---
license: mit
tags:
- text-generation
- qwen2
- instruct
- unsloth
- OpenDevin
datasets:
- xingyaoww/opendevin-code-act
---

## Qwen2.7b.OpenDevin 
brought to you by skratos115 (HF) / Kingatlas115 (GH) in colaboration with the official Opendevin Team ~xingyaoww
 
# Qwen2-7B-Instruct with OpenDevin Tool Calling

## Overview

This project involves the fine-tuning of the `Qwen2-7B-Instruct` model using the [opendevin-code-act dataset](https://huggingface.co/datasets/xingyaoww/opendevin-code-act) with the help of Unsloth. The primary goal is to develop a more powerful LLM capable of effectively using the CodeAct framework for tool calling. This is still in early development and should not be used in production. We are working on building a bigger dataset for tool paths/ trajectories and could you all the help we can by using the feedback integration to help us build better trajectories and release to the public via MIT license for OSS model training.
read more here:https://x.com/gneubig/status/1802740786242420896 and http://www.linkedin.com/feed/update/urn:li:activity:7208507606728929280/

## Model Details

- **Model Name**: Qwen2-7B-Instruct
- **Dataset**: [opendevin-code-act](https://huggingface.co/datasets/xingyaoww/opendevin-code-act)
- **Training Platform**: Unsloth

provided full merged files
	or
Quantized f16, q4_k_m, Q5_k_m, and Q8_0 gguf files.
I used the qwen2.7b.OD.q4_k_m.gguf for my testing and got it to write me a simple script. more testing to come.

## Running the Model

You can run this model using `vLLM` or `ollama`. The following instructions are for using `ollama`.

### Prerequisites

- Docker
- Hugging Face `transformers` library (version >= 4.37.0 is recommended)

q-4k
ollama run skratos115/qwen2-7b-opendevin-q4_k_m

or

f16
ollama run skratos115/qwen2-7b-opendevin-f16

### Running with Ollama

1. **Install Docker**: Ensure you have Docker installed on your machine.

2. **Pull the Latest Hugging Face Transformers**: 
   
   pip install transformers>=4.37.0
   

3. **Set Up Your Workspace**:
   
   WORKSPACE_BASE=$(pwd)/workspace
  

4. **Run the Docker Command**:
 docker run -it \
    --pull=always \
    -e SANDBOX_USER_ID=$(id -u) \
    -e PERSIST_SANDBOX="true" \
    -e LLM_API_KEY="ollama" \
    -e LLM_BASE_URL="http://[yourIPhere or 0.0.0.0]:11434" \
    -e SSH_PASSWORD="make something up here" \
    -e WORKSPACE_MOUNT_PATH=$WORKSPACE_BASE \
    -v $WORKSPACE_BASE:/opt/workspace_base \
    -v /var/run/docker.sock:/var/run/docker.sock \
    -p 3000:3000 \
    --add-host host.docker.internal:host-gateway \
    --name opendevin-app-$(date +%Y%m%d%H%M%S) \
    ghcr.io/opendevin/opendevin:main

Replace `[yourIPhere or 0.0.0.0]` with your actual IP address or use `0.0.0.0` for localhost.

## Early Development

This project is in its early stages, and we are continuously working to improve the model and its capabilities. Contributions and feedback are welcome.

## Support my work

Right now all of my work has been funded personally, if you like my work and can help support growth in the AI community consider joining or donating to my Patreon.
[Patreon Link](https://www.patreon.com/atlasaisecurity)

## License

This project is licensed under the [MIT License](LICENSE).