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ipetrukha
/
OpenCodeInterpreter-DS-6.7B-4bit

Text Generation
MLX
Safetensors
English
llama
code
conversational
Model card Files Files and versions
xet
Community
2

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"}
         ]
       }'
OpenCodeInterpreter-DS-6.7B-4bit
3.79 GB
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  • 1 contributor
History: 3 commits
ipetrukha's picture
ipetrukha
5259e8efd947c58701d9a790eb3cee4a9132084acdad61010ef9182cb5dc0267
05902e1 verified over 1 year ago
  • .gitattributes
    1.52 kB
    initial commit over 1 year ago
  • README.md
    658 Bytes
    5259e8efd947c58701d9a790eb3cee4a9132084acdad61010ef9182cb5dc0267 over 1 year ago
  • added_tokens.json
    458 Bytes
    5259e8efd947c58701d9a790eb3cee4a9132084acdad61010ef9182cb5dc0267 over 1 year ago
  • config.json
    792 Bytes
    5259e8efd947c58701d9a790eb3cee4a9132084acdad61010ef9182cb5dc0267 over 1 year ago
  • model.safetensors
    3.79 GB
    xet
    91a625de0abaa4bc4e4112c8970243bbfacf1646aea048a4955d65bd6743d44b over 1 year ago
  • model.safetensors.index.json
    52.4 kB
    5259e8efd947c58701d9a790eb3cee4a9132084acdad61010ef9182cb5dc0267 over 1 year ago
  • special_tokens_map.json
    462 Bytes
    5259e8efd947c58701d9a790eb3cee4a9132084acdad61010ef9182cb5dc0267 over 1 year ago
  • tokenizer.json
    1.37 MB
    5259e8efd947c58701d9a790eb3cee4a9132084acdad61010ef9182cb5dc0267 over 1 year ago
  • tokenizer.model
    500 kB
    xet
    5259e8efd947c58701d9a790eb3cee4a9132084acdad61010ef9182cb5dc0267 over 1 year ago
  • tokenizer_config.json
    5.26 kB
    5259e8efd947c58701d9a790eb3cee4a9132084acdad61010ef9182cb5dc0267 over 1 year ago
  • zero_to_fp32.py
    23.6 kB
    5259e8efd947c58701d9a790eb3cee4a9132084acdad61010ef9182cb5dc0267 over 1 year ago