Instructions to use skratos115/qwen2-7b-instruct-OpenDevin-merged_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use skratos115/qwen2-7b-instruct-OpenDevin-merged_model with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="skratos115/qwen2-7b-instruct-OpenDevin-merged_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("skratos115/qwen2-7b-instruct-OpenDevin-merged_model")
model = AutoModelForCausalLM.from_pretrained("skratos115/qwen2-7b-instruct-OpenDevin-merged_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use skratos115/qwen2-7b-instruct-OpenDevin-merged_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "skratos115/qwen2-7b-instruct-OpenDevin-merged_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "skratos115/qwen2-7b-instruct-OpenDevin-merged_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/skratos115/qwen2-7b-instruct-OpenDevin-merged_model

SGLang

How to use skratos115/qwen2-7b-instruct-OpenDevin-merged_model with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "skratos115/qwen2-7b-instruct-OpenDevin-merged_model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "skratos115/qwen2-7b-instruct-OpenDevin-merged_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "skratos115/qwen2-7b-instruct-OpenDevin-merged_model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "skratos115/qwen2-7b-instruct-OpenDevin-merged_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use skratos115/qwen2-7b-instruct-OpenDevin-merged_model with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for skratos115/qwen2-7b-instruct-OpenDevin-merged_model to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for skratos115/qwen2-7b-instruct-OpenDevin-merged_model to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for skratos115/qwen2-7b-instruct-OpenDevin-merged_model to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="skratos115/qwen2-7b-instruct-OpenDevin-merged_model",
    max_seq_length=2048,
)

Docker Model Runner
How to use skratos115/qwen2-7b-instruct-OpenDevin-merged_model with Docker Model Runner:
```
docker model run hf.co/skratos115/qwen2-7b-instruct-OpenDevin-merged_model
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

Model Name: Qwen2-7B-Instruct
Dataset: 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)

Running with Ollama

Install Docker: Ensure you have Docker installed on your machine.
Pull the Latest Hugging Face Transformers:

pip install transformers>=4.37.0
Set Up Your Workspace:

WORKSPACE_BASE=$(pwd)/workspace
Run the Docker Command: docker run -it
--pull=always
-e SANDBOX_USER_ID=$(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.

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Safetensors

Model size

8B params

Tensor type

BF16

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