How to use from
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-4bit"
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-4bit
Run Hermes
hermes
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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