allocated": 26780, + "▁Ipswitch": 26781, + "▁replicas": 26782, + "▁circumvent": 26783, + "▁truncation": 26784, + "&9": 26785, + "\\4": 26786, + "))\\": 26787, + "u64": 26788, + "|--": 26789, + "ния": 26790, + "▁IL": 26791, + "▁VP": 26792, + "inea": 26793, + "▁11th": 26794, + "▁2.14": 26795, + "SOURCE": 26796, + "otypes": 26797, + "shaped": 26798, + "▁catal": 26799, + "balance": 26800, + "inction": 26801, + "▁12.1.1": 26802, + "▁tagged": 26803, + "▁thesis": 26804, + "▁joining": 26805, + "▁tooling": 26806, + "RESILIENT": 26807, + "planation": 26808, + "▁automodule": 26809, + "▁inequality": 26810, + "xl": 26811, + ".==": 26812, + "670": 26813, + "84.": 26814, + "Wik": 26815, + "/123": 26816, + "/353": 26817, + "Gold": 26818, + "TEXT": 26819, + "eder": 26820, + "unci": 26821, + "▁Pin": 26822, + "▁dup": 26823, + "avers": 26824, + "▁Vinc": 26825, + "▁ages": 26826, + "▁atau": 26827, + "mailto": 26828, + "ovecot": 26829, + "Details": 26830, + "Develop": 26831, + "started": 26832, + "2015-236": 26833, + "Problema": 26834, + "▁farming": 26835, + "▁Recently": 26836, + "▁mosConfig": 26837, + "▁elementary": 26838, + "applications": 26839, + "BulletinBoard": 26840, + ";2": 26841, + ".03": 26842, + "84,": 26843, + "cbc": 26844, + "▁GD": 26845, + "(04)": 26846, + "-22.": 26847, + "Anti": 26848, + "Blue": 26849, + "Once": 26850, + "ayan": 26851, + "▁(14": 26852, + "▁CHA": 26853, + "▁WAF": 26854, + "▁Earl": 26855, + "тифика": 26856, + "▁0x000": 26857, + "▁GENER": 26858, + "▁Logon": 26859, + "▁mines": 26860, + "failure": 26861, + "msg0003": 26862, + "▁InnoDB": 26863, + "▁couldn": 26864, + "licenses": 26865, + "urricane": 26866, + "▁protests": 26867, + "▁divisions": 26868, + "!6": 26869, + "%4": 26870, + "hm": 26871, + "tg": 26872, + "wb": 26873, + "23:": 26874, + "370": 26875, + "/227": 26876, + "RSIG": 26877, + "▁lex": 26878, + "antry": 26879, + "undoc": 26880, + "▁1935": 26881, + "▁Whit": 26882, + "▁spaw": 26883, + "Member": 26884, + "▁enrol": 26885, + "▁WebDAV": 26886, + "callback": 26887, + "▁affairs": 26888, + "▁expands": 26889, + "▁walking": 26890, + "▁entitled": 26891, + "▁removable": 26892, + "▁Conversion": 26893, + "▁initiating": 26894, + "▁conclusions": 26895, + "▁descendants": 26896, + "▁Instructions": 26897, + "▁Functionality": 26898, + "610": 26899, + "ELL": 26900, + "NFS": 26901, + "TES": 26902, + "▁{#": 26903, + "/192": 26904, + "▁222": 26905, + "▁GPT": 26906, + "Organ": 26907, + "ervice": 26908, + "▁endors": 26909, + "▁---------": 26910, + "▁NetWeaver": 26911, + "▁converter": 26912, + "▁Implementing": 26913, + "&6": 26914, + "−1": 26915, + "\":<": 27760, + "CHA": 27761, + "rss": 27762, + "вет": 27763, + "este": 27764, + "orts": 27765, + "▁IDE": 27766, + "▁Ori": 27767, + "▁dst": 27768, + "▁pwd": 27769, + "Japan": 27770, + "oline": 27771, + "poral": 27772, + "▁Chap": 27773, + "▁Nova": 27774, + "▁baby": 27775, + "illion": 27776, + "vicing": 27777, + "▁00000": 27778, + "▁defin": 27779, + "However": 27780, + "consult": 27781, + "▁Failed": 27782, + "▁Regard": 27783, + "sponsors": 27784, + "▁2020-06": 27785, + "▁Hunting": 27786, + "▁Prussia": 27787, + "▁Profiles": 27788, + "government": 27789, + "▁commitment": 27790, + "▁terminates": 27791, + "▁visualization": 27792, + "\"3": 27793, + "uw": 27794, + "-35": 27795, + "352": 27796, + ".7.2": 27797, + "/133": 27798, + "▁214": 27799, + "▁SPF": 27800, + "eping": 27801, + "▁Mort": 27802, + "▁noun": 27803, + "boards": 27804, + "▁inert": 27805, + "▁Hotfix": 27806, + "▁1800-25": 27807, + "▁Galileo": 27808, + "▁retired": 27809, + "/2013-01/": 27810, + "agreement": 27811, + "▁reliance": 27812, + "▁Cascading": 27813, + "▁V200R006C10,": 27814, + "▁сканирования": 27815, + ")6": 27816, + "654": 27817, + "▁(@": 27818, + "▁|`": 27819, + "▁Pwn": 27820, + "▁hat": 27821, + "erion": 27822, + "ydney": 27823, + "▁Bowl": 27824, + "▁Hous": 27825, + "▁Room": 27826, + "▁TIME": 27827, + "▁mutex": 27828, + "▁Assign": 27829, + "▁marker": 27830, + "-2005-04": 27831, + "▁shallow": 27832, + "evolution": 27833, + "▁Operators": 27834, + "▁explosion": 27835, + "▁WorkCentre": 27836, + "▁contiguous": 27837, + "▁Experiments": 27838, + "09:": 27839, + "avi": 27840, + ")**.": 27841, + "=\"1\"": 27842, + "ilan": 27843, + "▁ENT": 27844, + "▁QFX": 27845, + "▁ecx": 27846, + "ftech": 27847, + "olesc": 27848, + "▁---[": 27849, + "▁pins": 27850, + "/2001-": 27851, + "▁Datas": 27852, + "▁USING": 27853, + "▁sharp": 27854, + "▁veter": 27855, + "▁Anwend": 27856, + "▁Morris": 27857, + "disabled": 27858, + "▁Beijing": 27859, + "▁analytic": 27860, + "connecting": 27861, + "▁overrides": 27862, + "registration": 27863, + "▁V200R008C20,": 27864, + "0e": 27865, + "jj": 27866, + "лу": 27867, + "MUD": 27868, + "Raw": 27869, + "lfi": 27870, + "▁<>": 27871, + "▁LC": 27872, + "▁Rx": 27873, + "/267": 27874, + "COMP": 27875, + "Fish": 27876, + "GUID": 27877, + "▁Lux": 27878, + "▁MTU": 27879, + "-79**": 27880, + "atars": 27881, + "vanti": 27882, + "▁Know": 27883, + "clamav": 27884, + "visual": 27885, + "▁spoke": 27886, + "▁worry": 27887, + "screens": 27888, + "▁induct": 27889, + "▁ransom": 27890, + "▁Reserved": 27891, + "▁Workshop": 27892, + "▁Controlled": 27893, + "▁facilitating": 27894, + "▁discrimination": 27895, + "=90": 27896, + "ALS": 27897, + "Dep": 27898, + "Tur": 27899, + "hom": 27900, + "pag": 27901, + "▁gp": 27902, + "%20-": 27903, + "▁tum": 27904, + "Logon": 27905, + "▁memo": 27906, + "▁prey": 27907, + "efront": 27908, + "iliate": 27909, + "▁Years": 27910, + "/2010/0": 27911, + "▁wouldn": 27912, + "telegram": 27913, + "▁distant": 27914, + "▁habitat": 27915, + "▁sensing": 27916, + "▁autonomy": 27917, + "▁beautiful": 27918, + "▁DEVELOPING": 27919, + "▁correlated": 27920, + "▁provisioned": 27921, + "▁TelePresence": 27922, + "\"4": 27923, + "mr": 27924, + "Len": 27925, + "isi": 27926, + "▁PH": 27927, + "▁[3": 27928, + "-16.": 27929, + "bara": 27930, + "suit": 27931, + "▁-1,": 27932, + "▁0xc": 27933, + "▁21.": 27934, + "▁QCA": 27935, + "▁وال": 27936, + "ensis": 27937, + "plant": 27938, + "ubble": 27939, + "▁1.01": 27940, + "▁Rend": 27941, + "▁akan": 27942, + "▁lith": 27943, + "sha256": 27944, + "▁4.0.2": 27945, + "▁BEGIN": 27946, + "▁Evalu": 27947, + "▁Graham": 27948, + "▁guilty": 27949, + "▁poetry": 27950, + "-2002-04": 27951, + "perature": 27952, + "▁implant": 27953, + "▁brothers": 27954, + "googleapis": 27955, + "association": 27956, + "▁subsystems": 27957, + "▁Controllers": 27958, + "▁Collaborative": 27959, + "!5": 27960, + "\\2": 27961, + "`2": 27962, + "この": 27963, + ".__": 27964, + "08:": 27965, + "=0(": 27966, + "Num": 27967, + "▁tc": 27968, + "/14/": 27969, + "Risk": 27970, + "TA13": 27971, + "itrc": 27972, + "▁20:": 27973, + "▁Kom": 27974, + "ancel": 27975, + "pgsql": 27976, + "▁x64/": 27977, + "[0-9]{": 27978, + "▁Medal": 27979, + "Package": 27980, + "redteam": 27981, + "gulftech": 27982, + "parallel": 27983, + "▁Raleigh": 27984, + "Detection": 27985, + "▁Aberdeen": 27986, + "▁Shanghai": 27987, + "▁stripped": 27988, + "▁ifversion": 27989, + "▁advertised": 27990, + "▁destinations": 27991, + "▁subdirectory": 27992, + ")7": 27993, + ";4": 27994, + "pq": 27995, + "ین": 27996, + ".57": 27997, + "4).": 27998, + "gur": 27999, + "Pool": 28000, + "ps1'": 28001, + "repl": 28002, + "▁174": 28003, + "▁DER": 28004, + "openh": 28005, + "▁3600": 28006, + "base64": 28007, + "iframe": 28008, + "▁trivy": 28009, + "central": 28010, + "hardware": 28011, + "▁hospitals": 28012, + "▁emphasized": 28013, + "362": 28014, + "=33": 28015, + "aku": 28016, + "/358": 28017, + "snyk": 28018, + "▁MIN": 28019, + "▁PBX": 28020, + "aning": 28021, + "equal": 28022, + "Survey": 28023, + "▁Syria": 28024, + "-2003-04": 28025, + "Critical": 28026, + "▁Detroit": 28027, + "▁farmers": 28028, + "Wordpress": 28029, + "▁Jonathan": 28030, + "▁Simphony": 28031, + "▁mutually": 28032, + "▁outlines": 28033, + "resolution": 28034, + "administrator": 28035, + "▁-------------": 28036, + "▁predominantly": 28037, + "!1": 28038, + "いる": 28039, + ".4)": 28040, + "=94": 28041, + "dit": 28042, + "▁Bh": 28043, + "/140": 28044, + "VITE": 28045, + "▁RBR": 28046, + "▁SRV": 28047, + "▁jdk": 28048, + "▁Stra": 28049, + "▁Vert": 28050, + "▁folk": 28051, + "▁FETCH": 28052, + "Central": 28053, + "metrics": 28054, + "▁wishes": 28055, + "▁crystal": 28056, + "▁gadgets": 28057, + "▁cylinder": 28058, + "▁Cleartext": 28059, + "▁Commander": 28060, + "▁exercises": 28061, + "NIC": 28062, + "RAR": 28063, + "gpg": 28064, + "▁се": 28065, + "Each": 28066, + "▁108": 28067, + "▁CDR": 28068, + "▁Ont": 28069, + "▁PAC": 28070, + "ности": 28071, + "ользу": 28072, + "▁Misc": 28073, + "▁hist": 28074, + "▁kids": 28075, + "▁syst": 28076, + "▁Touch": 28077, + "▁races": 28078, + "/2002-1": 28079, + "▁Banner": 28080, + "▁iframe": 28081, + "bounties": 28082, + "▁chamber": 28083, + "▁nervous": 28084, + "▁silicon": 28085, + "▁molecule": 28086, + "▁unstable": 28087, + "enetration": 28088, + "▁Isolation": 28089, + "▁V600R006C": 28090, + "▁musicians": 28091, + "▁destructive": 28092, + "▁localization": 28093, + "▁investigating": 28094, + ".4/": 28095, + "ENC": 28096, + "Fin": 28097, + "▁Xn": 28098, + ".6.0": 28099, + "deed": 28100, + "dess": 28101, + "▁18:": 28102, + "Begin": 28103, + "prene": 28104, + "▁earn": 28105, + "mapper": 28106, + "▁Fixes": 28107, + "▁Scale": 28108, + "▁Seven": 28109, + "▁dirty": 28110, + "▁stdin": 28111, + "complex": 28112, + "▁15.006": 28113, + "Generate": 28114, + "Wireless": 28115, + "andidate": 28116, + "▁Scholar": 28117, + "▁violate": 28118, + "/2013-02/": 28119, + "▁Diversity": 28120, + "▁microcode": 28121, + "▁Accounting": 28122, + "▁HackTricks": 28123, + "▁combustion": 28124, + "organizations": 28125, + "▁Historically": 28126, + "countermeasures": 28127, + "260": 28128, + "DMA": 28129, + "ERO": 28130, + "VID": 28131, + "▁;;": 28132, + "0040": 28133, + "IETF": 28134, + "▁0);": 28135, + "▁Abu": 28136, + ".4R3-": 28137, + "▁5.5,": 28138, + "▁PLAN": 28139, + "/2009-": 28140, + "▁3.0.3": 28141, + "▁ERROR": 28142, + "▁taxes": 28143, + "▁1800-1": 28144, + "▁coders": 28145, + "▁Loading": 28146, + "▁Airlines": 28147, + "conditions": 28148, + "▁oversight": 28149, + "▁treatments": 28150, + "▁Mississippi": 28151, + ",5": 28152, + "bz": 28153, + "wx": 28154, + "ει": 28155, + "abr": 28156, + "ран": 28157, + "▁UA": 28158, + "NTLM": 28159, + "sche": 28160, + "▁163": 28161, + "▁Eis": 28162, + "▁JVM": 28163, + "▁Nev": 28164, + "▁lan": 28165, + "▁4.10": 28166, + "▁6.1,": 28167, + "▁kits": 28168, + "Parent": 28169, + "Person": 28170, + "broker": 28171, + "▁dword": 28172, + "▁ClamAV": 28173, + "▁median": 28174, + "-2001-04": 28175, + "atherine": 28176, + "restrict": 28177, + "▁12.2.5,": 28178, + "▁Century": 28179, + "▁creator": 28180, + "▁deliber": 28181, + "▁endhint": 28182, + "▁Explicit": 28183, + "▁payments": 28184, + "▁GroupWise": 28185, + "▁perimeter": 28186, + "▁simplicity": 28187, + "▁compromises": 28188, + "▁syntactically": 28189, + "▁خ": 28190, + "19-": 28191, + "PLO": 28192, + "afa": 28193, + "jwt": 28194, + "▁DU": 28195, + "PAGE": 28196, + "avia": 28197, + "▁GOT": 28198, + "▁Hil": 28199, + "%3A15": 28200, + "OLICY": 28201, + "irmed": 28202, + "▁Burp": 28203, + "/2011-": 28204, + "ATTACK": 28205, + "histor": 28206, + "teness": 28207, + "▁4.1.0": 28208, + "▁Close": 28209, + "▁Ports": 28210, + "▁arter": 28211, + "▁genus": 28212, + "-2016-5": 28213, + "▁Marine": 28214, + "▁breath": 28215, + "▁buckets": 28216, + "▁archived": 28217, + "▁Entertain": 28218, + "▁restoring": 28219, + "▁strengths": 28220, + "▁NP": 28221, + "▁TC": 28222, + "▁[:": 28223, + "▁ws": 28224, + "/186": 28225, + "bios": 28226, + "rios": 28227, + "▁Gun": 28228, + "▁jun": 28229, + "-12-0": 28230, + "▁3.10": 28231, + "▁Cron": 28232, + "▁Advert": 28233, + "-2005-20": 28234, + "-2006-04": 28235, + "▁regener": 28236, + "▁rootkit": 28237, + "AmazonRDS": 28238, + "▁Expressions": 28239, + "▁characterize": 28240, + "9/0": 28241, + "Dll": 28242, + "HT5": 28243, + "eni": 28244, + "ých": 28245, + "▁لل": 28246, + "/296": 28247, + "ATOR": 28248, + "Unit": 28249, + "alin": 28250, + "hydr": 28251, + "ifik": 28252, + "yram": 28253, + "▁116": 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"das": 28307, + "▁V1": 28308, + "▁V4": 28309, + "ARTE": 28310, + "burn": 28311, + "grte": 28312, + "sens": 28313, + "▁SVN": 28314, + "▁VLC": 28315, + "▁dub": 28316, + "▁COPY": 28317, + "esktop": 28318, + "minute": 28319, + "themes": 28320, + "▁1925)": 28321, + "▁4.0.3": 28322, + "▁DEBUG": 28323, + "▁Sharp": 28324, + "Discord": 28325, + "▁Queens": 28326, + "▁colour": 28327, + "▁Backdoor": 28328, + "▁especific": 28329, + "▁tunneling": 28330, + "▁Applicable": 28331, + "▁Performing": 28332, + "▁newspapers": 28333, + "▁coefficient": 28334, + "▁programmers": 28335, + "▁reinforcement": 28336, + "Pop": 28337, + "TRS": 28338, + "xor": 28339, + "-532": 28340, + "/237": 28341, + "Dest": 28342, + "artz": 28343, + "asen": 28344, + "cube": 28345, + "enos": 28346, + "Limit": 28347, + "illed": 28348, + "▁Gain": 28349, + "▁Flags": 28350, + "▁RRSIG": 28351, + "▁thank": 28352, + "-2017-4": 28353, + "Twitter": 28354, + "kubelet": 28355, + "▁masses": 28356, + "▁Anthrop": 28357, + "OpenLinux": 28358, + 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28615, + "n3.": 28616, + "ugo": 28617, + "4000": 28618, + "haus": 28619, + "sqli": 28620, + "опас": 28621, + "▁183": 28622, + "▁ATP": 28623, + "-0000": 28624, + "Track": 28625, + "angan": 28626, + "oping": 28627, + "rison": 28628, + "yzing": 28629, + "▁0x60": 28630, + "▁ACLU": 28631, + "▁cosm": 28632, + "▁tier": 28633, + "▁torn": 28634, + "Denial": 28635, + "gnutls": 28636, + "icists": 28637, + "▁1928)": 28638, + "▁rastre": 28639, + "▁ruling": 28640, + "-2005-24": 28641, + "▁denomin": 28642, + "othetical": 28643, + "▁mistaken": 28644, + "▁traveled": 28645, + "▁Behavioral": 28646, + "▁Interfaces": 28647, + "▁predictive": 28648, + "▁supervisor": 28649, + "-----------|": 28650, + ")5": 28651, + "2k": 28652, + "nw": 28653, + "580": 28654, + "GNU": 28655, + "SUC": 28656, + "ddd": 28657, + "vet": 28658, + "–14": 28659, + "▁8:": 28660, + "-32.": 28661, + "fony": 28662, + "opro": 28663, + "▁Prü": 28664, + "#####": 28665, + "acies": 28666, + "leans": 28667, + "v2.6/": 28668, + "▁12.5": 28669, + 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28721, + ".1R3-": 28722, + "ecode": 28723, + "▁1.14": 28724, + "▁Wait": 28725, + "▁ware": 28726, + "▁être": 28727, + "▁0x080": 28728, + "derived": 28729, + "romised": 28730, + "▁Claude": 28731, + "▁Tamper": 28732, + "▁coffee": 28733, + "▁vessel": 28734, + "-2002-14": 28735, + "-2004-20": 28736, + "cloudapps": 28737, + "different": 28738, + "▁Malaysia": 28739, + "▁outright": 28740, + "▁Databases": 28741, + "▁ambiguity": 28742, + "▁collector": 28743, + "▁resolving": 28744, + "▁===============": 28745, + "сы": 28746, + "50-": 28747, + "LSA": 28748, + "MOV": 28749, + "gcc": 28750, + "1110": 28751, + "idal": 28752, + "ений": 28753, + "▁'--": 28754, + "▁(13": 28755, + "hyper": 28756, + "occus": 28757, + "three": 28758, + "▁(20)": 28759, + "celona": 28760, + "irates": 28761, + "▁Daily": 28762, + "▁SPARC": 28763, + "▁Morgan": 28764, + "▁galaxy": 28765, + "-2007-14": 28766, + "verified": 28767, + "▁clarity": 28768, + "▁genuine": 28769, + "▁heritage": 28770, + "stylesheet": 28771, + "▁customize": 28772, + "▁OpenSolaris": 28773, + "▁terrestrial": 28774, + "▁jurisdiction": 28775, + "▁transferring": 28776, + "▁advertisement": 28777, + "3::": 28778, + "715": 28779, + "kel": 28780, + "-306": 28781, + "/24/": 28782, + "Live": 28783, + "Menu": 28784, + "sent": 28785, + "▁Wol": 28786, + "Links": 28787, + "embed": 28788, + "▁grab": 28789, + "▁1.5.1": 28790, + "▁Proto": 28791, + "▁annex": 28792, + "▁códig": 28793, + "▁pipes": 28794, + "sourced": 28795, + "sentinel": 28796, + "▁isolate": 28797, + "msg00016.": 28798, + "▁Memorial": 28799, + "▁democracy": 28800, + "▁happening": 28801, + "▁telescope": 28802, + "▁hypotheses": 28803, + "▁unpredictable": 28804, + "nz": 28805, + "25:": 28806, + "316": 28807, + "45:": 28808, + "48-": 28809, + "▁J2": 28810, + "▁tx": 28811, + "▁验证": 28812, + "/359": 28813, + "cryp": 28814, + "ются": 28815, + "▁Gut": 28816, + "▁Kle": 28817, + "Right": 28818, + "byter": 28819, + "huntr": 28820, + "popup": 28821, + "pscan": 28822, + "▁Camb": 28823, + "▁mesh": 28824, + "▁paqu": 28825, + "▁vide": 28826, + "VIDIOC": 28827, + "rowing": 28828, + "▁Adams": 28829, + "▁expire": 28830, + "compress": 28831, + "ersonate": 28832, + "▁Arsenal": 28833, + "▁OPTIONAL": 28834, + "▁Received": 28835, + "▁notified": 28836, + "▁operands": 28837, + "structured": 28838, + "▁positional": 28839, + "▁transcript": 28840, + "▁outstanding": 28841, + "xf": 28842, + "Ear": 28843, + "cdn": 28844, + "еди": 28845, + "▁Bic": 28846, + "▁PAY": 28847, + "omega": 28848, + "atting": 28849, + "▁2008)": 28850, + "------|": 28851, + "▁dancer": 28852, + "▁lacked": 28853, + "Assembly": 28854, + "Endpoint": 28855, + "▁Affairs": 28856, + "▁induced": 28857, + "▁jointly": 28858, + "▁muscles": 28859, + "dashboard": 28860, + "▁abilities": 28861, + "▁segurança": 28862, + "▁subdomain": 28863, + "▁satellites": 28864, + "▁decentralized": 28865, + ".63": 28866, + "658": 28867, + "tib": 28868, + ":**\\": 28869, + "Mess": 28870, + "atid": 28871, + "isor": 28872, + "mage": 28873, + "nost": 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"releasen": 30581, + "▁obscure": 30582, + "▁Meetings": 30583, + "▁Students": 30584, + "▁sécurité": 30585, + "▁Principal": 30586, + "▁additions": 30587, + "announcement": 30588, + "▁Bibliography": 30589, + "▁fingerprinting": 30590, + "ín": 30591, + "516": 30592, + "ный": 30593, + "▁Aw": 30594, + "kali": 30595, + "▁9th": 30596, + "▁Cow": 30597, + "▁TAP": 30598, + "▁r11": 30599, + "Quest": 30600, + "▁duty": 30601, + "aruban": 30602, + "explor": 30603, + "gotten": 30604, + "olving": 30605, + "▁valve": 30606, + "▁Harris": 30607, + "▁Warsaw": 30608, + "▁beacon": 30609, + "▁casual": 30610, + "▁holder": 30611, + "▁pixels": 30612, + "▁signer": 30613, + "▁Reviews": 30614, + "▁Targets": 30615, + "▁Vincent": 30616, + "▁friendly": 30617, + "▁Hollywood": 30618, + "▁curriculum": 30619, + "▁publishers": 30620, + "▁Marketplace": 30621, + "--------------|": 30622, + "02,": 30623, + "354": 30624, + "rcv": 30625, + "svr": 30626, + "w3.": 30627, + "▁1–": 30628, + "▁ov": 30629, + "tras": 30630, + "▁GCC": 30631, + "▁Piv": 30632, + "▁amd": 30633, + "▁wyk": 30634, + "BRARY": 30635, + "hemas": 30636, + "uberc": 30637, + "urses": 30638, + "▁Pand": 30639, + "▁pret": 30640, + "▁Metro": 30641, + "▁Patri": 30642, + "resolve": 30643, + "segment": 30644, + "▁silent": 30645, + "-2006-24": 30646, + "-2017-02": 30647, + "positive": 30648, + "▁Calling": 30649, + "▁Toronto": 30650, + "▁stellar": 30651, + "ambiguous": 30652, + "▁deposits": 30653, + "erequisite": 30654, + "▁suffering": 30655, + "▁workspace": 30656, + "enumeration": 30657, + "▁AnyConnect": 30658, + "▁decreasing": 30659, + "establishment": 30660, + "اف": 30661, + "يد": 30662, + "55/": 30663, + "578": 30664, + "j7f": 30665, + "nor": 30666, + "rc2": 30667, + "ски": 30668, + "5/00": 30669, + "airo": 30670, + "▁98,": 30671, + "▁RTF": 30672, + "▁SSD": 30673, + "▁0x40": 30674, + "▁18.2": 30675, + "▁Dave": 30676, + "▁FIDO": 30677, + "▁Helm": 30678, + "Christ": 30679, + "Planet": 30680, + "▁Robin": 30681, + "▁Still": 30682, + "▁congr": 30683, + "▁noisy": 30684, + "Digital": 30685, + "factory": 30686, + "▁XnView": 30687, + "▁alphab": 30688, + "-2004-19": 30689, + "Registry": 30690, + "▁MongoDB": 30691, + "▁fastest": 30692, + "▁Normally": 30693, + "Prevention": 30694, + "▁magazines": 30695, + "▁Dependency": 30696, + "▁breakpoints": 30697, + "▁Interpretation": 30698, + "му": 30699, + "-70": 30700, + "JWT": 30701, + "▁می": 30702, + "\"\"\"\"": 30703, + "/349": 30704, + "inus": 30705, + "mart": 30706, + "▁Bav": 30707, + "▁Nom": 30708, + "▁Sor": 30709, + "ipper": 30710, + "olver": 30711, + "▁Rick": 30712, + "▁Ryan": 30713, + "▁drew": 30714, + "▁spur": 30715, + "inters": 30716, + "urface": 30717, + "▁6.2.0": 30718, + "▁Artif": 30719, + "▁Macro": 30720, + "▁Pseud": 30721, + "samsung": 30722, + "▁Making": 30723, + "actively": 30724, + "▁Stealth": 30725, + "▁freeing": 30726, + "Incorrect": 30727, + "▁supporters": 30728, + "aquasecurity": 30729, + "arubanetworks": 30730, + "▁interpretations": 30731, + "Ms": 30732, + "690": 30733, + "Ker": 30734, + "ORG": 30735, + "RTC": 30736, + "guy": 30737, + "hyp": 30738, + "-1.2": 30739, + "/320": 30740, + "LENG": 30741, + "beit": 30742, + "‌های": 30743, + "▁cla": 30744, + "leaks": 30745, + "▁1915": 30746, + "▁CIFS": 30747, + "▁TELE": 30748, + "▁USSR": 30749, + "▁0x400": 30750, + "▁Cacti": 30751, + "▁Niger": 30752, + "▁coins": 30753, + "▁fonts": 30754, + "▁danger": 30755, + "-2006-14": 30756, + "duration": 30757, + "Forwarded": 30758, + "▁Germanic": 30759, + "▁ignoring": 30760, + "2016-28817": 30761, + "▁Forescout": 30762, + "▁resistant": 30763, + "▁subscribe": 30764, + "▁Antarctica": 30765, + "▁volunteers": 30766, + "▁Lightweight": 30767, + "▁compartment": 30768, + "▁reservation": 30769, + "xg": 30770, + ".0;": 30771, + ".5)": 30772, + "39/": 30773, + "80b": 30774, + "▁9:": 30775, + "/21/": 30776, + "talk": 30777, + "▁CIA": 30778, + "elian": 30779, + "elaide": 30780, + "▁1920)": 30781, + "▁Vegas": 30782, + "UNKNOWN": 30783, + "-2006-19": 30784, + "frontend": 30785, + "▁evident": 30786, + "▁sockaddr": 30787, + "jul2016-30": 30788, + "▁deliberately": 30789, + "359": 30790, + "564": 30791, + "57/": 30792, + "=24": 30793, + "SAML": 30794, + "UNIX": 30795, + "ivic": 30796, + "masq": 30797, + "▁MED": 30798, + "▁gig": 30799, + "erset": 30800, + "▁CAPT": 30801, + "▁Digi": 30802, + "▁Hawk": 30803, + "▁9.0.0": 30804, + "▁Armed": 30805, + "▁Colon": 30806, + "▁blast": 30807, + "▁helic": 30808, + "Managed": 30809, + "▁Harold": 30810, + "-2007-19": 30811, + "-2007-50": 30812, + "▁evening": 30813, + "▁leaking": 30814, + "▁seventh": 30815, + "/2006/06/": 30816, + "databases": 30817, + "▁Equation": 30818, + "▁15.006.30": 30819, + "▁Firewalls": 30820, + "▁Normative": 30821, + "-2012-03-07": 30822, + "▁172.16.191": 30823, + "▁subscriptions": 30824, + "SSI": 30825, + "ncpy": 30826, + "uary": 30827, + "▁Aer": 30828, + "▁Fly": 30829, + "▁OPC": 30830, + "Hound": 30831, + "sp800": 30832, + "▁Laws": 30833, + "▁Prob": 30834, + "▁Situ": 30835, + "father": 30836, + "labels": 30837, + "ogenic": 30838, + "▁1.12.": 30839, + "▁10000": 30840, + "▁Milan": 30841, + "▁Symbol": 30842, + "-2002-19": 30843, + "resolved": 30844, + "▁Startup": 30845, + "PLICATION": 30846, + "▁regulated": 30847, + "▁discretion": 30848, + "▁entreprene": 30849, + "▁irrelevant": 30850, + "▁disconnected": 30851, + "▁transmissions": 30852, + "▁subdirectories": 30853, + "9.2": 30854, + "EST": 30855, + "rsp": 30856, + "▁2F": 30857, + "+0x1": 30858, + "/283": 30859, + "3333": 30860, + "Ping": 30861, + "▁AIM": 30862, + "▁Sab": 30863, + "▁rul": 30864, + "/2024": 30865, + "Owned": 30866, + "ensen": 30867, + "escap": 30868, + "radio": 30869, + "sonic": 30870, + "▁STAR": 30871, + "▁brew": 30872, + "▁clay": 30873, + "▁1997.": 30874, + "▁5.3.1": 30875, + "▁Ptole": 30876, + "▁rifle": 30877, + "▁(2018,": 30878, + "▁cPanel": 30879, + "▁fertil": 30880, + "▁floppy": 30881, + "▁pursue": 30882, + "▁Drivers": 30883, + "▁Phantom": 30884, + "▁strange": 30885, + "▁dramatic": 30886, + "▁diplomatic": 30887, + "linuxsecurity": 30888, + "▁pseudorandom": 30889, + "▁concatenation": 30890, + "99:": 30891, + "▁PF": 30892, + "▁RM": 30893, + ")**:": 30894, + "2350": 30895, + "elin": 30896, + "slot": 30897, + "▁118": 30898, + "▁119": 30899, + "▁cmp": 30900, + "▁gpg": 30901, + "▁ram": 30902, + "▁rib": 30903, + "10000": 30904, + "BASED": 30905, + "▁CALL": 30906, + "▁SBOM": 30907, + "queror": 30908, + "▁Henri": 30909, + "▁Maxim": 30910, + "▁Satur": 30911, + "▁mayor": 30912, + "▁weigh": 30913, + "itorial": 30914, + "▁Arctic": 30915, + "▁Credit": 30916, + "▁Fellow": 30917, + "▁Monday": 30918, + "▁financ": 30919, + "shutdown": 30920, + "▁skilled": 30921, + "▁безопас": 30922, + "▁attorney": 30923, + "▁workshop": 30924, + "Kubernetes": 30925, + "▁Characters": 30926, + "releasenotes": 30927, + "▁sustainability": 30928, + "Oi": 30929, + "▁č": 30930, + "4),": 30931, + "65/": 30932, + ":12": 30933, + "▁.\\": 30934, + ".8.2": 30935, + "NING": 30936, + "hRep": 30937, + "loqu": 30938, + "rees": 30939, + "stad": 30940, + "▁ACC": 30941, + "▁bes": 30942, + "Quick": 30943, + "chell": 30944, + "react": 30945, + "▁\"...": 30946, + "▁Glen": 30947, + "▁belt": 30948, + "▁horm": 30949, + "▁rent": 30950, + "assium": 30951, + "▁joins": 30952, + "▁puede": 30953, + "▁Annual": 30954, + "▁proofs": 30955, + "-2004-22": 30956, + "-2006-38": 30957, + "▁helpers": 30958, + "▁Exercise": 30959, + "▁charging": 30960, + "▁situated": 30961, + "▁bootstrap": 30962, + "▁configures": 30963, + "▁convincing": 30964, + "▁neutralize": 30965, + "▁granularity": 30966, + "▁disassembler": 30967, + "ικ": 30968, + "文件": 30969, + "FMT": 30970, + "Spr": 30971, + "THER": 30972, + "aust": 30973, + "bons": 30974, + "olib": 30975, + "▁McG": 30976, + "▁bog": 30977, + "DWORD": 30978, + "▁6.1.": 30979, + "▁Kill": 30980, + "▁desk": 30981, + "aceful": 30982, + "▁20070": 30983, + "▁Maker": 30984, + "▁Stage": 30985, + "▁blobs": 30986, + "▁Linked": 30987, + "UserName": 30988, + "▁15.1X49": 30989, + "▁Stevens": 30990, + "▁delimit": 30991, + "▁enjoyed": 30992, + "▁monthly": 30993, + "▁proving": 30994, + "▁sourced": 30995, + "▁Albanian": 30996, + "▁Initiation": 30997, + "332": 30998, + "0030": 30999, + "Prom": 31000, + "Vuln": 31001, + "WiFi": 31002, + "▁DPC": 31003, + "▁usb": 31004, + "badge": 31005, + "urray": 31006, + "▁Corp": 31007, + "stdout": 31008, + "▁Corps": 31009, + "estones": 31010, + "focused": 31011, + "marking": 31012, + "▁enthus": 31013, + "▁firing": 31014, + "▁iPadOS": 31015, + "-2005-14": 31016, + "handling": 31017, + "▁hostile": 31018, + "▁lattice": 31019, + "▁consumes": 31020, + "▁../../../": 31021, + "▁Telehealth": 31022, + "▁LibreOffice": 31023, + "▁instability": 31024, + "▁virtualized": 31025, + "Mi": 31026, + "iş": 31027, + "ANG": 31028, + "avo": 31029, + "/276": 31030, + "logo": 31031, + "▁(30": 31032, + "▁WHO": 31033, + ".03.0": 31034, + "abbix": 31035, + "allas": 31036, + "anmar": 31037, + "▁1.21": 31038, + "▁3.4.": 31039, + "▁CERT": 31040, + "▁Drug": 31041, + "▁Gent": 31042, + "▁Madr": 31043, + "▁tram": 31044, + "iotics": 31045, + "▁1.5.2": 31046, + "▁1931)": 31047, + "▁1994.": 31048, + "▁5.01,": 31049, + "WARNING": 31050, + "because": 31051, + "▁Fisher": 31052, + "▁Pascal": 31053, + "▁прилож": 31054, + "▁Germans": 31055, + ".800-53r5": 31056, + "Configure": 31057, + "Principal": 31058, + "▁authored": 31059, + "▁grouping": 31060, + "▁Hardening": 31061, + "▁packaging": 31062, + "’)": 31063, + ".\\\\": 31064, + "52:": 31065, + "699": 31066, + "SSO": 31067, + "db8": 31068, + "()()": 31069, + "/384": 31070, + "RUST": 31071, + "TIFF": 31072, + "atan": 31073, + "yxel": 31074, + "▁203": 31075, + "▁rod": 31076, + "aphor": 31077, + "games": 31078, + "teenth": 31079, + "▁1994,": 31080, + "▁neutr": 31081, + "▁Oliver": 31082, + "▁SUPPLY": 31083, + "▁código": 31084, + "-2006-02": 31085, + "timeline": 31086, + "▁wrapped": 31087, + "▁animated": 31088, + "▁interfer": 31089, + "namespaces": 31090, + "▁audiences": 31091, + "ivesecurity": 31092, + "▁IDENTIFIER": 31093, + "▁Literature": 31094, + "▁journalists": 31095, + "▁inaccessible": 31096, + "40/": 31097, + "60-": 31098, + "NEW": 31099, + "vnc": 31100, + "дер": 31101, + "пол": 31102, + "▁\"'": 31103, + "▁#+": 31104, + "▁@@": 31105, + "▁QR": 31106, + "/273": 31107, + "SPEC": 31108, + "astr": 31109, + "▁Put": 31110, + "SCALL": 31111, + "datas": 31112, + "lings": 31113, + "yzers": 31114, + "▁1934)": 31115, + "▁Taran": 31116, + "▁Teams": 31117, + "236628.": 31118, + "Maximum": 31119, + "▁PayPal": 31120, + "▁impose": 31121, + "Weakness": 31122, + "▁Applying": 31123, + "▁laureate": 31124, + "▁renderer": 31125, + "HttpRequest": 31126, + "▁fraudulent": 31127, + "cpujan2018-3": 31128, + "▁optimizations": 31129, + "że": 31130, + ").\"": 31131, + "63:": 31132, + "OTS": 31133, + "_2_": 31134, + "hcl": 31135, + "leh": 31136, + "/413": 31137, + "▁Kay": 31138, + "▁PIX": 31139, + "ucket": 31140, + "▁UPnP": 31141, + "▁dari": 31142, + "▁APQ80": 31143, + "▁IMail": 31144, + "Allowed": 31145, + "odester": 31146, + "▁Ivanti": 31147, + "▁cables": 31148, + "▁invert": 31149, + "-2005-09": 31150, + "-2005-44": 31151, + "-2006-03": 31152, + "▁octicon": 31153, + "▁Programm": 31154, + "▁Venezuel": 31155, + "▁severely": 31156, + "▁Territory": 31157, + "▁preparing": 31158, + "▁Validating": 31159, + "▁separating": 31160, + "!!!!!!!!!!!!!!!!": 31161, + "224": 31162, + "42:": 31163, + "/126": 31164, + "2100": 31165, + "para": 31166, + "tran": 31167, + "urbs": 31168, + "▁2-1": 31169, + "▁90%": 31170, + "▁Tob": 31171, + "Party": 31172, + "adays": 31173, + "endum": 31174, + "4RUj7f": 31175, + "Signer": 31176, + "▁10.5,": 31177, + "▁1989.": 31178, + "▁Liver": 31179, + "▁Nepal": 31180, + "▁Rhine": 31181, + "▁redes": 31182, + "Extract": 31183, + "-2007-53": 31184, + "▁arrives": 31185, + "anonymous": 31186, + "▁ARCserve": 31187, + "▁Province": 31188, + "▁ontology": 31189, + "▁organize": 31190, + "hRep4RUj7f": 31191, + "▁scholarly": 31192, + "▁insecurely": 31193, + "мы": 31194, + "54:": 31195, + "T14": 31196, + "▁dy": 31197, + "-01_": 31198, + "sten": 31199, + "▁Bed": 31200, + "▁Esp": 31201, + "▁Sit": 31202, + "▁TER": 31203, + "instr": 31204, + "▁0x55": 31205, + "▁1905": 31206, + "▁Past": 31207, + "▁Zend": 31208, + "▁Zeus": 31209, + "Native": 31210, + "SPONSE": 31211, + "▁Compet": 31212, + "▁Submit": 31213, + "▁Guardian": 31214, + "▁metallic": 31215, + "▁rejection": 31216, + "Performance": 31217, + "▁biodiversity": 31218, + "-3)": 31219, + ".45": 31220, + "/61": 31221, + "762": 31222, + "HT2": 31223, + "ods": 31224, + "сту": 31225, + "▁_<": 31226, + "▁oc": 31227, + "Drop": 31228, + "ms99": 31229, + "otyp": 31230, + "▁(``": 31231, + "▁208": 31232, + "▁AN0": 31233, + "▁PSK": 31234, + "UTION": 31235, + "▁Peer": 31236, + "▁Tick": 31237, + "▁pcap": 31238, + "▁wird": 31239, + "atable": 31240, + "imodal": 31241, + "inders": 31242, + "rebase": 31243, + "▁10.1,": 31244, + "▁Burke": 31245, + "▁blogs": 31246, + "MACHINE": 31247, + "wrapper": 31248, + "-2008-50": 31249, + "-2009-00": 31250, + "ilingual": 31251, + "▁extinct": 31252, + "▁quoting": 31253, + "strapping": 31254, + "▁Discover": 31255, + "▁Thompson": 31256, + "▁reversed": 31257, + "▁attribution": 31258, + "▁politicians": 31259, + "▁counterparts": 31260, + "▁sequentially": 31261, + "یت": 31262, + "–8": 31263, + "/)\\": 31264, + "2}}": 31265, + "san": 31266, + "мер": 31267, + "▁Zo": 31268, + "▁c7": 31269, + "-19.": 31270, + "Supp": 31271, + "bolt": 31272, + "erne": 31273, + "olly": 31274, + "prob": 31275, + "▁12-": 31276, + "▁SEE": 31277, + "eyond": 31278, + "idean": 31279, + "niscc": 31280, + "ocode": 31281, + "▁2.7.": 31282, + "▁5.2,": 31283, + "▁8000": 31284, + "Victim": 31285, + "expand": 31286, + "▁1996.": 31287, + "▁Carlo": 31288, + "▁RRset": 31289, + "▁alien": 31290, + "▁debut": 31291, + "▁safer": 31292, + "msg0004": 31293, + "▁Breach": 31294, + "▁Aramaic": 31295, + "▁Lebanon": 31296, + "-2016-108": 31297, + "▁inactive": 31298, + "▁mainland": 31299, + "verification": 31300, + "▁anniversary": 31301, + "-4:": 31302, + "377": 31303, + "74/": 31304, + "799": 31305, + "::\\": 31306, + "ESA": 31307, + "uxe": 31308, + "/...": 31309, + "/15/": 31310, + "TA06": 31311, + "rett": 31312, + "znál": 31313, + "▁650": 31314, + "▁MOS": 31315, + "chmod": 31316, + "letic": 31317, + "www1.": 31318, + "▁Cong": 31319, + "▁Join": 31320, + "▁Lamb": 31321, + "▁Left": 31322, + "echnet": 31323, + "twcert": 31324, + "▁intel": 31325, + "▁todos": 31326, + "▁Campus": 31327, + "▁commem": 31328, + "-2007-40": 31329, + "▁Lookout": 31330, + "▁Smarter": 31331, + "▁chromos": 31332, + "▁ecology": 31333, + "▁coherent": 31334, + "▁dissemin": 31335, + "▁engaging": 31336, + "▁reporter": 31337, + "▁acquiring": 31338, + "▁occupation": 31339, + "▁Declaration": 31340, + "}^": 31341, + "24-": 31342, + "39:": 31343, + "652": 31344, + "=\"_": 31345, + "crm": 31346, + "/25/": 31347, + "05-0": 31348, + "Font": 31349, + "anim": 31350, + "atos": 31351, + "hots": 31352, + "mind": 31353, + "rain": 31354, + "▁xen": 31355, + "1-01-": 31356, + "Armor": 31357, + "_256_": 31358, 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