---
pipeline_tag: text-generation
inference: true
widget:
- text: 'def print_hello_world():'
  example_title: Hello world
  group: Python
license: bigcode-openrail-m
datasets:
- bigcode/commitpackft
- bigcode/oasst-octopack
metrics:
- code_eval
library_name: transformers
tags:
- code
model-index:
- name: OctoCoder
  results:
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalSynthesize Python
    metrics:
    - name: pass@1
      type: pass@1
      value: 46.2
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalSynthesize JavaScript
    metrics:
    - name: pass@1
      type: pass@1
      value: 39.2
      verified: false
---

![Octopack](https://github.com/bigcode-project/octopack/blob/31f3320f098703c7910e43492c39366eeea68d83/banner.png?raw=true)

# OctoCoder

Play with the model on the [TODO Playground](https://huggingface.co/spaces/bigcode/bigcode-playground).

##  Table of Contents

1. [Model Summary](##model-summary)
2. [Use](##use)
3. [Limitations](##limitations)
4. [Training](##training)
5. [License](##license)
6. [Citation](##citation)

## Model Summary

OctoCoder is an instruction tuned model with 15.5B parameters created by finetuning StarCoder on CommitPackFT & OASST as described in the OctoPack paper.

- **Repository:** [bigcode/octopack](https://github.com/bigcode-project/octopack)
- **Paper:** [TODO]()
- **Languages:** 80+ Programming languages
- **OctoPack🐙🎒:**
<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></t> 
<th><a href=https://huggingface.co/datasets/bigcode/commitpackft>CommitPackFT</a></th>
<td>Filtered version of CommitPack for high-quality commit messages that resemble instructions</td>
</tr>
<tr>
<th>Model</t> 
<th><a href=https://huggingface.co/bigcode/octocoder>OctoCoder</a></th>
<td>StarCoder (16B parameters) instruction tuned on CommitPackFT + OASST</td>
</tr>
<tr>
<th></t> 
<th><a href=https://huggingface.co/bigcode/octogeex>OctoGeeX</a></th>
<td>CodeGeeX2 (6B parameters) instruction tuned on CommitPackFT + OASST</td>
</tr>
<tr>
<th>Evaluation&nbsp;&nbsp;</t> 
<th><a href=https://huggingface.co/datasets/bigcode/humanevalpack>HumanEvalPack</a></th>
<td>Extension of OpenAI's HumanEval to cover 3 scenarios across 6 languages</td>
</tr>
</table>


## Use

### Intended use

The model follows instructions provided in the input. We recommend prefacing your input with "Question: " and finishing with "Answer:", for example: "Question: Please write a function in Python that performs bubble sort.\n\nAnswer:"

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

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

checkpoint = "bigcode/octocoder"
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("Question: Please write a function in Python that performs bubble sort.\n\nAnswer:", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```

# Training

## Model

- **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective
- **Steps:** 250k pretraining & 30 instruction tuning
- **Pretraining tokens:** 1 trillion pretraining & 2M instruction tuning
- **Precision:** bfloat16

## Hardware

- **Pretraining:**
  - **GPUs:** 512 Tesla A100
  - **Training time:** 24 days
- **Instruction tuning:**
  - **GPUs:** 8 Tesla A100
  - **Training time:** 4 hours

## Software

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

# Citation

TODO