Text Generation
Transformers
Safetensors
English
codegen
Solidity
BlockChain
Smart Contracts
Code Generation
Instructions to use Chain-GPT/Solidity-LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chain-GPT/Solidity-LLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Chain-GPT/Solidity-LLM")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Chain-GPT/Solidity-LLM") model = AutoModelForCausalLM.from_pretrained("Chain-GPT/Solidity-LLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Chain-GPT/Solidity-LLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Chain-GPT/Solidity-LLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Chain-GPT/Solidity-LLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Chain-GPT/Solidity-LLM
- SGLang
How to use Chain-GPT/Solidity-LLM 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 "Chain-GPT/Solidity-LLM" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Chain-GPT/Solidity-LLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Chain-GPT/Solidity-LLM" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Chain-GPT/Solidity-LLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Chain-GPT/Solidity-LLM with Docker Model Runner:
docker model run hf.co/Chain-GPT/Solidity-LLM
Upload folder using huggingface_hub
Browse files
README.md
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# Summary
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Solidity LLM, despite its compact 2B parameter size, delivers standout performance in generating Solidity smart contracts. It achieved the highest compilation success rate (83%), showcasing robust syntactic and structural understanding. Its strong OpenZeppelin compliance (65%), though slightly behind very large models like GPT-4.5, is impressive given the scale difference, reflecting reliable use of industry-standard patterns and libraries.
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Further, Solidity LLM ranked highest in gas efficiency (72%), producing optimized code suitable for cost-sensitive deployments. While the security score (58%) indicates room for improvement, the model consistently generated secure-enough contracts for practical use. Its concise output (70% LOC score) also suggests an efficient coding style, balancing brevity with completeness.
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# Summary
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Solidity LLM, despite its compact 2B parameter size, delivers standout performance in generating Solidity smart contracts. It achieved the highest compilation success rate (83%), showcasing robust syntactic and structural understanding. Its strong OpenZeppelin compliance (65%), though slightly behind very large models like GPT-4.5, is impressive given the scale difference, reflecting reliable use of industry-standard patterns and libraries.
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Further, Solidity LLM ranked highest in gas efficiency (72%), producing optimized code suitable for cost-sensitive deployments. While the security score (58%) indicates room for improvement, the model consistently generated secure-enough contracts for practical use. Its concise output (70% LOC score) also suggests an efficient coding style, balancing brevity with completeness.
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Overall, Solidity LLM proves to be a resource-efficient, reliable, and well-balanced model for Solidity code generation.
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Looking ahead, future releases will focus on improving support for newer versions of the Solidity language and OpenZeppelin libraries, enhancing user interaction by enabling contract modifications, expanding compatibility to other languages like Rust, and developing larger models capable of handling longer context windows.
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