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Model: bytesbrains/hunter-crypto-7b Source: Original Platform
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LICENSE
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LICENSE
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hunter-crypto — License
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Copyright © 2026 BytesBrains Pte. Ltd. (Naderu)
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This model is released under the Apache License, Version 2.0.
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It is a derivative (LoRA fine-tune, merged) of Qwen/Qwen2.5-Coder-7B-Instruct,
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which is licensed under Apache-2.0. The upstream license and terms carry through to
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this derivative. A copy of the Apache-2.0 license is available at:
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https://www.apache.org/licenses/LICENSE-2.0
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The Naderu-authored training data and fine-tuning code in this repository are likewise
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provided under Apache-2.0.
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Use restriction (intended-use notice): hunter-crypto is provided for authorized
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penetration testing, defensive security, and CTF/education only. It must not be used to
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attack systems without explicit permission. This notice states intended use; it does not
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modify the Apache-2.0 grant above.
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README.md
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README.md
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---
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license: apache-2.0
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license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct/blob/main/LICENSE
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base_model: Qwen/Qwen2.5-Coder-7B-Instruct
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language:
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- en
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library_name: mlx
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pipeline_tag: text-generation
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tags:
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- cryptography
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- security
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- ctf
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- offensive-security
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- mlx
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- qlora
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- qwen-coder
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---
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# hunter-crypto-7b
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**A local cryptography-attack specialist for authorized security work, by [Naderu](https://naderu.com) — a BytesBrains Pte. Ltd. venture.**
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`hunter-crypto` is a compact, specialised model for the **cryptography corner** of penetration
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testing and CTF work: given a challenge — a cipher, a weak-parameter RSA key, an oracle — it
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identifies the weakness and produces a **runnable attack script** (PyCryptodome / SageMath) that
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recovers the plaintext or flag. It runs **fully offline on Apple Silicon**, so engagement data never
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leaves the box. It is the first model of the **Hunter** family.
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- **Fine-tuned from:** [`Qwen/Qwen2.5-Coder-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) (Apache-2.0)
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- **Method:** QLoRA (r=8, scale=20) on a 4-bit MLX quant, assistant-tokens-only, 1000 iters, MLX on Apple Silicon
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- **This artifact:** merged **bf16** weights (adapter fused into the base, de-quantized)
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- **Full provenance, recipe, and eval harness:** [github.com/nandal/naderu](https://github.com/nandal/naderu) — `models/hunter-crypto/`
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## Intended use
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A **local specialist** behind an **authorized** pentest / CTF workflow, handling the cryptography
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corner offline: recovering plaintext/flags from **cryptographically weak or misconfigured**
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constructions during authorized testing, CTF competition, and security education.
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> **Authorized use only.** This model is for authorized penetration testing, defensive security, and
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> CTF/education. It is **not** for attacking systems you do not have explicit permission to test. It
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> targets weak/misconfigured crypto for assessment; it is not a tool for defeating properly-deployed
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> modern cryptography, and does not claim to.
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**Out of scope:** general chat, factual Q&A, non-crypto exploitation, and any use requiring
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guaranteed correctness. Its output is a *candidate* attack to be run and verified, not an authority.
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## The task contract
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Given a challenge (description + material: ciphertext, public parameters, an oracle interface,
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source), the model returns (1) a short identification of the weakness, and (2) a single runnable
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script that, when executed against the provided material, prints the recovered plaintext/flag.
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## Evaluation
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Graded by end-to-end execution — the model's script must run and print the known secret (no partial
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credit) — over a frozen held-out set verified disjoint from training. Base = the 4-bit Qwen2.5-Coder
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base with no adapter; trained = this model.
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| Tier | Metric | Gate | Base | **Trained** |
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|------|--------|------|------|-------------|
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| In-distribution (48, six trained categories) | solve_rate | ≥0.95 → **PASS** | 0.333 | **1.000** |
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| In-distribution (48) | valid_script_rate | ≥0.95 → **PASS** | 0.333 | **1.000** |
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| Independent-method (36, different generator) | solve_rate | reported | 0.250 | **0.972** |
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| OOD-hard (24, untrained RSA attacks) | solve_rate | non-gating | 0.000 | 0.125 |
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Trained categories (all maxed on the in-distribution tier): `xor1`, `xorK`, `caesar`, `affine`,
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`base_chain`, `rsa_low_e`. The independent-method result (0.972 on a set built by a *different*
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generator with varied encodings/labels/parameters) is the signal that the model learned the attacks,
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not just the training generator's surface templates. Full breakdown:
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[`models/hunter-crypto/benchmarks.md`](https://github.com/nandal/naderu/blob/main/models/hunter-crypto/benchmarks.md).
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## Limitations, risks & safety
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- **Small, narrow model.** It proposes attacks for *weak* crypto; it is not a cryptanalysis engine
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and will be wrong on hard or out-of-distribution problems (see OOD tier). Always run and verify.
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- **No guarantee of correctness or safety of generated code — run generated scripts in a sandbox.**
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- **Dual-use.** Crypto-attack capability is inherently dual-use; scope is authorized assessment of
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weak/misconfigured crypto, shipped as one corner of a human-consent-gated workflow.
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## License
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Apache-2.0, inheriting `Qwen/Qwen2.5-Coder-7B-Instruct`'s upstream terms.
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54
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{{\"name\": <function-name>, \"arguments\": <args-json-object>}}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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"eos_token_id": 151643,
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"hidden_size": 3584,
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"initializer_range": 0.02,
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"intermediate_size": 18944,
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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"rms_norm_eps": 1e-06,
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"rope_theta": 1000000.0,
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"sliding_window": 131072,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.44.0",
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"use_cache": true,
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"vocab_size": 152064
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3
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3
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31
tokenizer_config.json
Normal file
31
tokenizer_config.json
Normal file
@@ -0,0 +1,31 @@
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}
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Reference in New Issue
Block a user