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--- |
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license: apache-2.0 |
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base_model: codeparrot/codeparrot-small |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: solidity-generator |
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results: [] |
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datasets: |
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- mwritescode/slither-audited-smart-contracts |
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pipeline_tag: text-generation |
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language: |
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- en |
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library_name: transformers |
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widget: |
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- text: "contract MyToken is ERC20{" |
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--- |
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# solidity-generator |
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This model is a model specialized in generating Solidity contract codes. Derived from the [codeparrot/codeparrot-small](https://huggingface.co/codeparrot/codeparrot-small) model, it's been meticulously trained on an extensive set of Solidity contracts and patterns, making it apt for assisting in drafting or suggesting contract structures. |
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## Model description |
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This model has been designed specifically for generating Solidity contracts. Being a derivative of the `codeparrot-small` model, it retains the broader capabilities of the parent model while demonstrating a keen proficiency in understanding and generating Solidity-centric texts. |
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### Performance |
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The model reported a loss of `0.2180` on the evaluation set. |
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## Intended Uses & Limitations |
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### Intended Uses: |
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1. Assist developers by auto-generating contract code snippets based on prompts. |
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2. Help in understanding and drafting complex contract structures. |
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### Limitations: |
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1. The generated code must be reviewed for security and functional correctness. |
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2. The clarity of the generated code largely depends on the specificity of the prompt. |
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## Training Details |
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### Dataset |
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The model was fine-tuned on an undisclosed dataset comprised of a range of Solidity contracts. |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 7e-05 |
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- train_batch_size: 5 |
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- eval_batch_size: 5 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 144 |
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- num_epochs: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 0.302 | 0.35 | 2000 | 0.3237 | |
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| 0.298 | 0.69 | 4000 | 0.2871 | |
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| 0.232 | 1.04 | 6000 | 0.2645 | |
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| 0.2415 | 1.38 | 8000 | 0.2522 | |
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| 0.2261 | 1.73 | 10000 | 0.2431 | |
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| 0.1924 | 2.07 | 12000 | 0.2332 | |
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| 0.1913 | 2.42 | 14000 | 0.2282 | |
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| 0.2152 | 2.76 | 16000 | 0.2215 | |
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| 0.1508 | 3.11 | 18000 | 0.2180 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.3 |
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- Tokenizers 0.13.3 |
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## How to Use |
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If you wish to use this model to generate Solidity contract code, follow the steps below: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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# Load the model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("ckandemir/solidity_generator") |
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model = AutoModelForCausalLM.from_pretrained("ckandemir/solidity_generator") |
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# Input your code prompt |
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input_text = "contract MyToken is ERC20{" |
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input_ids = tokenizer.encode(input_text, return_tensors='pt') |
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sample_output = model.generate(input_ids, do_sample=True, max_length=400, num_return_sequences=1, temperature=0.7) |
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# Decode and print the generated text |
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generated_text = tokenizer.decode(sample_output[0], skip_special_tokens=True) |
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print(generated_text) |
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``` |
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