How to use from
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"}
     ]
   }'
Quick Links

mlx-community/DeepSeek-V4-Flash-4bit

Made possible by Lambda.ai ❤️

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/DeepSeek-V4-Flash-4bit")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
Downloads last month
23,082
Safetensors
Model size
284B params
Tensor type
BF16
·
U32
·
F32
·
U8
·
I64
·
MLX
Hardware compatibility
Log In to add your hardware

4-bit

Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Collection including mlx-community/DeepSeek-V4-Flash-4bit