Instructions to use mlx-community/DeepSeek-R1-Distill-Qwen-32B-Japanese-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/DeepSeek-R1-Distill-Qwen-32B-Japanese-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("mlx-community/DeepSeek-R1-Distill-Qwen-32B-Japanese-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 Settings
- LM Studio
- MLX LM
How to use mlx-community/DeepSeek-R1-Distill-Qwen-32B-Japanese-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 "mlx-community/DeepSeek-R1-Distill-Qwen-32B-Japanese-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/DeepSeek-R1-Distill-Qwen-32B-Japanese-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/DeepSeek-R1-Distill-Qwen-32B-Japanese-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
- Xet hash:
- ac83b1637e13e78c0cc474515c3c05fbfb0fd38563ebba1399159dfe1f0c91e1
- Size of remote file:
- 2.36 GB
- SHA256:
- d1aae126479afb6f47724e1d95391dd56b9ce1cf93ac42fd7ffcd56b4fdd9765
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