Instructions to use tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-8bit 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("tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-8bit") 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
- Pi
How to use tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-8bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-8bit"
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": "tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-8bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-8bit 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 "tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-8bit"
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 tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-8bit
Run Hermes
hermes
- MLX LM
How to use tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-8bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-8bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-8bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-8bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
Qwen3-Swallow-32B-RL-v0.2-MLX-8bit
This model is an MLX format conversion of tokyotech-llm/Qwen3-Swallow-32B-RL-v0.2, optimized for Apple Silicon.
Model Details
| Attribute | Value |
|---|---|
| Original Model | tokyotech-llm/Qwen3-Swallow-32B-RL-v0.2 |
| Architecture | Dense Transformer |
| Parameters | 32B |
| Quantization | 8-bit quantization |
| Model Size | ~32 GB |
| Format | MLX (Apple Silicon optimized) |
| Converted with | mlx-lm v0.30.8 |
| License | Apache 2.0 |
About Qwen3-Swallow
Qwen3-Swallow is a bilingual Japanese-English large language model developed by the Swallow Project at the Institute of Science Tokyo (formerly Tokyo Institute of Technology) and AIST. Built upon Qwen3 through Continual Pre-Training (CPT), Supervised Fine-Tuning (SFT), and Reinforcement Learning (RL), it achieves strong performance on both Japanese and English tasks while maintaining capabilities in mathematics and coding.
For more details, see the original model card.
Usage
Quick Start (Python)
from mlx_lm import load, generate
model, tokenizer = load("tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-8bit")
messages = [{"role": "user", "content": "hello"}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True, max_tokens=512)
Interactive Chat
mlx_lm.chat --model tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-8bit
OpenAI-Compatible Server
mlx_lm.server --model tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-8bit --port 8080
Then connect with any OpenAI-compatible client at http://localhost:8080/v1.
Acknowledgments
- Original model by Swallow Project (Institute of Science Tokyo and AIST)
- MLX framework by Apple Machine Learning Research
- Conversion performed using mlx-lm
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Model tree for tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-8bit
Base model
Qwen/Qwen3-32B