Instructions to use jhsu12/solidity-vulnerability-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jhsu12/solidity-vulnerability-detector with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct") model = PeftModel.from_pretrained(base_model, "jhsu12/solidity-vulnerability-detector") - Transformers
How to use jhsu12/solidity-vulnerability-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jhsu12/solidity-vulnerability-detector") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jhsu12/solidity-vulnerability-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jhsu12/solidity-vulnerability-detector with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jhsu12/solidity-vulnerability-detector" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jhsu12/solidity-vulnerability-detector", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/jhsu12/solidity-vulnerability-detector
- SGLang
How to use jhsu12/solidity-vulnerability-detector 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 "jhsu12/solidity-vulnerability-detector" \ --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": "jhsu12/solidity-vulnerability-detector", "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 "jhsu12/solidity-vulnerability-detector" \ --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": "jhsu12/solidity-vulnerability-detector", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use jhsu12/solidity-vulnerability-detector with Docker Model Runner:
docker model run hf.co/jhsu12/solidity-vulnerability-detector
Commit History
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Add fine-tuning process presentation markdown
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Add demo script: saves FN/FP Solidity code as .sol files for demo purposes
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Remove old comparison files, will re-upload to demo/
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Move comparison script to demo/ folder, output to demo/false_negative_and_false_positive/
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Add base vs fine-tuned comparison script for finding FN/FP examples
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Add baseline evaluation script for solidity vulnerability detection
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Upload evaluate_on_kaggle.py
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Upload calibrate_thresholds.py
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Add script to evaluate classifiers on combined data from all expert datasets
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Add sanity check script: evaluate classifier on its own training/test data
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Add sanity check: print first 5 samples with raw output vs ground truth
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Add expert classifier evaluation script with per-vuln breakdowns and Trackio visualization
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Add script to upload Solidity evaluation dataset to Hub
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Update upload script: support all checkpoint-* folders per expert, upload as branches + tags
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Add script to upload expert classifier checkpoints to Hub
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Add classifier inference script for expert classification models
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Add inference script for vulnerability detection
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Fix post-training eval crash: use trainer.state.best_metric instead of separate evaluate() call
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Add classification-based expert training script
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Fix trackio.init(): use 'name' instead of 'run'
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Switch to Qwen2.5-Coder-3B-Instruct + fix OOM eval issues
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Add multi-expert evaluation script
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Add per-expert training script
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Add multi-expert training and evaluation scripts
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Add multi-expert training scripts and design document
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v2: Improved dataset — removed generic samples, added Access Control/Reentrancy/Unchecked Calls from darkknight25
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Add v2 training script — improved dataset, explicit Hub push, Access Control test
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Optimize eval: batched generation + greedy decoding + reduced max_tokens (10-20x faster)
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Update README with full model card, training details, and usage examples
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Add evaluation script with per-type metrics
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Upload best LoRA adapters (lowest eval loss)
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Fix: max_seq_length → max_length (TRL API rename)
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Fix: total_mem → total_memory (PyTorch API)
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Update: auto-detect flash-attn, add H200 tier, use sdpa fallback
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Add comprehensive README with usage, training details, and research background
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Add auto-configuring training script (Colab/A100/T4 compatible)
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initial commit
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