Instructions to use mlx-community/DeepSeek-V4-Flash-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/DeepSeek-V4-Flash-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-V4-Flash-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 mlx-community/DeepSeek-V4-Flash-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-V4-Flash-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/DeepSeek-V4-Flash-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-V4-Flash-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
Is it possible to convert to a 2-bit quantized version?
Is it possible to convert to a 2-bit quantized version?
it is. Would you like to?
The Trade-Offs at a Glance
4-bit Quantization (The Sweet Spot): Delivers near-baseline model accuracy while cutting memory usage in half. It is the industry standard for local inference.
2-bit Quantization (The Extreme Limit): Maximizes memory savings to fit massive models on small hardware, but causes severe text degradation and "hallucinations."
Key Differences
Memory Footprint: 2-bit cuts the RAM/VRAM required by 4-bit exactly in half.
Perplexity (Accuracy): 4-bit retains high reasoning capabilities. 2-bit suffers a massive quality drop, often breaking logic and coding skills.
Use Case: Use 4-bit for daily production and reliable coding tasks. Use 2-bit only as a last resort to fit a giant model onto low-end hardware.
Hey, I recently came across a library which compresses model caching to 2 bit 1 bit so the size of kv caching will rediuce may be you can try this technique
may be you can try this