---
license: apache-2.0
datasets:
- lambdasec/cve-single-line-fixes
- lambdasec/gh-top-1000-projects-vulns
language:
- code
tags:
- code
programming_language:
- Java
- JavaScript
- Python
inference: false
model-index:
- name: SantaFixer
  results:
  - task:
      type: text-generation
    dataset:
      type: lambdasec/gh-top-1000-projects-vulns
      name: GH Top 1000 Projects Vulnerabilities
    metrics:
    - name: pass@10 (Java)
      type: pass@10
      value: 0.1
      verified: false
    - name: pass@10 (Python)
      type: pass@10
      value: 0.2
      verified: false
    - name: pass@10 (JavaScript)
      type: pass@10
      value: 0.3
      verified: false
---

# Model Card for SantaFixer

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This is a LLM for code that is focussed on generating bug fixes using infilling. 

## Model Details

### Model Description

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- **Developed by:** [codelion](https://huggingface.co/codelion)
- **Model type:** GPT-2
- **Finetuned from model:** [bigcode/santacoder](https://huggingface.co/bigcode/santacoder)

### Model Sources [optional]

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## Uses

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### Direct Use

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

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## Training Details

### Training Data

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### Training Procedure 

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#### Training Hyperparameters

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## Evaluation

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### Results

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#### Summary



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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

### Model Architecture and Objective

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## Glossary [optional]

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