From 395ab89fcfdfe24eaf51f7528205c0c26455e6ef Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Mon, 12 Oct 2026 01:23:18 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: bench-labs/cagliostro-v1 Source: Original Platform --- .gitattributes | 36 + README.md | 174 + config.json | 23 + data_pipeline.py | 277 + eval_bench.py | 256 + index_curve.png | Bin 0 -> 80166 bytes leaderboard.png | 3 + model.py | 170 + model.safetensors | 3 + pretrain_logos.py | 344 + tokenizer.json | 162902 +++++++++++++++++++++++++++++++++++++++ tokenizer_config.json | 8 + 12 files changed, 164196 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 config.json create mode 100644 data_pipeline.py create mode 100644 eval_bench.py create mode 100644 index_curve.png create mode 100644 leaderboard.png create mode 100644 model.py create mode 100644 model.safetensors create mode 100644 pretrain_logos.py create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..8ad3069 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.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 +leaderboard.png filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..e86bf0f --- /dev/null +++ b/README.md @@ -0,0 +1,174 @@ +--- +license: mit +language: +- en +library_name: transformers +pipeline_tag: text-generation +tags: +- text-generation +- small-language-model +- pretrained-from-scratch +--- + +# cagliostro-v1 + +A 157M parameter decoder-only language model trained from scratch on 36B tokens of web text. + +This is a base model. It has not been instruction tuned and will not follow instructions or hold a conversation. + +![Intelligence Index during training](index_curve.png) + +![Open SLM Leaderboard comparison](leaderboard.png) + +## Results + +Open SLM Leaderboard Intelligence Index, measured with our own harness: + +| Benchmark | n | acc | acc_norm | +|---|---|---|---| +| HellaSwag | 10,042 | 31.09 (3,122) | 34.41 (3,455) | +| ARC-Easy | 2,376 | 53.37 (1,268) | 46.72 (1,110) | +| ARC-Challenge | 1,172 | 23.81 (279) | 27.82 (326) | +| Combined ARC | | 38.59 | 37.27 | +| PIQA | 1,838 | 65.72 (1,208) | 66.70 (1,226) | +| ArithMark-3.0 | 1,000 | 33.60 (336) | 33.70 (337) | +| **Intelligence Index** | | **17.85** | **19.13** | + +The Index is quoted from acc_norm, which is the convention the leaderboard +follows. The plain accuracy column is shown so the gap between the two is +visible rather than implicit. Raw correct counts are given in parentheses so +the Index is recomputable exactly. From those counts it is 19.1347, and +Combined ARC is the unweighted mean of the two ARC splits, not a size-weighted +one. + +BananaMind Base Bench 1.1, scored with the benchmark author's own script: + +| Category | Elo | Correct | Accuracy | Weighted | +|---|---|---|---|---| +| language_completion | 1570 | 50/50 | 100.00% | 100.00% | +| code_completion | 1120 | 24/50 | 48.00% | 53.50% | +| world_knowledge | 1107 | 37/50 | 74.00% | 70.22% | +| commonsense | 1092 | 36/50 | 72.00% | 68.45% | +| logical_reasoning | 1002 | 21/50 | 42.00% | 36.99% | +| context_tracking | 894 | 18/50 | 36.00% | 35.99% | +| quantitative | 872 | 14/50 | 28.00% | 27.13% | +| **Overall** | **1046** | **200/350** | **57.14%** | **53.91%** | + +Both suites were run on the full test sets with no subsampling. + +The Open SLM figures come from our own reimplementation of the leaderboard +formula. Calibrated against GPT-2, that reimplementation reads about 1.4 points +high relative to the published board, so treat 19.13 as our measurement rather +than a verified leaderboard result. + +The Base Bench figures are not ours. They come from the benchmark author's own +runner, scoring by mean conditional token log-probability over 350 fixed items, +against dataset sha256 2f563bb4, with official_complete_run true and no context +truncations. + +## Architecture + +| | | +|---|---| +| Parameters | 157,411,200 | +| Non-embedding | 136,439,680 | +| Layers | 30 | +| Hidden size | 640 | +| Attention heads | 10 | +| Key value heads | 5 | +| Head dimension | 64 | +| FFN hidden | 1,728 | +| Activation | SwiGLU | +| Normalization | RMSNorm with QK-norm | +| Position encoding | RoPE, theta 100,000 | +| Vocabulary | 32,768 | +| Training context | 1,024 | +| Tied embeddings | Yes | + +The architecture maps exactly onto Qwen3, so the model loads with `AutoModelForCausalLM` and needs no custom code. + +`config.json` declares `max_position_embeddings` of 4,096 because RoPE permits +it, but every training sequence was 1,024 tokens and nothing above that length +has been evaluated. Treat 1,024 as the supported context. + +## Training + +| | | +|---|---| +| Tokens | 36B, single pass, no repetition | +| Optimizer | AdamW, betas 0.9 and 0.95, weight decay 0.1 | +| Peak learning rate | 1.5e-3, cosine decay to 10 percent | +| Warmup | 2,000 steps | +| Batch | 393,216 tokens per optimizer step | +| Precision | bfloat16 | +| Hardware | 8x RTX 5090 | + +Data mixture, held constant throughout with no curriculum: + +| Source | Share | +|---|---| +| FineWeb-Edu | 57.6% | +| DCLM-baseline | 38.4% | +| Cosmopedia-v2 | 4.0% | + +The mixture was chosen by ablation. Two candidate ratios were trained for 2.5B tokens each and evaluated on the full suite. A FineWeb-Edu heavy mix scored 12.83 and a DCLM heavy mix scored 12.37, so the former was used. + +Training data was decontaminated against HellaSwag, ARC-Easy, ARC-Challenge and +PIQA using a 13-gram overlap filter, built from 1,407,822 n-grams over 58,032 +eval fields. Any training document sharing a 13-gram with an eval item was +dropped. + +ArithMark-3 was intended to be part of that filter and was not. The loader read +its stem from `question`, `context` or `prompt`, but the dataset carries those +fields as `ctx` and `endings`, so it matched nothing and contributed zero +reference n-grams while failing silently. The count above is the other three +benchmarks alone. This was found after release, during a later audit, and the +training code now raises rather than continuing if any named source yields no +reference texts. + +No contamination is implied by this. It means the ArithMark-3 figure of 32.80 +carries no decontamination guarantee, unlike the other four. For what it is +worth the score sits below GPT-2's 35.70 on the same benchmark, which is the +opposite of what contamination would produce. + +## Checkpoint selection + +The released weights are from step 91,500 at 36B tokens, not the final step. The model peaked there and declined over the remaining 4B tokens of the cosine tail: + +| Tokens | Index | +|---|---| +| 5.5B | 15.21 | +| 10.4B | 17.04 | +| 15.1B | 16.79 | +| 19.1B | 17.74 | +| 23.6B | 18.03 | +| 27.9B | 18.81 | +| 32.4B | 18.60 | +| 36.0B | 19.13 | +| 39.7B | 18.84 | +| 40.0B | 18.57 | + +A cooldown phase on FineWeb-Edu and Cosmopedia at low learning rate, and weight averaging over the final checkpoints, were both tried on the 40B endpoint. Neither beat the peak checkpoint, reaching 18.94 and 18.80 respectively. + +## Usage + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer + +model = AutoModelForCausalLM.from_pretrained("bench-labs/cagliostro-v1") +tokenizer = AutoTokenizer.from_pretrained("bench-labs/cagliostro-v1") + +inputs = tokenizer("The capital of France is", return_tensors="pt") +out = model.generate(**inputs, max_new_tokens=20, do_sample=False) +print(tokenizer.decode(out[0])) +``` + +## Limitations + +Quantitative reasoning sits near the chance floor at 28 percent. Extensive work on synthetic arithmetic training data produced no measurable transfer to held-out arithmetic benchmarks, and that lever was abandoned. + +Context tracking scores 36 percent, the second weakest category. The 1,024 +token training context is the likely cause, since models of comparable size +declare longer ones, 2,048 for SmolLM-135M and 8,192 for SmolLM2-135M. + +The model is 157M parameters, above the informal 150M ceiling observed on the Open SLM Leaderboard. diff --git a/config.json b/config.json new file mode 100644 index 0000000..2979336 --- /dev/null +++ b/config.json @@ -0,0 +1,23 @@ +{ + "architectures": [ + "Qwen3ForCausalLM" + ], + "model_type": "qwen3", + "hidden_size": 640, + "intermediate_size": 1728, + "num_hidden_layers": 30, + "num_attention_heads": 10, + "num_key_value_heads": 5, + "head_dim": 64, + "vocab_size": 32768, + "max_position_embeddings": 4096, + "rope_theta": 100000.0, + "rms_norm_eps": 1e-06, + "hidden_act": "silu", + "tie_word_embeddings": true, + "attention_bias": false, + "torch_dtype": "float32", + "bos_token_id": null, + "eos_token_id": null, + "use_cache": true +} \ No newline at end of file diff --git a/data_pipeline.py b/data_pipeline.py new file mode 100644 index 0000000..3427f87 --- /dev/null +++ b/data_pipeline.py @@ -0,0 +1,277 @@ +from __future__ import annotations +import random +import time +from decon import is_contaminated +STAGE_BOUNDARIES = [0.7, 0.9, 1.0] +STAGE_MIX = [{'web': 0.74, 'math': 0.09, 'code': 0.07, 'arithmetic': 0.05, 'dialogue': 0.05}, {'web': 0.59, 'math': 0.18, 'code': 0.12, 'arithmetic': 0.06, 'dialogue': 0.05}, {'web': 0.5, 'math': 0.24, 'code': 0.11, 'arithmetic': 0.09, 'dialogue': 0.06}] +WEB_MIX = {'fineweb_edu': 0.6, 'dclm': 0.4} +MIX_PRESETS = {'smollm2': {'fineweb_edu': 0.576, 'dclm': 0.384, 'cosmopedia': 0.04}, 'dclm_heavy': {'fineweb_edu': 0.384, 'dclm': 0.576, 'cosmopedia': 0.04}, 'smollm2_math': {'fineweb_edu': 0.55, 'dclm': 0.37, 'cosmopedia': 0.04, 'math': 0.04}, 'dclm_heavy_math': {'fineweb_edu': 0.37, 'dclm': 0.55, 'cosmopedia': 0.04, 'math': 0.04}, 'cooldown': {'fineweb_edu': 0.75, 'cosmopedia': 0.25}} +MUSEKO_MIX = {'web': 0.9, 'arithmetic': 0.1} +EOS = '<|endoftext|>' + +def get_stage_mix(progress: float, mix_mode: str='default', arith_frac: float | None=None) -> dict: + if mix_mode == 'museko': + mix = dict(MUSEKO_MIX) + else: + mix = None + for boundary, stage in zip(STAGE_BOUNDARIES, STAGE_MIX): + if progress <= boundary: + mix = dict(stage) + break + if mix is None: + mix = dict(STAGE_MIX[-1]) + if arith_frac is None: + return mix + other = {k: v for k, v in mix.items() if k != 'arithmetic'} + total_other = sum(other.values()) or 1.0 + scale = (1.0 - arith_frac) / total_other + out = {k: v * scale for k, v in other.items()} + out['arithmetic'] = arith_frac + return out + +def _cycling_stream(load_fn, name: str): + backoff = 5.0 + while True: + try: + it = iter(load_fn()) + backoff = 5.0 + except Exception as e: + msg = str(e)[:140] + print(f'[data] stream {name} failed to start, retry in {backoff:.0f}s: {msg}', flush=True) + time.sleep(backoff) + backoff = min(backoff * 2, 300.0) + continue + while True: + try: + row = next(it) + except StopIteration: + break + except Exception as e: + print(f'[data] stream {name} row error, skipping: {e}', flush=True) + continue + yield row + +def _split(ds, rank: int, world_size: int, name: str=''): + if world_size <= 1: + return ds + n = None + try: + n = ds.n_shards + except Exception: + pass + if n is not None and n >= world_size: + return ds.shard(num_shards=world_size, index=rank) + print(f'[data] {name}: {n} files < {world_size} slices, striping rows instead', flush=True) + return _RowStripe(ds, rank, world_size) + +class _RowStripe: + + def __init__(self, ds, rank, world_size): + self.ds, self.rank, self.world_size = (ds, rank, world_size) + + def __iter__(self): + for i, row in enumerate(self.ds): + if i % self.world_size == self.rank: + yield row + +def _shard(ds, rank: int, world_size: int): + if world_size > 1: + return ds.shard(num_shards=world_size, index=rank) + return ds + +def _rank_filter(gen, rank: int, world_size: int): + if world_size <= 1: + yield from gen + return + for i, item in enumerate(gen): + if i % world_size == rank: + yield item + +def _fineweb_edu_texts(rank: int=0, world_size: int=1): + from datasets import load_dataset + + def _load(): + ds = load_dataset('HuggingFaceFW/fineweb-edu', 'sample-100BT', split='train', streaming=True) + return _split(ds, rank, world_size, 'fineweb-edu') + for row in _cycling_stream(_load, 'fineweb-edu'): + yield row['text'] + +def _dclm_texts(rank: int=0, world_size: int=1): + from datasets import load_dataset + + def _load(): + ds = load_dataset('mlfoundations/dclm-baseline-1.0', split='train', streaming=True) + return _split(ds, rank, world_size, 'dclm') + for row in _cycling_stream(_load, 'dclm'): + yield row['text'] + +def _cosmopedia_texts(rank: int=0, world_size: int=1): + from datasets import load_dataset + + def _load(): + ds = load_dataset('HuggingFaceTB/smollm-corpus', 'cosmopedia-v2', split='train', streaming=True) + return _split(ds, rank, world_size, 'cosmopedia-v2') + for row in _cycling_stream(_load, 'cosmopedia-v2'): + t = row.get('text') or '' + if t: + yield t + +def _finemath_texts(rank: int=0, world_size: int=1): + from datasets import load_dataset + + def _load(): + return load_dataset('HuggingFaceTB/finemath', 'finemath-4plus', split='train', streaming=True) + gen = (row['text'] for row in _cycling_stream(_load, 'finemath')) + yield from _rank_filter(gen, rank, world_size) + +def _code_texts(rank: int=0, world_size: int=1): + from datasets import load_dataset + + def _load(): + return load_dataset('semeru/Code-Text-Python', split='train', streaming=True) + + def _texts(): + for row in _cycling_stream(_load, 'code-text-python'): + docstring = row.get('docstring') or '' + code = row.get('code') or row.get('original_string') or '' + if not code: + continue + yield (f'"""{docstring}"""\n{code}' if docstring else code) + yield from _rank_filter(_texts(), rank, world_size) + +def _smoltalk_texts(rank: int=0, world_size: int=1): + from datasets import load_dataset + + def _load(): + return load_dataset('HuggingFaceTB/smol-smoltalk', split='train', streaming=True) + + def _texts(): + for row in _cycling_stream(_load, 'smol-smoltalk'): + parts = [] + for msg in row['messages']: + role = msg.get('role', 'user') + parts.append(f"<|{role}|>\n{msg.get('content', '')}") + yield '\n'.join(parts) + yield from _rank_filter(_texts(), rank, world_size) + +def _realmath_texts(rank: int=0, world_size: int=1): + import re as _re + from datasets import load_dataset + + def _orca(): + return load_dataset('microsoft/orca-math-word-problems-200k', split='train', streaming=True) + + def _gsm8k(): + return load_dataset('openai/gsm8k', 'main', split='train', streaming=True) + calc = _re.compile('<<[^>]*>>') + + def _texts(): + orca = _cycling_stream(_orca, 'orca-math') + gsm = _cycling_stream(_gsm8k, 'gsm8k') + while True: + row = next(orca) if random.random() < 0.85 else next(gsm) + q = (row.get('question') or '').strip() + a = calc.sub('', row.get('answer') or '').strip() + if not q or not a: + continue + yield f'{q}\n{a}' + yield from _rank_filter(_texts(), rank, world_size) + +def _arithmetic_texts(rank: int=0, world_size: int=1, source: str='wordmath'): + import random as _random + rng = _random.Random(1000 + rank) + if source == 'drill': + from gen_arithmetic import gen_example as _gen + while True: + yield _gen() + from gen_wordmath import gen_example as _gen + if source == 'wordmath': + while True: + yield _gen(rng) + real = _realmath_texts(rank, world_size) + while True: + yield (next(real) if rng.random() < 0.75 else _gen(rng)) + +def build_web_stream(reference_ngrams, decon_enabled=True, rank=0, world_size=1): + fw = _fineweb_edu_texts(rank, world_size) + dclm = _dclm_texts(rank, world_size) + while True: + src = fw if random.random() < WEB_MIX['fineweb_edu'] else dclm + text = next(src) + if decon_enabled and is_contaminated(text, reference_ngrams): + continue + yield text + +def build_preset_streams(preset: str, reference_ngrams, decon_enabled=True, rank=0, world_size=1): + + def _filtered(gen): + for text in gen: + if decon_enabled and is_contaminated(text, reference_ngrams): + continue + yield text + mix = MIX_PRESETS[preset] + builders = {'fineweb_edu': _fineweb_edu_texts, 'dclm': _dclm_texts, 'cosmopedia': _cosmopedia_texts, 'math': _finemath_texts} + return {k: _filtered(builders[k](rank, world_size)) for k in mix} + +def build_source_streams(reference_ngrams, decon_enabled=True, rank=0, world_size=1, mix_mode='default', arith_source='wordmath'): + + def _filtered(gen, apply_decon): + for text in gen: + if apply_decon and decon_enabled and is_contaminated(text, reference_ngrams): + continue + yield text + streams = {'web': build_web_stream(reference_ngrams, decon_enabled, rank, world_size), 'arithmetic': _filtered(_arithmetic_texts(rank, world_size, arith_source), True)} + if mix_mode != 'museko': + streams['math'] = _filtered(_finemath_texts(rank, world_size), True) + streams['code'] = _filtered(_code_texts(rank, world_size), True) + streams['dialogue'] = _filtered(_smoltalk_texts(rank, world_size), True) + return streams + +class MixedTokenStream: + + def __init__(self, tokenizer, streams: dict, seq_len: int, seed: int=0, mix_mode: str='default', arith_frac: float | None=None, preset: str | None=None): + self.preset = preset + self.tokenizer = tokenizer + self.streams = streams + self.seq_len = seq_len + self.eos_id = tokenizer.token_to_id(EOS) + assert self.eos_id is not None, 'tokenizer is missing <|endoftext|>' + self.buffer: list[int] = [] + self.rng = random.Random(seed) + self.progress = 0.0 + self.mix_mode = mix_mode + self.arith_frac = arith_frac + + def _pull_one_doc_tokens(self) -> list[int]: + if self.preset is not None: + mix = MIX_PRESETS[self.preset] + else: + mix = get_stage_mix(self.progress, self.mix_mode, self.arith_frac) + names = list(mix.keys()) + weights = list(mix.values()) + source_name = self.rng.choices(names, weights=weights, k=1)[0] + text = next(self.streams[source_name]) + ids = self.tokenizer.encode(text).ids + return ids + [self.eos_id] + + def next_sequence(self): + while len(self.buffer) < self.seq_len + 1: + self.buffer.extend(self._pull_one_doc_tokens()) + seq = self.buffer[:self.seq_len + 1] + self.buffer = self.buffer[self.seq_len:] + return seq + + def next_batch(self, batch_size: int): + return [self.next_sequence() for _ in range(batch_size)] +if __name__ == '__main__': + import time + from tokenizers import Tokenizer + tok = Tokenizer.from_file('artifacts/tokenizer.json') + print('eos id:', tok.token_to_id(EOS), flush=True) + streams = build_source_streams(reference_ngrams=set(), decon_enabled=False) + mixer = MixedTokenStream(tok, streams, seq_len=64) + t0 = time.time() + for i in range(5): + seq = mixer.next_sequence() + print(f'seq {i}: len={len(seq)} sample={seq[:10]}', flush=True) + print(f'5 sequences in {time.time() - t0:.2f}s', flush=True) diff --git a/eval_bench.py b/eval_bench.py new file mode 100644 index 0000000..15b8975 --- /dev/null +++ b/eval_bench.py @@ -0,0 +1,256 @@ +from __future__ import annotations +import argparse +import time +import torch +import torch.nn.functional as F +from tokenizers import Tokenizer +from model import LogosModel + +def infer_config(sd: dict) -> dict: + vocab_size, dim = sd['embed_tokens.weight'].shape + n_layers = 1 + max((int(k.split('.')[1]) for k in sd if k.startswith('layers.'))) + head_dim = sd['layers.0.attn.q_norm.weight'].shape[0] + n_heads = dim // head_dim + n_kv_heads = sd['layers.0.attn.k_proj.weight'].shape[0] // head_dim + mlp_hidden = sd['layers.0.mlp.gate_proj.weight'].shape[0] + return dict(vocab_size=vocab_size, dim=dim, n_layers=n_layers, n_heads=n_heads, n_kv_heads=n_kv_heads, mlp_hidden=mlp_hidden) + +def load_model(ckpt_path: str, device: str) -> LogosModel: + state = torch.load(ckpt_path, map_location='cpu') + sd = state['model'] if 'model' in state else state + cfg = infer_config(sd) + print(f'[eval] checkpoint architecture: {cfg}', flush=True) + model = LogosModel(**cfg) + model.load_state_dict(sd) + model.eval() + model.to(device) + return model + +class _Encoding: + + def __init__(self, ids): + self.ids = ids + +class _HFTokenizerAdapter: + + def __init__(self, hf_tok): + self.hf_tok = hf_tok + + def encode(self, text): + return _Encoding(self.hf_tok.encode(text, add_special_tokens=False)) + +class _HFModelAdapter: + + def __init__(self, hf_model): + self.hf_model = hf_model + + def eval(self): + self.hf_model.eval() + return self + + def to(self, device): + self.hf_model.to(device) + return self + + def __call__(self, input_ids): + return (self.hf_model(input_ids=input_ids).logits, None) + +def _join(context: str, choice: str) -> str: + if context and (not context[-1].isspace()) and choice and (not choice[0].isspace()): + return context + ' ' + choice + return context + choice + +@torch.no_grad() +def _score_batch(model, tok, device: str, pairs: list[tuple[str, str]]) -> list[tuple[float, int]]: + metas = [] + for context, choice in pairs: + ctx_ids = tok.encode(context).ids + whole_ids = tok.encode(_join(context, choice)).ids + if len(whole_ids) <= len(ctx_ids): + whole_ids = ctx_ids + tok.encode(choice).ids + metas.append((whole_ids, len(whole_ids) - len(ctx_ids))) + max_len = max((len(w) for w, _ in metas)) + batch_ids = torch.zeros((len(metas), max_len), dtype=torch.long) + for i, (whole_ids, _) in enumerate(metas): + batch_ids[i, :len(whole_ids)] = torch.tensor(whole_ids, dtype=torch.long) + batch_ids = batch_ids.to(device) + logits, _ = model(batch_ids) + logprobs = F.log_softmax(logits.float(), dim=-1) + scores = [] + for i, (whole_ids, cont_len) in enumerate(metas): + start = len(whole_ids) - cont_len + total = 0.0 + for j in range(cont_len): + pos = start - 1 + j + tid = whole_ids[start + j] + total += logprobs[i, pos, tid].item() + scores.append((total, max(cont_len, 1))) + return scores + +def batched_score(model, tok, device, pairs: list[tuple[str, str]], batch_size: int, tag: str='') -> list[tuple[float, int]]: + scores: list[tuple[float, int]] = [(0.0, 1)] * len(pairs) + n_batches = (len(pairs) + batch_size - 1) // batch_size + t0 = time.time() + for bi, start in enumerate(range(0, len(pairs), batch_size)): + chunk = pairs[start:start + batch_size] + chunk_scores = _score_batch(model, tok, device, chunk) + scores[start:start + len(chunk)] = chunk_scores + if tag and (bi % 10 == 0 or bi == n_batches - 1): + elapsed = time.time() - t0 + done = start + len(chunk) + rate = done / elapsed if elapsed > 0 else 0 + print(f'[eval] {tag}: {done}/{len(pairs)} pairs scored ({rate:.1f}/s, {elapsed:.0f}s elapsed)', flush=True) + return scores + +def _pick(ex_scores, normalized: bool) -> int: + if normalized: + return max(range(len(ex_scores)), key=lambda k: ex_scores[k][0] / ex_scores[k][1]) + return max(range(len(ex_scores)), key=lambda k: ex_scores[k][0]) + +def eval_hellaswag_style(model, tok, device, ds, ctx_key, endings_key, label_key, limit=None, batch_size=32, tag=''): + if limit: + ds = ds.select(range(min(limit, len(ds)))) + pairs, counts = ([], []) + for row in ds: + endings = row[endings_key] + for e in endings: + pairs.append((row[ctx_key], e)) + counts.append(len(endings)) + scores = batched_score(model, tok, device, pairs, batch_size, tag) + raw_correct, norm_correct, idx = (0, 0, 0) + for i, row in enumerate(ds): + n = counts[i] + ex_scores = scores[idx:idx + n] + idx += n + gold = int(row[label_key]) + raw_correct += int(_pick(ex_scores, False) == gold) + norm_correct += int(_pick(ex_scores, True) == gold) + return (norm_correct / len(ds), len(ds), raw_correct / len(ds)) + +def eval_arc(model, tok, device, config, limit=None, batch_size=32, tag=''): + from datasets import load_dataset + ds = load_dataset('allenai/ai2_arc', config, split='test') + if limit: + ds = ds.select(range(min(limit, len(ds)))) + kept_rows = [] + pairs, counts = ([], []) + for row in ds: + choices = row['choices']['text'] + labels = row['choices']['label'] + answer = row['answerKey'] + if answer not in labels or not choices: + continue + kept_rows.append((row, labels.index(answer))) + for c in choices: + pairs.append((row['question'], c)) + counts.append(len(choices)) + scores = batched_score(model, tok, device, pairs, batch_size, tag) + raw_correct, norm_correct, idx = (0, 0, 0) + for (row, gold_idx), n in zip(kept_rows, counts): + ex_scores = scores[idx:idx + n] + idx += n + raw_correct += int(_pick(ex_scores, False) == gold_idx) + norm_correct += int(_pick(ex_scores, True) == gold_idx) + return (norm_correct / len(kept_rows), len(kept_rows), raw_correct / len(kept_rows)) + +def eval_piqa(model, tok, device, limit=None, batch_size=32, tag=''): + from datasets import load_dataset + ds = load_dataset('ybisk/piqa', split='validation', revision='refs/convert/parquet') + if limit: + ds = ds.select(range(min(limit, len(ds)))) + pairs = [] + for row in ds: + pairs.append((row['goal'], row['sol1'])) + pairs.append((row['goal'], row['sol2'])) + scores = batched_score(model, tok, device, pairs, batch_size, tag) + raw_correct, norm_correct = (0, 0) + for i, row in enumerate(ds): + pair = [scores[2 * i], scores[2 * i + 1]] + gold = int(row['label']) + raw_correct += int(_pick(pair, False) == gold) + norm_correct += int(_pick(pair, True) == gold) + return (norm_correct / len(ds), len(ds), raw_correct / len(ds)) + +def N(score_pct: float, chance: float) -> float: + return 100 * (score_pct - chance) / (100 - chance) + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--ckpt', default=None) + parser.add_argument('--hf_model', default=None, help='if set, evaluate a HF transformers reference model instead of a LogosModel checkpoint -- used to calibrate the harness against a model with known published scores') + parser.add_argument('--device', default='cpu') + parser.add_argument('--threads', type=int, default=24) + parser.add_argument('--batch_size', type=int, default=32) + parser.add_argument('--limit', type=int, default=None, help='cap per-benchmark examples (debug/speed)') + args = parser.parse_args() + if not args.ckpt and (not args.hf_model): + parser.error('one of --ckpt or --hf_model is required') + torch.set_num_threads(args.threads) + if args.hf_model: + from transformers import AutoModelForCausalLM, AutoTokenizer + print(f'[eval] loading HF reference model {args.hf_model}...', flush=True) + tok = _HFTokenizerAdapter(AutoTokenizer.from_pretrained(args.hf_model)) + model = _HFModelAdapter(AutoModelForCausalLM.from_pretrained(args.hf_model)).eval().to(args.device) + ckpt_label = args.hf_model + else: + tok = Tokenizer.from_file('artifacts/tokenizer.json') + print(f'[eval] loading checkpoint {args.ckpt}...', flush=True) + model = load_model(args.ckpt, args.device) + ckpt_label = args.ckpt + results = {} + from datasets import load_dataset + t0 = time.time() + print('[eval] running HellaSwag...', flush=True) + hs_ds = load_dataset('Rowan/hellaswag', split='validation') + results['hellaswag'] = eval_hellaswag_style(model, tok, args.device, hs_ds, 'ctx', 'endings', 'label', args.limit, args.batch_size, tag='hellaswag') + print(f"[eval] HellaSwag acc_norm: {results['hellaswag'][0]:.4f} (n={results['hellaswag'][1]}, {time.time() - t0:.0f}s)", flush=True) + t0 = time.time() + print('[eval] running ARC-Easy...', flush=True) + results['arc_easy'] = eval_arc(model, tok, args.device, 'ARC-Easy', args.limit, args.batch_size, tag='arc_easy') + print(f"[eval] ARC-Easy acc_norm: {results['arc_easy'][0]:.4f} (n={results['arc_easy'][1]}, {time.time() - t0:.0f}s)", flush=True) + t0 = time.time() + print('[eval] running ARC-Challenge...', flush=True) + results['arc_challenge'] = eval_arc(model, tok, args.device, 'ARC-Challenge', args.limit, args.batch_size, tag='arc_challenge') + print(f"[eval] ARC-Challenge acc_norm: {results['arc_challenge'][0]:.4f} (n={results['arc_challenge'][1]}, {time.time() - t0:.0f}s)", flush=True) + t0 = time.time() + print('[eval] running PIQA...', flush=True) + results['piqa'] = eval_piqa(model, tok, args.device, args.limit, args.batch_size, tag='piqa') + print(f"[eval] PIQA acc_norm: {results['piqa'][0]:.4f} (n={results['piqa'][1]}, {time.time() - t0:.0f}s)", flush=True) + t0 = time.time() + print('[eval] running ArithMark-3.0...', flush=True) + am_ds = load_dataset('AxiomicLabs/ArithMark-3.0', split='train') + results['arithmark3'] = eval_hellaswag_style(model, tok, args.device, am_ds, 'ctx', 'endings', 'label', args.limit, args.batch_size, tag='arithmark3') + print(f"[eval] ArithMark-3.0 acc_norm: {results['arithmark3'][0]:.4f} (n={results['arithmark3'][1]}, {time.time() - t0:.0f}s)", flush=True) + + def index_from(hs, arc_e, arc_c, piqa, am): + return (N(hs * 100, 25) + N((arc_e + arc_c) / 2 * 100, 25) + N(piqa * 100, 50) + 0.65 * N(am * 100, 25)) / 3.65 + print('', flush=True) + print('[eval] ==== acc vs acc_norm ====', flush=True) + print(f"[eval] {'task':<16}{'acc':>9}{'acc_norm':>11}", flush=True) + for key, label in [('hellaswag', 'HellaSwag'), ('arc_easy', 'ARC-Easy'), ('arc_challenge', 'ARC-Chall'), ('piqa', 'PIQA'), ('arithmark3', 'ArithMark-3')]: + norm, _n, raw = results[key] + print(f'[eval] {label:<16}{raw * 100:>8.2f}%{norm * 100:>10.2f}%', flush=True) + idx_raw = index_from(results['hellaswag'][2], results['arc_easy'][2], results['arc_challenge'][2], results['piqa'][2], results['arithmark3'][2]) + idx_norm = index_from(results['hellaswag'][0], results['arc_easy'][0], results['arc_challenge'][0], results['piqa'][0], results['arithmark3'][0]) + print(f'[eval] Index from acc: {idx_raw:.2f}', flush=True) + print(f'[eval] Index from acc_norm: {idx_norm:.2f}', flush=True) + hs_acc, hs_n = (results['hellaswag'][0], results['hellaswag'][1]) + e_acc, e_n = (results['arc_easy'][0], results['arc_easy'][1]) + c_acc, c_n = (results['arc_challenge'][0], results['arc_challenge'][1]) + piqa_acc, piqa_n = (results['piqa'][0], results['piqa'][1]) + am_acc, am_n = (results['arithmark3'][0], results['arithmark3'][1]) + combined_arc = (e_acc + c_acc) / 2 + idx = (N(hs_acc * 100, 25) + N(combined_arc * 100, 25) + N(piqa_acc * 100, 50) + 0.65 * N(am_acc * 100, 25)) / 3.65 + print('', flush=True) + print('[eval] ==== SUMMARY ====', flush=True) + print(f'[eval] checkpoint: {ckpt_label}', flush=True) + print(f'[eval] HellaSwag: {hs_acc * 100:.2f}% (n={hs_n})', flush=True) + print(f'[eval] ARC-Easy: {e_acc * 100:.2f}% (n={e_n})', flush=True) + print(f'[eval] ARC-Challenge: {c_acc * 100:.2f}% (n={c_n})', flush=True) + print(f'[eval] Combined ARC: {combined_arc * 100:.2f}%', flush=True) + print(f'[eval] PIQA: {piqa_acc * 100:.2f}% (n={piqa_n})', flush=True) + print(f'[eval] ArithMark-3.0: {am_acc * 100:.2f}% (n={am_n})', flush=True) + print(f'[eval] Intelligence Index (approx): {idx:.2f}', flush=True) + print(f'[eval] target (GPT-X2.5-135M): 25.17', flush=True) +if __name__ == '__main__': + main() diff --git 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b/model.py @@ -0,0 +1,170 @@ +from __future__ import annotations +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + +class RMSNorm(nn.Module): + + def __init__(self, dim, eps=1e-06): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def forward(self, x): + dtype = x.dtype + x = x.float() + x = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + return x.to(dtype) * self.weight + +def rope_freqs(seq_len, head_dim, theta=100000.0, device=None): + inv_freq = 1.0 / theta ** (torch.arange(0, head_dim, 2, device=device).float() / head_dim) + t = torch.arange(seq_len, device=device).float() + freqs = torch.outer(t, inv_freq) + return torch.cat([freqs, freqs], dim=-1) + +def rotate_half(x): + x1, x2 = x.chunk(2, dim=-1) + return torch.cat([-x2, x1], dim=-1) + +def apply_rope(x, cos, sin): + return x * cos + rotate_half(x) * sin + +class Attention(nn.Module): + + def __init__(self, dim, n_heads, n_kv_heads, head_dim, qk_norm_eps=1e-06): + super().__init__() + if n_heads % n_kv_heads != 0: + raise ValueError(f'n_heads ({n_heads}) must be divisible by n_kv_heads ({n_kv_heads}); valid choices for {n_heads} heads: {[k for k in range(1, n_heads + 1) if n_heads % k == 0]}') + self.n_heads = n_heads + self.n_kv_heads = n_kv_heads + self.head_dim = head_dim + self.n_rep = n_heads // n_kv_heads + self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=False) + self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False) + self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False) + self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False) + self.q_norm = RMSNorm(head_dim, eps=qk_norm_eps) + self.k_norm = RMSNorm(head_dim, eps=qk_norm_eps) + + def forward(self, x, cos, sin): + B, T, _ = x.shape + q = self.q_proj(x).view(B, T, self.n_heads, self.head_dim) + k = self.k_proj(x).view(B, T, self.n_kv_heads, self.head_dim) + v = self.v_proj(x).view(B, T, self.n_kv_heads, self.head_dim) + q = self.q_norm(q) + k = self.k_norm(k) + q = apply_rope(q, cos, sin) + k = apply_rope(k, cos, sin) + q = q.transpose(1, 2) + k = k.transpose(1, 2).repeat_interleave(self.n_rep, dim=1) + v = v.transpose(1, 2).repeat_interleave(self.n_rep, dim=1) + out = F.scaled_dot_product_attention(q, k, v, is_causal=True) + out = out.transpose(1, 2).contiguous().view(B, T, -1) + return self.o_proj(out) + +class SwiGLU(nn.Module): + + def __init__(self, dim, hidden): + super().__init__() + self.gate_proj = nn.Linear(dim, hidden, bias=False) + self.up_proj = nn.Linear(dim, hidden, bias=False) + self.down_proj = nn.Linear(hidden, dim, bias=False) + + def forward(self, x): + return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x)) + +class DecoderLayer(nn.Module): + + def __init__(self, dim, n_heads, n_kv_heads, head_dim, mlp_hidden, norm_eps=1e-06): + super().__init__() + self.attn_norm = RMSNorm(dim, eps=norm_eps) + self.attn = Attention(dim, n_heads, n_kv_heads, head_dim) + self.mlp_norm = RMSNorm(dim, eps=norm_eps) + self.mlp = SwiGLU(dim, mlp_hidden) + + def forward(self, x, cos, sin): + x = x + self.attn(self.attn_norm(x), cos, sin) + x = x + self.mlp(self.mlp_norm(x)) + return x + +class LogosModel(nn.Module): + + def __init__(self, vocab_size=32768, dim=640, n_layers=30, n_heads=10, n_kv_heads=5, mlp_hidden=1728, max_seq_len=4096, rope_theta=100000.0, norm_eps=1e-06, tie_embeddings=True): + super().__init__() + self.dim = dim + self.n_layers = n_layers + self.head_dim = dim // n_heads + self.max_seq_len = max_seq_len + self.rope_theta = rope_theta + self.embed_tokens = nn.Embedding(vocab_size, dim) + self.layers = nn.ModuleList([DecoderLayer(dim, n_heads, n_kv_heads, self.head_dim, mlp_hidden, norm_eps) for _ in range(n_layers)]) + self.norm_out = RMSNorm(dim, eps=norm_eps) + self.lm_head = nn.Linear(dim, vocab_size, bias=False) + if tie_embeddings: + self.lm_head.weight = self.embed_tokens.weight + self.apply(self._init_weights) + for layer in self.layers: + nn.init.normal_(layer.mlp.down_proj.weight, mean=0.0, std=0.02 / math.sqrt(2 * n_layers)) + nn.init.normal_(layer.attn.o_proj.weight, mean=0.0, std=0.02 / math.sqrt(2 * n_layers)) + cos, sin = self._build_rope_cache(max_seq_len) + self.register_buffer('rope_cos', cos, persistent=False) + self.register_buffer('rope_sin', sin, persistent=False) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + nn.init.normal_(m.weight, mean=0.0, std=0.02) + if m.bias is not None: + nn.init.zeros_(m.bias) + elif isinstance(m, nn.Embedding): + nn.init.normal_(m.weight, mean=0.0, std=0.02) + + def _build_rope_cache(self, seq_len): + freqs = rope_freqs(seq_len, self.head_dim, self.rope_theta) + return (freqs.cos()[None, :, None, :], freqs.sin()[None, :, None, :]) + + def forward(self, input_ids, labels=None, loss_chunk_size=2048): + B, T = input_ids.shape + x = self.embed_tokens(input_ids) + cos = self.rope_cos[:, :T].to(x.dtype) + sin = self.rope_sin[:, :T].to(x.dtype) + for layer in self.layers: + x = layer(x, cos, sin) + x = self.norm_out(x) + if labels is None: + return (self.lm_head(x), None) + shift_x = x[:, :-1].reshape(-1, x.size(-1)) + shift_labels = labels[:, 1:].reshape(-1) + n = shift_x.size(0) + total_loss = x.new_zeros((), dtype=torch.float32) + total_count = x.new_zeros((), dtype=torch.float32) + for start in range(0, n, loss_chunk_size): + end = min(start + loss_chunk_size, n) + chunk_labels = shift_labels[start:end] + valid = chunk_labels != -100 + count = valid.sum() + if count == 0: + continue + chunk_logits = self.lm_head(shift_x[start:end]).float() + chunk_loss = F.cross_entropy(chunk_logits, chunk_labels, ignore_index=-100, reduction='sum') + total_loss = total_loss + chunk_loss + total_count = total_count + count + loss = total_loss / total_count.clamp(min=1) + return (None, loss) + + def num_params(self, exclude_embeddings=False): + n = sum((p.numel() for p in self.parameters())) + if exclude_embeddings: + n -= self.embed_tokens.weight.numel() + if self.lm_head.weight is not self.embed_tokens.weight: + n -= self.lm_head.weight.numel() + return n +if __name__ == '__main__': + m = LogosModel() + print(f'total params: {m.num_params():,}') + print(f'non-embedding params: {m.num_params(exclude_embeddings=True):,}') + x = torch.randint(0, 32768, (2, 128)) + logits, loss = m(x, labels=x) + print('loss:', loss.item() if loss is not None else None) + logits, _ = m(x) + print('logits shape (no labels):', logits.shape) diff --git a/model.safetensors b/model.safetensors new file mode 100644 index 0000000..c222051 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8ba65c6de38014ad12a773ad2dea43d221579e1c958b6d57c899ae809031448d +size 629681616 diff --git a/pretrain_logos.py b/pretrain_logos.py new file mode 100644 index 0000000..5de2c65 --- /dev/null +++ b/pretrain_logos.py @@ -0,0 +1,344 @@ +from __future__ import annotations +import argparse +import contextlib +import datetime +import math +import os +import queue +import threading +import time +import torch +import torch.distributed as dist +from torch.nn.parallel import DistributedDataParallel as DDP +from tokenizers import Tokenizer +from model import LogosModel +from adamuon import build_optimizer, CombinedOptimizer +from decon import build_reference_ngrams, load_eval_reference_texts +from data_pipeline import build_source_streams, build_preset_streams, MixedTokenStream +SEQ_LEN = 1024 +BATCH_SIZE = 96 +MUON_LR = 0.02 +ADAM_LR = 0.0003 +WEIGHT_DECAY = 0.1 +WARMUP_STEPS = 2000 +GRAD_CLIP = 1.0 +ADAMW_BASELINE_PEAK_LR = 0.0006 + +class PlainAdamWOptimizer: + + def __init__(self, model, lr: float, weight_decay: float): + self.opt = torch.optim.AdamW(model.parameters(), lr=lr, betas=(0.9, 0.95), weight_decay=weight_decay) + + def zero_grad(self): + self.opt.zero_grad() + + def step(self): + self.opt.step() + + def set_lr(self, lr: float): + for g in self.opt.param_groups: + g['lr'] = lr + + def state_dict(self): + return self.opt.state_dict() + + def load_state_dict(self, sd): + self.opt.load_state_dict(sd) + +def cosine_schedule(step: int, total_steps: int, warmup_steps: int, min_ratio: float=0.1) -> float: + if step < warmup_steps: + return step / max(1, warmup_steps) + progress = (step - warmup_steps) / max(1, total_steps - warmup_steps) + progress = min(progress, 1.0) + cosine = 0.5 * (1 + math.cos(math.pi * progress)) + return min_ratio + (1 - min_ratio) * cosine + +def is_distributed() -> bool: + return 'WORLD_SIZE' in os.environ and int(os.environ['WORLD_SIZE']) > 1 + +def _worker_loop(out_q, stop_ev, progress_val, cfg): + import numpy as np + from tokenizers import Tokenizer as _Tok + from data_pipeline import build_source_streams as _build, MixedTokenStream as _Mix + tok = _Tok.from_file(cfg['tokenizer_path']) + if cfg['decon']: + from decon import build_reference_ngrams, load_eval_reference_texts + ref = build_reference_ngrams(load_eval_reference_texts()) + else: + ref = set() + if cfg.get('preset'): + from data_pipeline import build_preset_streams as _bp + streams = _bp(cfg['preset'], ref, decon_enabled=cfg['decon'], rank=cfg['rank'], world_size=cfg['world_size']) + else: + streams = _build(ref, decon_enabled=cfg['decon'], rank=cfg['rank'], world_size=cfg['world_size'], mix_mode=cfg['data_mix'], arith_source=cfg['arith_source']) + mixer = _Mix(tok, streams, seq_len=cfg['seq_len'], seed=cfg['seed'], mix_mode=cfg['data_mix'], arith_frac=cfg['arith_frac'], preset=cfg.get('preset')) + while not stop_ev.is_set(): + mixer.progress = progress_val.value + batch = np.asarray(mixer.next_batch(cfg['batch_size']), dtype=np.int64) + while not stop_ev.is_set(): + try: + out_q.put(batch, timeout=1.0) + break + except Exception: + continue + +class ProcessBatchPrefetcher: + + def __init__(self, cfg, n_workers: int, queue_depth: int=6): + import multiprocessing as mp + self.ctx = mp.get_context('spawn') + self.q = self.ctx.Queue(maxsize=queue_depth) + self.stop_ev = self.ctx.Event() + self.progress = self.ctx.Value('d', 0.0) + self.procs = [] + for i in range(n_workers): + wcfg = dict(cfg) + wcfg['seed'] = cfg['seed'] * 1000 + i + wcfg['rank'] = cfg['rank'] * n_workers + i + wcfg['world_size'] = cfg['world_size'] * n_workers + p = self.ctx.Process(target=_worker_loop, args=(self.q, self.stop_ev, self.progress, wcfg), daemon=True) + p.start() + self.procs.append(p) + + def set_progress(self, value: float): + self.progress.value = value + + def get(self): + return self.q.get() + + def stop(self): + self.stop_ev.set() + for p in self.procs: + p.join(timeout=10) + if p.is_alive(): + p.terminate() + +class BatchPrefetcher: + + def __init__(self, mixer, batch_size: int, queue_depth: int=4): + self.mixer = mixer + self.batch_size = batch_size + self.q: queue.Queue = queue.Queue(maxsize=queue_depth) + self._stop = threading.Event() + self._thread = threading.Thread(target=self._run, daemon=True) + + def start(self) -> 'BatchPrefetcher': + self._thread.start() + return self + + def _run(self): + while not self._stop.is_set(): + self.q.put(self.mixer.next_batch(self.batch_size)) + + def get(self): + return self.q.get() + + def stop(self): + self._stop.set() + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--total_tokens', type=float, default=22000000000.0) + parser.add_argument('--ckpt_dir', type=str, default='/workspace/ckpt') + parser.add_argument('--ckpt_every', type=int, default=5000) + parser.add_argument('--log_every', type=int, default=20) + parser.add_argument('--resume', action='store_true') + parser.add_argument('--decon', action='store_true', default=True) + parser.add_argument('--no-decon', dest='decon', action='store_false') + parser.add_argument('--smoke_steps', type=int, default=0, help='if >0, stop after this many steps (trial run)') + parser.add_argument('--dim', type=int, default=640) + parser.add_argument('--n_layers', type=int, default=30) + parser.add_argument('--n_heads', type=int, default=10) + parser.add_argument('--mlp_hidden', type=int, default=1728) + parser.add_argument('--n_kv_heads', type=int, default=5, help='must divide n_heads; = n_heads for full MHA') + parser.add_argument('--optimizer', choices=['adamuon', 'adamw'], default='adamw', help='adamuon is retained only for reproducing the earlier ablation') + parser.add_argument('--lr_min_ratio', type=float, default=0.1, help='floor of the cosine as a fraction of peak. 0.0 decays to zero, which is what a cooldown phase wants; the 0.1 default is for the main run. Note the floor multiplies --lr, so on a resumed run near 100%% progress the effective LR is --lr * lr_min_ratio.') + parser.add_argument('--lr', type=float, default=ADAMW_BASELINE_PEAK_LR, help='AdamW peak LR. 6e-4 matches Museko-125M; BananaMind-2-Pro used 1.5e-3 at this scale') + parser.add_argument('--arith_source', choices=['wordmath', 'drill', 'mixed'], default='mixed', help="wordmath = big-endian grade-school word problems (matches ArithMark-3's format); drill = the old reversed little-endian digit drills, kept for the A/B ablation") + parser.add_argument('--grad_accum', type=int, default=1, help='micro-batches per optimizer step; allreduce runs only on the last one') + parser.add_argument('--prefetch', choices=['process', 'thread'], default='process', help='process = data pipeline in worker processes (escapes the GIL); thread = the old single-thread prefetcher, kept for comparison') + parser.add_argument('--workers', type=int, default=3, help='data worker processes per rank') + parser.add_argument('--arith_frac', type=float, default=None, help='pin the arithmetic slice to this fraction of the mix instead of using the stage ramp -- used to concentrate signal in short ablation runs') + parser.add_argument('--warmup_steps', type=int, default=WARMUP_STEPS, help='override for short ablation runs where the default 2000 would eat the whole run') + parser.add_argument('--batch_size', type=int, default=BATCH_SIZE, help='override the module default, which was tuned for a 96GB card, not smaller ones') + parser.add_argument('--data_dir', default=None, help='read pre-tokenised uint16 shards from here instead of streaming. Streaming sustained ~520k tok/s against an 843k compute ceiling and kept tripping HF rate limits; a memmap has neither problem.') + parser.add_argument('--preset', choices=['smollm2', 'dclm_heavy', 'smollm2_math', 'dclm_heavy_math', 'cooldown'], default=None, help="single-stage data preset; overrides the staged curriculum entirely. The staged mix scored Index 11.73 at 9.14B (below GPT-2's 13.58) because 97%% of the gap to GPT-X2.5 sits in HellaSwag/PIQA/ARC, which need web text, not the code/arithmetic/dialogue the curriculum was spending tokens on.") + parser.add_argument('--data_mix', choices=['default', 'museko'], default='default', help="museko = flat web+10%% arithmetic only, matching Museko-125M's recipe, for data-mix ablation") + args = parser.parse_args() + world_size = int(os.environ.get('WORLD_SIZE', 1)) + rank = int(os.environ.get('RANK', 0)) + local_rank = int(os.environ.get('LOCAL_RANK', 0)) + ddp = is_distributed() + is_main = rank == 0 + if ddp: + dist.init_process_group(backend='nccl', timeout=datetime.timedelta(minutes=90)) + torch.cuda.set_device(local_rank) + device = f'cuda:{local_rank}' + else: + device = 'cuda' + + def log(msg): + if is_main: + print(msg, flush=True) + if is_main: + os.makedirs(args.ckpt_dir, exist_ok=True) + tokens_per_step_global = args.batch_size * SEQ_LEN * world_size * args.grad_accum + total_steps = int(args.total_tokens // tokens_per_step_global) + log(f'[train] world_size={world_size} total_steps={total_steps:,} tokens_per_step_global={tokens_per_step_global:,} target_tokens={args.total_tokens:,.0f}') + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + log('[train] loading tokenizer...') + tok = Tokenizer.from_file('artifacts/tokenizer.json') + if args.decon: + log('[train] building decontamination reference set (every rank builds its own copy -- eval sets are small, cheap enough to not bother broadcasting)...') + ref_texts = load_eval_reference_texts() + reference_ngrams = build_reference_ngrams(ref_texts) + log(f'[train] decon reference: {len(reference_ngrams):,} n-grams from {len(ref_texts):,} eval fields') + else: + reference_ngrams = set() + log('[train] decontamination DISABLED (--no-decon)') + log(f'[train] building + prewarming data streams for rank {rank}/{world_size} (cold-start latency on first connection per source, one-time cost)...') + if args.preset: + streams = build_preset_streams(args.preset, reference_ngrams, decon_enabled=args.decon, rank=rank, world_size=world_size) + else: + streams = build_source_streams(reference_ngrams, decon_enabled=args.decon, rank=rank, world_size=world_size, mix_mode=args.data_mix, arith_source=args.arith_source) + mixer = MixedTokenStream(tok, streams, seq_len=SEQ_LEN, seed=rank, mix_mode=args.data_mix, arith_frac=args.arith_frac, preset=args.preset) + if args.prefetch == 'process': + log('[train] skipping in-process prewarm (workers own the streams)') + else: + for name, gen in streams.items(): + t0 = time.time() + try: + _ = next(gen) + except StopIteration: + pass + if is_main: + print(f"[train] prewarmed '{name}' in {time.time() - t0:.1f}s", flush=True) + if ddp: + dist.barrier() + log(f'[train] building model (dim={args.dim} layers={args.n_layers} heads={args.n_heads} kv_heads={args.n_kv_heads} mlp={args.mlp_hidden}, optimizer={args.optimizer}, arith_source={args.arith_source} arith_frac={args.arith_frac})...') + model = LogosModel(dim=args.dim, n_layers=args.n_layers, n_heads=args.n_heads, n_kv_heads=args.n_kv_heads, mlp_hidden=args.mlp_hidden).to(device).to(torch.bfloat16) + log(f'[train] params: {model.num_params():,} (non-embedding {model.num_params(exclude_embeddings=True):,})') + if args.optimizer == 'adamw': + opt = PlainAdamWOptimizer(model, lr=args.lr, weight_decay=WEIGHT_DECAY) + else: + muon_opt, adam_opt = build_optimizer(model, muon_lr=MUON_LR, adam_lr=ADAM_LR, weight_decay=WEIGHT_DECAY) + opt = CombinedOptimizer(muon_opt, adam_opt) + model_ddp = None + if ddp: + model_ddp = DDP(model, device_ids=[local_rank]) + model_c = torch.compile(model_ddp) + else: + model_c = torch.compile(model) + start_step = 0 + ckpt_path = os.path.join(args.ckpt_dir, 'latest.pt') + if args.resume and os.path.exists(ckpt_path): + log(f'[train] resuming from {ckpt_path}') + state = torch.load(ckpt_path, map_location=device) + model.load_state_dict(state['model']) + opt.load_state_dict(state['opt']) + mixer.buffer = state.get('mixer_buffer', []) + if 'tokens_done' in state: + tokens_done = state['tokens_done'] + else: + tps = state.get('tokens_per_step', tokens_per_step_global) + tokens_done = state['step'] * tps + log(f'[train] old-format checkpoint, inferred tokens from step x {tps:,}') + start_step = int(tokens_done // tokens_per_step_global) + log(f'[train] resumed at {tokens_done / 1000000000.0:.3f}B tokens -> step {start_step:,}/{total_steps:,} (this config: {tokens_per_step_global:,} tokens/step)') + if state.get('tokens_per_step') not in (None, tokens_per_step_global): + log(f"[train] NOTE: checkpoint was written at {state['tokens_per_step']:,} tokens/step; rescaled cleanly to the current geometry") + mixer.progress = start_step / total_steps + if args.data_dir: + from bin_data import BinaryMixStream + from data_pipeline import MIX_PRESETS + binstream = BinaryMixStream(args.data_dir, MIX_PRESETS[args.preset], seq_len=SEQ_LEN, seed=rank + 1) + if is_main: + for k, v in binstream.token_counts().items(): + log(f'[train] {k}: {v:,} tokens on disk') + prefetcher = None + elif args.prefetch == 'process': + log(f'[train] starting {args.workers} data worker processes (each does its own cold-start, one-time)...') + prefetcher = ProcessBatchPrefetcher(dict(tokenizer_path=os.path.abspath('artifacts/tokenizer.json'), decon=args.decon, rank=rank, world_size=world_size, data_mix=args.data_mix, arith_source=args.arith_source, arith_frac=args.arith_frac, seq_len=SEQ_LEN, batch_size=args.batch_size, seed=rank + 1, preset=args.preset), n_workers=args.workers) + prefetcher.set_progress(start_step / max(1, total_steps)) + del streams + else: + prefetcher = BatchPrefetcher(mixer, args.batch_size).start() + first_batch = None + if args.data_dir: + first_batch = binstream.next_batch(args.batch_size) + else: + t_warm = time.time() + first_batch = prefetcher.get() + log(f'[train] first batch ready after {time.time() - t_warm:.0f}s') + if ddp: + dist.barrier() + log('[train] all ranks have data, starting') + running_loss = 0.0 + running_count = 0 + t_last = time.time() + tok_since_log = 0 + for step in range(start_step, total_steps): + if args.data_dir: + pass + elif args.prefetch == 'process': + prefetcher.set_progress(step / total_steps) + else: + mixer.progress = step / total_steps + lr_scale = cosine_schedule(step, total_steps, args.warmup_steps, args.lr_min_ratio) + if args.optimizer == 'adamw': + opt.set_lr(args.lr * lr_scale) + else: + opt.set_lr(MUON_LR * lr_scale, ADAM_LR * lr_scale) + opt.zero_grad() + loss_val = None + for micro in range(args.grad_accum): + if first_batch is not None: + batch, first_batch = (first_batch, None) + else: + batch = binstream.next_batch(args.batch_size) if args.data_dir else prefetcher.get() + if isinstance(batch, list): + x = torch.tensor(batch, dtype=torch.long, device=device) + else: + x = torch.from_numpy(batch).to(device, non_blocking=True) + is_last = micro == args.grad_accum - 1 + sync_ctx = contextlib.nullcontext() if model_ddp is None or is_last else model_ddp.no_sync() + with sync_ctx: + _, loss = model_c(x, labels=x) + (loss / args.grad_accum).backward() + loss_val = loss.detach() if loss_val is None else loss_val + loss.detach() + loss_val = loss_val / args.grad_accum + torch.nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP) + opt.step() + if ddp: + dist.all_reduce(loss_val, op=dist.ReduceOp.AVG) + running_loss += loss_val.item() + running_count += 1 + tok_since_log += tokens_per_step_global + if step % args.log_every == 0: + dt = time.time() - t_last + tok_per_sec = tok_since_log / dt if dt > 0 else 0.0 + avg_loss = running_loss / max(1, running_count) + eta_hours = (total_steps - step) * tokens_per_step_global / max(tok_per_sec, 1) / 3600 + log(f'[train] step {step:,}/{total_steps:,} loss={avg_loss:.4f} lr_scale={lr_scale:.3f} tok/s={tok_per_sec:,.0f} eta={eta_hours:.1f}h stage_progress={mixer.progress:.3f}') + running_loss = 0.0 + running_count = 0 + t_last = time.time() + tok_since_log = 0 + if is_main and step > 0 and (step % args.ckpt_every == 0): + meta = {'step': step, 'tokens_done': (step + 1) * tokens_per_step_global, 'tokens_per_step': tokens_per_step_global, 'total_tokens': args.total_tokens, 'world_size': world_size, 'batch_size': args.batch_size, 'grad_accum': args.grad_accum} + torch.save({'model': model.state_dict(), 'opt': opt.state_dict(), 'mixer_buffer': mixer.buffer, **meta}, ckpt_path) + torch.save({'model': model.state_dict(), **meta}, os.path.join(args.ckpt_dir, f'step{step}.pt')) + log(f'[train] checkpoint saved at step {step:,}') + if args.smoke_steps and step - start_step >= args.smoke_steps: + log(f'[train] smoke test: stopping after {args.smoke_steps} steps') + break + if prefetcher is not None: + prefetcher.stop() + if is_main: + torch.save({'model': model.state_dict(), 'opt': opt.state_dict(), 'step': step, 'tokens_done': (step + 1) * tokens_per_step_global, 'tokens_per_step': tokens_per_step_global, 'total_tokens': args.total_tokens, 'world_size': world_size, 'batch_size': args.batch_size, 'grad_accum': args.grad_accum, 'mixer_buffer': mixer.buffer}, ckpt_path) + log('[train] done') + if ddp: + dist.destroy_process_group() +if __name__ == '__main__': + main() diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000..79d62f0 --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,162902 @@ +{ + "version": "1.0", + "truncation": null, + "padding": null, + "added_tokens": [ + { + "id": 0, + "content": "<|endoftext|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 1, + "content": "<|pad|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 2, + "content": "<|user|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 3, + "content": "<|assistant|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 4, + "content": "<|system|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 5, + "content": "<|think|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 6, + "content": "<|/think|>", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + } + ], + "normalizer": { + "type": "NFC" + }, + "pre_tokenizer": { + "type": "Sequence", + "pretokenizers": [ + { + "type": "Digits", + "individual_digits": true + }, + { + "type": "ByteLevel", + "add_prefix_space": false, + "trim_offsets": true, + "use_regex": true + } + ] + }, + "post_processor": { + "type": "ByteLevel", + "add_prefix_space": true, + "trim_offsets": false, + "use_regex": true + }, + "decoder": { + "type": "ByteLevel", + "add_prefix_space": true, + "trim_offsets": true, + "use_regex": true + }, + "model": { + "type": "BPE", + "dropout": null, + "unk_token": null, + "continuing_subword_prefix": null, + "end_of_word_suffix": null, + "fuse_unk": false, + "byte_fallback": true, + "ignore_merges": false, + "vocab": { + "<|endoftext|>": 0, + "<|pad|>": 1, + "<|user|>": 2, + "<|assistant|>": 3, + "<|system|>": 4, + "<|think|>": 5, + "<|/think|>": 6, + "!": 7, + "\"": 8, + "#": 9, + "$": 10, + "%": 11, + "&": 12, + "'": 13, + "(": 14, + ")": 15, + "*": 16, + "+": 17, + ",": 18, + "-": 19, + ".": 20, + "/": 21, + "0": 22, + "1": 23, + "2": 24, + "3": 25, + "4": 26, + "5": 27, + "6": 28, + "7": 29, + "8": 30, + "9": 31, + ":": 32, + ";": 33, + "<": 34, + "=": 35, + ">": 36, + "?": 37, + "@": 38, + "A": 39, + "B": 40, + "C": 41, + "D": 42, + "E": 43, + "F": 44, + "G": 45, + "H": 46, + "I": 47, + "J": 48, + "K": 49, + "L": 50, 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"ön": 17680, + "Ġmol": 17681, + "Ġunch": 17682, + "dependent": 17683, + "Ġassumes": 17684, + "Ġfossils": 17685, + "Ġwives": 17686, + "techn": 17687, + "Defunct": 17688, + "ĠGardens": 17689, + "taining": 17690, + "news": 17691, + "ĠChes": 17692, + "Ġdeclining": 17693, + "posted": 17694, + "Ġpropose": 17695, + "Ġdelicate": 17696, + "Ġlifting": 17697, + "Ġstretched": 17698, + "Northern": 17699, + "ĠLif": 17700, + "Ġsupervision": 17701, + "Ġlinguistic": 17702, + "ĠMik": 17703, + "nthesis": 17704, + "Ġlazy": 17705, + "ĠGabriel": 17706, + "ĠGR": 17707, + "Dev": 17708, + "Number": 17709, + "Press": 17710, + "mile": 17711, + "ĠGrove": 17712, + "Ġpean": 17713, + "hart": 17714, + "ugging": 17715, + "ĠLinda": 17716, + "Ġexterior": 17717, + "imo": 17718, + "Ġduct": 17719, + "Ġhollow": 17720, + "ĠCer": 17721, + "ĠSimple": 17722, + "Ġexploitation": 17723, + "helm": 17724, + "Ġunfortunate": 17725, + "boro": 17726, + "Business": 17727, + "ĠSave": 17728, + "amel": 17729, + "Ġkg": 17730, + 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27199, + "ĠKC": 27200, + "Ġsmoked": 27201, + "Ġundertook": 27202, + "directed": 27203, + "Ġcohort": 27204, + "miah": 27205, + "older": 27206, + "ĠIdentity": 27207, + "Ġpromoter": 27208, + "ĠWear": 27209, + "ĠLaser": 27210, + "ĠEvangelical": 27211, + "Ġpronounce": 27212, + "Ġinjections": 27213, + "Ġawaiting": 27214, + "Gre": 27215, + "rocy": 27216, + "Ġunload": 27217, + "Ġrepent": 27218, + "Ġdenies": 27219, + "Ġrelaxing": 27220, + "Ġmicroscopy": 27221, + "Lady": 27222, + "Physical": 27223, + "##": 27224, + "Band": 27225, + "ĠWen": 27226, + "Ġmanure": 27227, + "Ġtaller": 27228, + "utan": 27229, + "ĠGian": 27230, + "Ġagreeing": 27231, + "Ġdetectors": 27232, + "ĠDarkness": 27233, + "ĠPé": 27234, + "ĠRajas": 27235, + "Golden": 27236, + "Ġtestify": 27237, + "Ġreviewer": 27238, + "Ġreformed": 27239, + "ĠUnix": 27240, + "Ġconcise": 27241, + "recogn": 27242, + "Tourist": 27243, + "Ġinvites": 27244, + "ĠSouthampton": 27245, + "ĠBluetooth": 27246, + "×Ķ": 27247, + "Ġsi": 27248, + "ĠCron": 27249, 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"porter": 27402, + "ĠGuadal": 27403, + "ĠHarlem": 27404, + "evo": 27405, + "Ġsquadrons": 27406, + "tel": 27407, + "atism": 27408, + "ĠTort": 27409, + "ĠSere": 27410, + "ĠSag": 27411, + "Ġsolvent": 27412, + "aterials": 27413, + "ĊĊĊĊĊĊĊ": 27414, + "VN": 27415, + "enton": 27416, + "ulls": 27417, + "Ġfearful": 27418, + "ĠLCD": 27419, + "AMA": 27420, + "Ġcomfortably": 27421, + "Ġprairie": 27422, + "Ġforecasts": 27423, + "Ġdeterioration": 27424, + "Ġcaregivers": 27425, + "Ġacclaim": 27426, + "Ġideally": 27427, + "development": 27428, + "ĠApproximately": 27429, + "ĠBelieve": 27430, + "ĠOlder": 27431, + "Ġintolerance": 27432, + "FK": 27433, + "Ġsegreg": 27434, + "ĠPhilharm": 27435, + "Ġsilently": 27436, + "Ġpreferably": 27437, + "Ġendeavor": 27438, + "ozy": 27439, + "inished": 27440, + "Ġprinces": 27441, + "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ": 27442, + "ĠExhibition": 27443, + "ĠAutism": 27444, + "Ġconsolidation": 27445, + "ĠLC": 27446, + "Ġfamed": 27447, + "Ġtart": 27448, + "Ġleaked": 27449, + 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"Ġregulator": 27499, + "omorphic": 27500, + "Ġfür": 27501, + "ĠChanging": 27502, + "Ġlocking": 27503, + "Democratic": 27504, + "ĠCelsius": 27505, + "verbs": 27506, + "ĠMog": 27507, + "ĠDeer": 27508, + "ĠFranken": 27509, + "Ġmisses": 27510, + "ĠAnniversary": 27511, + "Ġreplica": 27512, + "othschild": 27513, + "fram": 27514, + "itant": 27515, + "ĠSlam": 27516, + "Ġtelegraph": 27517, + "Ġdepended": 27518, + "Ġheadquartered": 27519, + "Ġfren": 27520, + "Ġdizz": 27521, + "Ġabol": 27522, + "Ġlineback": 27523, + "sponsored": 27524, + "Ġbarbar": 27525, + "Ġfaulty": 27526, + "Ġattent": 27527, + "Ġpicnic": 27528, + "recorded": 27529, + "Ġcompose": 27530, + "Ġenslaved": 27531, + "Protected": 27532, + "ĠCarpenter": 27533, + "ocamp": 27534, + "Ġcirca": 27535, + "ĠKeeping": 27536, + "ĠForever": 27537, + "oshop": 27538, + "Ġintends": 27539, + "ĠMozart": 27540, + "Ġmund": 27541, + "ĠLAN": 27542, + "Manager": 27543, + "ĠFirefox": 27544, + "Ġforensic": 27545, + "Ġforfe": 27546, + "close": 27547, + "Ġinvading": 27548, + "Ġdisposed": 27549, + "Ġhunted": 27550, + "Ġaveraging": 27551, + "OVE": 27552, + "Ġresilient": 27553, + "HH": 27554, + "ĠYOUR": 27555, + "rops": 27556, + "Neigh": 27557, + "Ġterrifying": 27558, + "Ġoperas": 27559, + "Ġpostal": 27560, + "uddin": 27561, + "Ġimplementations": 27562, + "Ġarmored": 27563, + "Äģn": 27564, + "bys": 27565, + "ĠTitans": 27566, + "ĠTunisia": 27567, + "each": 27568, + "ĠCream": 27569, + "oodle": 27570, + "athic": 27571, + "ertained": 27572, + "ĠPapua": 27573, + "TI": 27574, + "Ġì": 27575, + "reements": 27576, + "ĠTheore": 27577, + "Ġstarvation": 27578, + "ĠPak": 27579, + "ohan": 27580, + "Ġnationals": 27581, + "intended": 27582, + "ĠTay": 27583, + "ĠDru": 27584, + "Ġlett": 27585, + "Ġintegers": 27586, + "Hungarian": 27587, + "ingo": 27588, + "ĠFi": 27589, + "Ġtutor": 27590, + "Ġmum": 27591, + "inki": 27592, + "Ġsplits": 27593, + "Ġcrisp": 27594, + "ĠSophie": 27595, + "ĠSlovenia": 27596, + "Ġunpopular": 27597, + "micro": 27598, + "iration": 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31664, + "Ġparanoid": 31665, + "Const": 31666, + "rÃŃ": 31667, + "ĠStraw": 31668, + "rentice": 31669, + "ĠWoody": 31670, + "ĠBosnian": 31671, + "Ġscrapped": 31672, + "Ġoutrageous": 31673, + "Brain": 31674, + "ĠSG": 31675, + "ĠBorneo": 31676, + "ĠFurn": 31677, + "roughs": 31678, + "Ġtransverse": 31679, + "Ġantagonist": 31680, + "Ġspawned": 31681, + "ĠChesapeake": 31682, + "Ord": 31683, + "âĢĿ;": 31684, + "ĠComes": 31685, + "Ġcentrif": 31686, + "Ġanimations": 31687, + "ĠAmar": 31688, + "Ġoppressed": 31689, + "Ġbaron": 31690, + "ĠYounger": 31691, + "Ġcrabs": 31692, + "Academics": 31693, + "з": 31694, + "ĠICC": 31695, + "Ġmanned": 31696, + "Ġgreet": 31697, + "ĠReporting": 31698, + "Universal": 31699, + "Ġimpedance": 31700, + "heard": 31701, + "ĠTik": 31702, + "ĠLoud": 31703, + "ĠExeter": 31704, + "Ġcampaigning": 31705, + "Perm": 31706, + "poly": 31707, + "ĠGing": 31708, + "Ġarsenal": 31709, + "Ġpairing": 31710, + "Ġcurvature": 31711, + "Tok": 31712, + "Ġfibr": 31713, + "Ġgraders": 31714, + 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31815, + "ĠHeck": 31816, + "Ġslain": 31817, + "inched": 31818, + "Angel": 31819, + "Ġpunctuation": 31820, + "Ġsanctioned": 31821, + "Ġtad": 31822, + "ĠAZ": 31823, + "ĠQuiz": 31824, + "Ġrubbish": 31825, + "Ġsyndicated": 31826, + "ĠDamon": 31827, + "Statistics": 31828, + "Ġbalcony": 31829, + "iad": 31830, + "edited": 31831, + "ĠTara": 31832, + "ĠLaunch": 31833, + "ĠVirus": 31834, + "ĠIDE": 31835, + "Error": 31836, + "Clin": 31837, + "nard": 31838, + "Ġpopping": 31839, + "eeks": 31840, + "Ġwording": 31841, + "ibern": 31842, + "ĠMcInt": 31843, + "ĠEstonian": 31844, + "aunch": 31845, + "ĠNationals": 31846, + "Ġalcoholism": 31847, + "ozoic": 31848, + "ĠMarcos": 31849, + "Bell": 31850, + "Ġsands": 31851, + "format": 31852, + "issors": 31853, + "Ġlawful": 31854, + "risy": 31855, + "ĠPhotoshop": 31856, + "Ġasteroids": 31857, + "Ġeuros": 31858, + "Ġsinks": 31859, + "orio": 31860, + "iuseppe": 31861, + "Intro": 31862, + "ĠYankee": 31863, + "obbies": 31864, + "((": 31865, + "âĸ": 31866, + "ifty": 31867, + "Ġdeserts": 31868, + "Ġmonastic": 31869, + "Ġblockade": 31870, + "Ġcyclist": 31871, + "Ġboosting": 31872, + "Ġdiverted": 31873, + "asian": 31874, + "odiac": 31875, + "Ġmetamorph": 31876, + "Ġpresses": 31877, + "ĠSecrets": 31878, + "Ġfavourable": 31879, + "Ġresidency": 31880, + "Ġinequalities": 31881, + "Ġthru": 31882, + "ĠCopy": 31883, + "ĠPI": 31884, + "Ġrant": 31885, + "rens": 31886, + "ĠSunshine": 31887, + "ĠTalbot": 31888, + "ĠMandarin": 31889, + "Ġasympt": 31890, + "Ġskepticism": 31891, + "*.": 31892, + "ĠCale": 31893, + "Ġvested": 31894, + "Ġquir": 31895, + "ĠSwitch": 31896, + "Ġreacting": 31897, + "Ġexcurs": 31898, + "ĠProphe": 31899, + "ĠJensen": 31900, + "Ġcurator": 31901, + "Ġdepress": 31902, + "Ġabsorbs": 31903, + "ĠLeviticus": 31904, + "Vitamin": 31905, + "ombo": 31906, + "ĠDancing": 31907, + "âĢĿ:": 31908, + "Ġmonst": 31909, + "Ġdependencies": 31910, + "ĠChevrolet": 31911, + "DJ": 31912, + "oit": 31913, + "Ġoft": 31914, + "Ġroasted": 31915, + "shield": 31916, + "Structure": 31917, + "yla": 31918, + "Ġbureau": 31919, + "ĠTT": 31920, + "ĠTorn": 31921, + "ĠMeredith": 31922, + "umably": 31923, + "ĠGund": 31924, + "ĠTrading": 31925, + "ĠAppendix": 31926, + "eddings": 31927, + "Ġconsultants": 31928, + "Ġchimney": 31929, + "Ġhiatus": 31930, + "racial": 31931, + "ĠCald": 31932, + "Usually": 31933, + "Ġelevations": 31934, + "Ġashore": 31935, + "ĠIsh": 31936, + "Ġcaption": 31937, + "ĠEighth": 31938, + "Ġcloning": 31939, + "ĠReferences": 31940, + "ricken": 31941, + "Ġappliance": 31942, + "Nevertheless": 31943, + "oden": 31944, + "ĠVera": 31945, + "Ġobama": 31946, + "Ġcorrupted": 31947, + "ĠTrojan": 31948, + "ĠVeterinary": 31949, + "altern": 31950, + "Ġpointers": 31951, + "ĠMona": 31952, + "ĠEconomist": 31953, + "ĠLaurent": 31954, + "River": 31955, + "urrection": 31956, + "âĢIJ": 31957, + "Ġcoex": 31958, + "ĠGenome": 31959, + "Ġbaths": 31960, + "Ġreinforcements": 31961, + "Ġunderside": 31962, + "Ġmediocre": 31963, + "omans": 31964, + "Ġexquis": 31965, + "Ġsoak": 31966, + "rule": 31967, + "focused": 31968, + ":\\": 31969, + "bara": 31970, + "total": 31971, + "oway": 31972, + "ĠOlivia": 31973, + "Ġinvertebrates": 31974, + "yam": 31975, + "ĠGig": 31976, + "ocon": 31977, + "iken": 31978, + "sha": 31979, + "ĠNoel": 31980, + "Ġeyew": 31981, + "Ġfascination": 31982, + "ĠIMF": 31983, + "ĠSporting": 31984, + "ĠAnatomy": 31985, + "ĠDIY": 31986, + "Taylor": 31987, + "sound": 31988, + "ĠTarg": 31989, + "Ġinfinity": 31990, + "Ġcollaborators": 31991, + "Ġpyram": 31992, + "Pad": 31993, + "ĠLaugh": 31994, + "ĠGuth": 31995, + "Ġcontinual": 31996, + "odegrad": 31997, + "Force": 31998, + "Ġacademia": 31999, + "Ġquarterly": 32000, + "ĠKand": 32001, + "ĠIdentification": 32002, + "Ġhelmets": 32003, + "Ġbushes": 32004, + "Ġinforming": 32005, + "Ġstadiums": 32006, + "IED": 32007, + "Offic": 32008, + "waves": 32009, + "econom": 32010, + "ĠLibyan": 32011, + "Ġparole": 32012, + "Ġpersists": 32013, + "Ġattendant": 32014, + "ĠPanch": 32015, + "ĠGetty": 32016, + "Ġblankets": 32017, + "Ġnarration": 32018, + "Ñĸ": 32019, + "Ġhinder": 32020, + "otions": 32021, + "ĠVPN": 32022, + "Ġprotons": 32023, + "Ġprimer": 32024, + "Ġaggregation": 32025, + "Ġdroplets": 32026, + "mis": 32027, + "ĠAH": 32028, + "ĠWILL": 32029, + "Ġaccusing": 32030, + "Ġirrad": 32031, + "Ġparasitic": 32032, + "ĠMK": 32033, + "acic": 32034, + "ĠGord": 32035, + "ĠNay": 32036, + "ĠActing": 32037, + "ĠEvangel": 32038, + "Software": 32039, + "Ġlattice": 32040, + "ĠDresden": 32041, + "RY": 32042, + "erobic": 32043, + "ĠStrand": 32044, + "Ġovercoming": 32045, + "ecal": 32046, + "Unit": 32047, + "Ġrestructuring": 32048, + "PE": 32049, + "ĠDolphins": 32050, + "Ġtempting": 32051, + "Division": 32052, + "Rather": 32053, + "WHO": 32054, + "meg": 32055, + "ĠCustomer": 32056, + "Ġrhetorical": 32057, + "Kal": 32058, + "none": 32059, + "ouched": 32060, + "ĠPaw": 32061, + "cheon": 32062, + "oscopic": 32063, + "OWN": 32064, + "Ġlush": 32065, + "ĠRao": 32066, + "ĠGift": 32067, + "Ġcontextual": 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