tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-fp16
The tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-fp16 is a 32 billion parameter dense transformer model, converted to MLX format for Apple Silicon optimization. Developed by the Swallow Project (Institute of Science Tokyo and AIST) based on Qwen3, it is a bilingual Japanese-English model. This model excels in both Japanese and English tasks, maintaining strong capabilities in mathematics and coding through Continual Pre-Training, Supervised Fine-Tuning, and Reinforcement Learning.
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Overview
This model, tocchitocchi/Qwen3-Swallow-32B-RL-v0.2-MLX-fp16, is an MLX-format conversion of the tokyotech-llm/Qwen3-Swallow-32B-RL-v0.2 model, specifically optimized for Apple Silicon. It is a 32 billion parameter dense transformer, provided in full precision (fp16) and has a size of approximately 61 GB.
Key Capabilities
- Bilingual Proficiency: Developed by the Swallow Project (Institute of Science Tokyo and AIST), it is a large language model proficient in both Japanese and English.
- Robust Training: Built upon Qwen3, the model underwent Continual Pre-Training (CPT), Supervised Fine-Tuning (SFT), and Reinforcement Learning (RL) to enhance its performance.
- Multifaceted Strengths: Achieves strong performance across language tasks while retaining capabilities in mathematics and coding.
- Apple Silicon Optimization: Converted to the MLX format, making it efficient for use on Apple Silicon hardware.
Usage
This model supports quick integration via Python using mlx_lm for generation, interactive chat through the command line, and can be run as an OpenAI-compatible server for broader client compatibility.