Instructions to use Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged")
model = AutoModelForCausalLM.from_pretrained("Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged

SGLang

How to use Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged with Docker Model Runner:
```
docker model run hf.co/Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged
```

Qwen2.5-Coder-7B — Solidity Audit (V3, merged, deploy-ready)

Full-weight merge of the V3 QLoRA adapter into Qwen/Qwen2.5-Coder-7B-Instruct (bf16, 7.6B params, single 15.2 GB safetensors shard). This repo exists specifically to be deployed on a Hugging Face Inference Endpoint (or served with TGI/vLLM) — the adapter version is for PEFT users.

Part of the Solidity Audit Scanner project.

What it does

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.

Measured evaluation (n=100 stratified held-out, greedy, seed 42)

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.

Usage (OpenAI-compatible once deployed on an endpoint; transformers locally)

from transformers import AutoModelForCausalLM, AutoTokenizer
import 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.

Deployment

HF Inference Endpoint: 1× Nvidia L4 (24 GB) recommended (~$0.80/hr, scale-to-zero for near-zero idle cost). A T4 (16 GB) does not fit bf16.
Greedy decoding, 512 max new tokens matches the evaluated configuration.

Training

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.

Limitations

Trained on ~2.5K Solodit-audited snippets; ~290 one-off label types in the source taxonomy.
~Half of detected findings still get an adjacent canonical label (44.9% exact match) — the residual errors are reference-label ambiguity (documented in the V3 card).
Not a substitute for a professional audit. Findings are candidates for human review.

Defensive security tooling: analyzes code you paste in. Only audit contracts you are authorized to review.

Downloads last month: 319

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Finetuned

(475)

this model

Offlin33er
/

qwen25-coder-7b-solidity-audit-v3-merged

Qwen2.5-Coder-7B — Solidity Audit (V3, merged, deploy-ready)

What it does

Measured evaluation (n=100 stratified held-out, greedy, seed 42)

Usage (OpenAI-compatible once deployed on an endpoint; transformers locally)

Deployment

Training

Limitations

Model tree for Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged

Dataset used to train Offlin33er/qwen25-coder-7b-solidity-audit-v3-merged