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Offlin33er
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Model Details
Model Provider
Offlin33er
Model Tree
Qwen/Qwen2.5-Coder-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Given a Solidity snippet, produces a structured audit report: the primary finding classified into exactly one of 8 canonical categories (Reentrancy, Arithmetic Error, Access Control Flaw, Input Validation Error, Frontrunning / MEV, Denial of Service (DoS), Business Logic Violation, Clean (No Vulnerability)) — with functionality analysis, description, and remediation recommendation.
| Metric | V3 (this model) | V2 | V1 |
|---|---|---|---|
| Detection precision | 0.986 | 0.986 | 1.00 |
| Detection recall | 0.986 | 0.971 | 1.00 |
| False-positive rate on clean | 0.033 | 0.033 | 0.00 |
| Finding-type match (exact canonical) | 0.449 | 0.485 | 0.30 (lenient) |
Full per-row records: V3 eval_results.json.
Honest read: V1's perfect detection was a dataset artifact (see its card); V3 is the best detection config with the cleanest labels; the type-match gap between V2/V3 is within sampling noise at n≈70, and residual type errors are semantic ambiguity in reference labels, not model bugs.
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model = AutoModelForCausalLM.from_pretrained( "Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged", dtype=torch.bfloat16, device_map="auto")tok = AutoTokenizer.from_pretrained("Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged") messages = [ {"role": "system", "content": SYSTEM_PROMPT_V3}, # the V3 taxonomy prompt from the dataset card {"role": "user", "content": "Audit the following Solidity code for security vulnerabilities.\n\n```solidity\n<YOUR CODE>\n```"},]inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=512, do_sample=False)print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))The exact SYSTEM_PROMPT_V3 text is on the dataset card —
it was trained verbatim and must be used as-is.
1 epoch (139 steps) over Offlin33er/solidity-audit-instruct-v3 (2,224 rows, canonical 8-class taxonomy, split-preserving vs V1/V2); QLoRA r=32 on all attn+MLP projections, LR 2e-4 cosine, merged after training. Tracked on the trackio dashboard.
Defensive security tooling: analyzes code you paste in. Only audit contracts you are authorized to review.
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model = AutoModelForCausalLM.from_pretrained( "Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged", dtype=torch.bfloat16, device_map="auto")tok = AutoTokenizer.from_pretrained("Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged") messages = [ {"role": "system", "content": SYSTEM_PROMPT_V3}, # the V3 taxonomy prompt from the dataset card {"role": "user", "content": "Audit the following Solidity code for security vulnerabilities.\n\n```solidity\n<YOUR CODE>\n```"},]inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=512, do_sample=False)print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))