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--- |
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license: apache-2.0 |
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datasets: |
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- lambdasec/cve-single-line-fixes |
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- lambdasec/gh-top-1000-projects-vulns |
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language: |
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- code |
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tags: |
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- code |
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programming_language: |
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- Java |
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- JavaScript |
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- Python |
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inference: false |
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model-index: |
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- name: SantaFixer |
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results: |
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- task: |
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type: text-generation |
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dataset: |
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type: openai/human-eval-infilling |
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name: HumanEval |
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metrics: |
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- name: single-line infilling pass@1 |
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type: pass@1 |
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value: 0.47 |
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verified: false |
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- name: single-line infilling pass@10 |
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type: pass@10 |
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value: 0.74 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: lambdasec/gh-top-1000-projects-vulns |
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name: GH Top 1000 Projects Vulnerabilities |
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metrics: |
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- name: pass@1 (Java) |
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type: pass@1 |
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value: 0.26 |
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verified: false |
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- name: pass@10 (Java) |
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type: pass@10 |
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value: 0.48 |
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verified: false |
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- name: pass@1 (Python) |
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type: pass@1 |
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value: 0.31 |
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verified: false |
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- name: pass@10 (Python) |
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type: pass@10 |
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value: 0.56 |
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verified: false |
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- name: pass@1 (JavaScript) |
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type: pass@1 |
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value: 0.36 |
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verified: false |
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- name: pass@10 (JavaScript) |
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type: pass@10 |
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value: 0.62 |
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verified: false |
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--- |
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# Model Card for SantaFixer |
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<!-- Provide a quick summary of what the model is/does. --> |
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This is a LLM for code that is focussed on generating bug fixes using infilling. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** [codelion](https://huggingface.co/codelion) |
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- **Model type:** GPT-2 |
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- **Finetuned from model:** [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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# pip install -q transformers |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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checkpoint = "lambdasec/santafixer" |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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model = AutoModelForCausalLM.from_pretrained(checkpoint, |
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trust_remote_code=True).to(device) |
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input_text = "<fim-prefix>def print_hello_world():\n |
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<fim-suffix>\n print('Hello world!') |
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<fim-middle>" |
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inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) |
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outputs = model.generate(inputs) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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## Training Details |
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- **GPU:** Tesla P100 |
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- **Time:** ~5 hrs |
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### Training Data |
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The model was fine-tuned on the [CVE single line fixes dataset](https://huggingface.co/datasets/lambdasec/cve-single-line-fixes) |
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### Training Procedure |
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Supervised Fine Tuning (SFT) |
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#### Training Hyperparameters |
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- **optim:** adafactor |
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- **gradient_accumulation_steps:** 4 |
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- **gradient_checkpointing:** true |
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- **fp16:** false |
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## Evaluation |
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The model was tested with the [GitHub top 1000 projects vulnerabilities dataset](https://huggingface.co/datasets/lambdasec/gh-top-1000-projects-vulns) |
