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qwen2-7b-instruct-OpenDevin-merged_model

Text Generation
Transformers
Safetensors
qwen2
instruct
unsloth
OpenDevin
conversational
text-generation-inference
Inference Endpoints
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metadata
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 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

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)

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

License

This project is licensed under the MIT License.