Instructions to use ipetrukha/OpenCodeInterpreter-DS-6.7B-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ipetrukha/OpenCodeInterpreter-DS-6.7B-4bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("ipetrukha/OpenCodeInterpreter-DS-6.7B-4bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- MLX LM
How to use ipetrukha/OpenCodeInterpreter-DS-6.7B-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "ipetrukha/OpenCodeInterpreter-DS-6.7B-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "ipetrukha/OpenCodeInterpreter-DS-6.7B-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ipetrukha/OpenCodeInterpreter-DS-6.7B-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
| language: | |
| - en | |
| license: apache-2.0 | |
| pipeline_tag: text-generation | |
| tags: | |
| - code | |
| - mlx | |
| # ipetrukha/OpenCodeInterpreter-DS-6.7B-4bit | |
| The Model [ipetrukha/OpenCodeInterpreter-DS-6.7B-4bit](https://huggingface.co/ipetrukha/OpenCodeInterpreter-DS-6.7B-4bit) was converted to MLX format from [m-a-p/OpenCodeInterpreter-DS-6.7B](https://huggingface.co/m-a-p/OpenCodeInterpreter-DS-6.7B) using mlx-lm version **0.16.1**. | |
| ## Use with mlx | |
| ```bash | |
| pip install mlx-lm | |
| ``` | |
| ```python | |
| from mlx_lm import load, generate | |
| model, tokenizer = load("ipetrukha/OpenCodeInterpreter-DS-6.7B-4bit") | |
| response = generate(model, tokenizer, prompt="hello", verbose=True) | |
| ``` | |