---
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: openai/human-eval-infilling
      name: HumanEval
    metrics:
    - name: single-line infilling pass@1
      type: pass@1
      value: 0.47
      verified: false
    - name: single-line infilling pass@10
      type: pass@10
      value: 0.74
      verified: false
  - task:
      type: text-generation
    dataset:
      type: lambdasec/gh-top-1000-projects-vulns
      name: GH Top 1000 Projects Vulnerabilities
    metrics:
    - name: pass@1 (Java)
      type: pass@1
      value: 0.26
      verified: false
    - name: pass@10 (Java)
      type: pass@10
      value: 0.48
      verified: false
    - name: pass@1 (Python)
      type: pass@1
      value: 0.31
      verified: false
    - name: pass@10 (Python)
      type: pass@10
      value: 0.56
      verified: false
    - name: pass@1 (JavaScript)
      type: pass@1
      value: 0.36
      verified: false
    - name: pass@10 (JavaScript)
      type: pass@10
      value: 0.62
      verified: false
---

# Model Card for SantaFixer

<!-- Provide a quick summary of what the model is/does. -->

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

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



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


## How to Get Started with the Model

Use the code below to get started with the model.

```python
# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "lambdasec/santafixer"
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint,
              trust_remote_code=True).to(device)

input_text = "<fim-prefix>def print_hello_world():\n
              <fim-suffix>\n print('Hello world!')
              <fim-middle>"
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```

## Training Details

- **GPU:** Tesla P100
- **Time:** ~5 hrs

### Training Data

The model was fine-tuned on the [CVE single line fixes dataset](https://huggingface.co/datasets/lambdasec/cve-single-line-fixes)

### Training Procedure 

Supervised Fine Tuning (SFT)

#### Training Hyperparameters

- **optim:** adafactor
- **gradient_accumulation_steps:** 4
- **gradient_checkpointing:** true
- **fp16:** false

## Evaluation

The model was evaluted on the [GitHub top 1000 projects vulnerabilities dataset](https://huggingface.co/datasets/lambdasec/gh-top-1000-projects-vulns)