---
pipeline_tag: text-generation
inference: true
widget:
- text: 'Question: Please write a function in Python that performs bubble sort.\n\nAnswer:'
  example_title: Bubble sort
  group: Python
datasets:
- bigcode/commitpackft
- bigcode/oasst-octopack
metrics:
- code_eval
library_name: transformers
language:
- zh
- en
tags:
- codegeex
- glm
- chatglm
model-index:
- name: OctoGeeX
  results:
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalSynthesize Python
    metrics:
    - name: pass@1
      type: pass@1
      value: 44.7
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalSynthesize JavaScript
    metrics:
    - name: pass@1
      type: pass@1
      value: 33.8
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalSynthesize Java
    metrics:
    - name: pass@1
      type: pass@1
      value: 36.9
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalSynthesize Go
    metrics:
    - name: pass@1
      type: pass@1
      value: 21.9
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalSynthesize C++
    metrics:
    - name: pass@1
      type: pass@1
      value: 32.3
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalSynthesize Rust
    metrics:
    - name: pass@1
      type: pass@1
      value: 25.7
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalSynthesize Average
    metrics:
    - name: pass@1
      type: pass@1
      value: 30.9
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalFix Python
    metrics:
    - name: pass@1
      type: pass@1
      value: 28.1
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalFix JavaScript
    metrics:
    - name: pass@1
      type: pass@1
      value: 27.7
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalFix Java
    metrics:
    - name: pass@1
      type: pass@1
      value: 30.4
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalFix Go
    metrics:
    - name: pass@1
      type: pass@1
      value: 27.6
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalFix C++
    metrics:
    - name: pass@1
      type: pass@1
      value: 22.9
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalFix Rust
    metrics:
    - name: pass@1
      type: pass@1
      value: 9.6
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalFix Average
    metrics:
    - name: pass@1
      type: pass@1
      value: 24.4
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalExplain Python
    metrics:
    - name: pass@1
      type: pass@1
      value: 30.4
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalExplain JavaScript
    metrics:
    - name: pass@1
      type: pass@1
      value: 24.0
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalExplain Java
    metrics:
    - name: pass@1
      type: pass@1
      value: 24.7
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalExplain Go
    metrics:
    - name: pass@1
      type: pass@1
      value: 21.7
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalExplain C++
    metrics:
    - name: pass@1
      type: pass@1
      value: 21.0
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalExplain Rust
    metrics:
    - name: pass@1
      type: pass@1
      value: 15.9
      verified: false
  - task:
      type: text-generation
    dataset:
      type: bigcode/humanevalpack
      name: HumanEvalExplain Average
    metrics:
    - name: pass@1
      type: pass@1
      value: 22.9
      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. [License](#license)
5. [Citation](#citation)

# Model Summary

> OctoGeeX is an instruction tuned model with 6B parameters created by fine-tuning [CodeGeeX2](https://huggingface.co/THUDM/codegeex2-6b) on [CommitPackFT](https://huggingface.co/datasets/bigcode/commitpackft) & [OASST](https://huggingface.co/datasets/bigcode/oasst-octopack) as described in the OctoPack paper.

- **Repository:** [bigcode-project/octopack](https://github.com/bigcode-project/octopack)
- **Paper:** [OctoPack: Instruction Tuning Code Large Language Models](https://arxiv.org/abs/2308.07124)
- **Languages:** 100+ 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. You should always preface your input with "Question: " and finish it 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/octogeex"
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)

# License

本仓库的代码依照 [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) 协议开源，模型的权重的使用则需要遵循 [Model License](MODEL_LICENSE)。

The code in this repository is open-source under the [MIT license](https://github.com/bigcode-project/octopack/blob/main/LICENSE). The model weights are licensed under the [Model License](MODEL_LICENSE), please apply for commercial use by filling the [registration form](https://open.bigmodel.cn/mla/form?mcode=CodeGeeX2-6B).

# Citation

```bibtex
@article{muennighoff2023octopack,
      title={OctoPack: Instruction Tuning Code Large Language Models}, 
      author={Niklas Muennighoff and Qian Liu and Armel Zebaze and Qinkai Zheng and Binyuan Hui and Terry Yue Zhuo and Swayam Singh and Xiangru Tang and Leandro von Werra and Shayne Longpre},
      journal={arXiv preprint arXiv:2308.07124},
      year={2023}
}
```