Instructions to use bigatuna/Qwen3.5-9b-Sushi-Coder-RL-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bigatuna/Qwen3.5-9b-Sushi-Coder-RL-MLX 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("bigatuna/Qwen3.5-9b-Sushi-Coder-RL-MLX")

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
LM Studio

Pi new

How to use bigatuna/Qwen3.5-9b-Sushi-Coder-RL-MLX with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "bigatuna/Qwen3.5-9b-Sushi-Coder-RL-MLX"

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": "bigatuna/Qwen3.5-9b-Sushi-Coder-RL-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use bigatuna/Qwen3.5-9b-Sushi-Coder-RL-MLX 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 "bigatuna/Qwen3.5-9b-Sushi-Coder-RL-MLX"

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 bigatuna/Qwen3.5-9b-Sushi-Coder-RL-MLX

Run Hermes

hermes

MLX LM

How to use bigatuna/Qwen3.5-9b-Sushi-Coder-RL-MLX with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "bigatuna/Qwen3.5-9b-Sushi-Coder-RL-MLX"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "bigatuna/Qwen3.5-9b-Sushi-Coder-RL-MLX"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "bigatuna/Qwen3.5-9b-Sushi-Coder-RL-MLX",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Qwen3.5-9b-Sushi-Coder-RL-MLX

Lineage

Base model lineage: bigatuna/Qwen3.5-9b-Sushi-Coder
RL model: bigatuna/Qwen3.5-9b-Sushi-Coder-RL
MLX export source: this repo is a 4-bit MLX conversion of the RL checkpoint above
RL pipeline: NousResearch/atropos

Training

The upstream SFT model was trained with Unsloth on:

The RL stage was then run for coding with NousResearch/hermes-agent using NousResearch/atropos.

During that run, vLLM was patched with vllm-project/vllm PR #36395, fix(lora): add bounds checking for TP configurations, to address the LoRA tensor-parallel bounds issue.

Conversion

This repo contains an MLX export for Apple Silicon generated from the original Hugging Face safetensors checkpoint, not from the GGUF release.

Format: MLX
Quantization: 4-bit affine
Conversion stack: mlx-vlm

Files

model-00001-of-00002.safetensors
model-00002-of-00002.safetensors
model.safetensors.index.json
config.json
processor_config.json
tokenizer.json
tokenizer_config.json
generation_config.json
chat_template.jinja

Usage Note

This is an MLX multimodal export intended for Apple Silicon. Use it with mlx-vlm, not llama.cpp.

Quick Start

Install:

pip install -U mlx-vlm

Text generation:

mlx_vlm.generate \
  --model bigatuna/Qwen3.5-9b-Sushi-Coder-RL-MLX \
  --prompt "Write a Python function that parses a CSV file into dataclasses." \
  --max-tokens 512

Image + text:

mlx_vlm.generate \
  --model bigatuna/Qwen3.5-9b-Sushi-Coder-RL-MLX \
  --image /path/to/image.png \
  --prompt "Describe the bug in this screenshot and suggest a fix." \
  --max-tokens 512

Metadata

License: Apache-2.0
Architecture: Qwen 3.5
Format: MLX
Tags: mlx, mlx-vlm, apple-silicon, multimodal, code, rl

Downloads last month: 3,466

Safetensors

Model size

2B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for bigatuna/Qwen3.5-9b-Sushi-Coder-RL-MLX

Base model

bigatuna/Qwen3.5-9b-Sushi-Coder

Adapter

bigatuna/Qwen3.5-9b-Sushi-Coder-RL

Quantized

(2)

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Qwen3.5-9b-Sushi-Coder-RL-MLX