Instructions to use arthurcollet/OmniCoder-9B-mlx-mxfp8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arthurcollet/OmniCoder-9B-mlx-mxfp8 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("arthurcollet/OmniCoder-9B-mlx-mxfp8")

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 Settings
LM Studio

How to use arthurcollet/OmniCoder-9B-mlx-mxfp8 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "arthurcollet/OmniCoder-9B-mlx-mxfp8"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "arthurcollet/OmniCoder-9B-mlx-mxfp8"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use arthurcollet/OmniCoder-9B-mlx-mxfp8 with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "arthurcollet/OmniCoder-9B-mlx-mxfp8"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default arthurcollet/OmniCoder-9B-mlx-mxfp8

Run Hermes

hermes

MLX LM

How to use arthurcollet/OmniCoder-9B-mlx-mxfp8 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "arthurcollet/OmniCoder-9B-mlx-mxfp8"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "arthurcollet/OmniCoder-9B-mlx-mxfp8"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "arthurcollet/OmniCoder-9B-mlx-mxfp8",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

OmniCoder-9B-mlx-mxfp8

File size: 3,060 Bytes

9844b2a

{
    "architectures": [
        "Qwen3_5ForConditionalGeneration"
    ],
    "eos_token_id": 248044,
    "image_token_id": 248056,
    "model_type": "qwen3_5",
    "pad_token_id": 248055,
    "quantization": {
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        "bits": 8,
        "mode": "mxfp8"
    },
    "quantization_config": {
        "group_size": 32,
        "bits": 8,
        "mode": "mxfp8"
    },
    "text_config": {
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        "attention_dropout": 0.0,
        "attn_output_gate": true,
        "bos_token_id": null,
        "dtype": "bfloat16",
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        "head_dim": 256,
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        "intermediate_size": 12288,
        "layer_types": [
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        "mlp_only_layers": [],
        "mtp_num_hidden_layers": 1,
        "mtp_use_dedicated_embeddings": false,
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        "num_hidden_layers": 32,
        "num_key_value_heads": 4,
        "pad_token_id": null,
        "partial_rotary_factor": 0.25,
        "rms_norm_eps": 1e-06,
        "rope_parameters": {
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    "tie_word_embeddings": false,
    "transformers_version": "5.3.0.dev0",
    "unsloth_fixed": true,
    "video_token_id": 248057,
    "vision_end_token_id": 248054,
    "vision_start_token_id": 248053
}