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 "jedisct1/NextCoder-14B-q4-mlx"
Run an OpenAI-compatible server
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "jedisct1/NextCoder-14B-q4-mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "jedisct1/NextCoder-14B-q4-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'
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jedisct1/NextCoder-14B-mlx

This model jedisct1/NextCoder-14B-mlx was converted to MLX format from microsoft/NextCoder-14B using mlx-lm version 0.25.3.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("jedisct1/NextCoder-14B-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
15B params
Tensor type
BF16
·
U32
·
MLX
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