> For the complete documentation index, see [llms.txt](https://docs.chaingpt.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.chaingpt.org/dev-docs-b2b-saas-api-and-sdk/chaingpt-claude-skill-and-plugin/products/solidity-llm.md).

# Solidity LLM

## Solidity LLM

An open-source 2-billion-parameter language model fine-tuned specifically for Solidity smart contract generation. Hosted on HuggingFace, MIT-licensed, and completely free to run on your own hardware.

### Key Facts

|                              |                                                              |
| ---------------------------- | ------------------------------------------------------------ |
| **Model**                    | `Chain-GPT/Solidity-LLM`                                     |
| **Parameters**               | 2B                                                           |
| **License**                  | MIT                                                          |
| **Cost**                     | Free (self-hosted)                                           |
| **Platform**                 | [HuggingFace](https://huggingface.co/Chain-GPT/Solidity-LLM) |
| **Compilation Success Rate** | 83%                                                          |

### When to Use This vs. the Smart Contract Generator

|                | Solidity LLM                                           | Smart Contract Generator            |
| -------------- | ------------------------------------------------------ | ----------------------------------- |
| **Cost**       | Free                                                   | 1 credit per request                |
| **Hosting**    | Self-hosted (you need a GPU)                           | Cloud API                           |
| **Model size** | 2B parameters                                          | Larger, more capable model          |
| **Quality**    | Good (83% compilation rate)                            | Higher quality, production-tuned    |
| **Offline**    | Yes                                                    | No                                  |
| **Best for**   | Experimentation, CI pipelines, air-gapped environments | Production contracts, complex logic |

Use the Solidity LLM when you need offline generation, want zero API costs, or are integrating into CI/CD pipelines. Use the Smart Contract Generator when you need higher-quality output for production contracts.

### Quick Start -- Python

#### Install Dependencies

```bash
pip install transformers torch
```

#### Generate a Smart Contract

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Chain-GPT/Solidity-LLM"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Create a Solidity smart contract for an ERC-20 token with a 2% burn mechanism"

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_new_tokens=1024,
    temperature=0.7,
    do_sample=True,
)

generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_code)
```

#### With GPU Acceleration

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Chain-GPT/Solidity-LLM"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto",
)

prompt = "Write a Solidity contract for a multi-signature wallet requiring 3 of 5 approvals"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=1024,
    temperature=0.7,
    do_sample=True,
)

generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_code)
```

### Tips

* The 83% compilation success rate means roughly 1 in 5 outputs may need manual correction. Always compile and test the output.
* Pair the generated output with the Smart Contract Auditor to catch vulnerabilities before deployment.
* Use `float16` and `device_map="auto"` for efficient GPU memory usage.
* The model works well for standard patterns (ERC-20, ERC-721, staking, vesting) but may struggle with highly novel or complex contract logic.
* For production-critical contracts, consider using the cloud-based Smart Contract Generator instead.


---

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