README.md

---
pipeline_tag: text-generation
inference: true
widget:
- text: '<commit_before>def has_close_elements(numbers: List[float], threshold: float) -> bool:\n    for idx, elem in enumerate(numbers):\n        for idx2, elem2 in enumerate(numbers):\n            if idx != idx2:\n                distance = elem - elem2\n                if distance < threshold:\n                    return True\n\n    return False<commit_message>Fix bugs in has_close_elements.<commit_after>'
  example_title: Fix has_close_elements
  group: Python
license: bigcode-openrail-m
datasets:
- bigcode/commits-8129-v2
metrics:
- code_eval
library_name: transformers
tags:
- code
model-index:
- name: SantaCoderPack
  results:
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalFix Python
    metrics:
    - name: pass@1
      type: pass@1
      value: 3.2
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalFix JavaScript
    metrics:
    - name: pass@1
      type: pass@1
      value: 4.9
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalFix Java
    metrics:
    - name: pass@1
      type: pass@1
      value: 1.8
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalFix Go
    metrics:
    - name: pass@1
      type: pass@1
      value: 3.6
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalFix C++
    metrics:
    - name: pass@1
      type: pass@1
      value: 4.2
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalFix Rust
    metrics:
    - name: pass@1
      type: pass@1
      value: 1.7
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalFix Average
    metrics:
    - name: pass@1
      type: pass@1
      value: 3.3
      verified: false
---
![Octopack](https://github.com/bigcode-project/octopack/blob/31f3320f098703c7910e43492c39366eeea68d83/banner.png?raw=true)

# Table of Contents

1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Training](#training)
4. [Citation](#citation)

# Model Summary

SantaCoderPack is an pre-trained model with the same architecture of SantaCoder on 
<th><a href=https://huggingface.co/datasets/bigcode/commitpack>CommitPack</a> using this format: 
```html
<commit_before>code_before<commit_msg>message<commit_after>
```

- **Repository:** [bigcode/octopack](https://github.com/bigcode-project/octopack)
- **Paper:** [TODO]()
- **Languages:** Python, JavaScript, Java, C++, Go, Rust
- **SantaCoderPack:**
<table>
<tr>
<th>Data</t> 
<th><a href=https://huggingface.co/datasets/bigcode/commitpack>CommitPack</a></th>
<td>4TB of GitHub commits across 350 programming languages</td>
</tr>
<tr>
<th>Model</t> 
<th><a href=https://huggingface.co/bigcode/octocoder>SantaCoderPack</a></th>
<td>SantaCoderPack (1.1B parameters) pre-trained on CommitPack</td>
</tr>
<tr>
<th>Evaluation&nbsp;&nbsp;</t> 
<th><a href=https://huggingface.co/datasets/bigcode/humanevalpack>HumanEvalPack/HumanEvalFix</a></th>
<td>Extension of OpenAI's HumanEval to HumanEvalFix</td>
</tr>
</table>


# Use

## Intended use

The model follows instructions provided in the input. We recommend prefacing your input with "<commit_before>def has_close_elements(numbers: List[float], threshold: float) -> bool:\n    for idx, elem in enumerate(numbers):\n        for idx2, elem2 in enumerate(numbers):\n            if idx != idx2:\n                distance = elem - elem2\n                if distance < threshold:\n                    return True\n\n    return False<commit_message>Fix bugs in has_close_elements.<commit_after>"

**Feel free to share your generations in the Community tab!**

## Generation
```python
# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "bigcode/santacoderpack"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("Q<commit_before>def has_close_elements(numbers: List[float], threshold: float) -> bool:\n    for idx, elem in enumerate(numbers):\n        for idx2, elem2 in enumerate(numbers):\n            if idx != idx2:\n                distance = elem - elem2\n                if distance < threshold:\n                    return True\n\n    return False<commit_message>Fix bugs in has_close_elements.<commit_after>", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```

# Training

## Model

- **Architecture:** GPT-2 model with multi-query attention
- **Steps:** 250k pretraining
- **Pretraining tokens:** 131B
- **Precision:** bfloat16

## Hardware

- **Pretraining:**
  - **GPUs:** 32 Tesla A100
  - **Training time:** 15 days

## Software

- **Orchestration:** [Megatron-LM/Transformers](https://github.com/bigcode-project/santacoderpack#training)
- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)

# Citation

TODO