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 "Otilde/Seed-OSS-36B-Instruct-Mixed_4_6-MLX"
Run an OpenAI-compatible server
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Otilde/Seed-OSS-36B-Instruct-Mixed_4_6-MLX"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Otilde/Seed-OSS-36B-Instruct-Mixed_4_6-MLX",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'
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Otilde/Seed-OSS-36B-Instruct-Mixed_4_6-MLX

This model Otilde/Seed-OSS-36B-Instruct-Mixed_4_6-MLX was converted to MLX format from ByteDance-Seed/Seed-OSS-36B-Instruct using mlx-lm version 0.28.4.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("Otilde/Seed-OSS-36B-Instruct-Mixed_4_6-MLX")

prompt = "hello"

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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Safetensors
Model size
36B params
Tensor type
BF16
·
U32
·
MLX
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