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 "tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-4bit"
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-4bit"
# 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-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'
Quick Links

Qwen3-Swallow-32B-RL-v0.2-MLX-4bit

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 4-bit quantization
Model Size ~17 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-4bit")

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-4bit

OpenAI-Compatible Server

mlx_lm.server --model tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-4bit --port 8080

Then connect with any OpenAI-compatible client at http://localhost:8080/v1.

Acknowledgments

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