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 "TechxGenus/Seed-Coder-8B-Instruct-DWQ"
Run an OpenAI-compatible server
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "TechxGenus/Seed-Coder-8B-Instruct-DWQ"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "TechxGenus/Seed-Coder-8B-Instruct-DWQ",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'
Quick Links

Seed-Coder-8B-Instruct-DWQ

This model Seed-Coder-8B-Instruct-DWQ was converted to MLX format from Seed-Coder-8B-Instruct.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("TechxGenus/Seed-Coder-8B-Instruct-DWQ")
prompt = "write quick sort in python"
if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model size
1B params
Tensor type
BF16
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U32
·
MLX
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