Unsloth Enables Free Fine-Tuning of Muse Glimmer 30B on 24GB VRAM
danielhanchen · x · 2026-08-13
Unsloth has announced support for free fine-tuning and reinforcement learning (GRPO RL) on Meta's Muse Glimmer 30B model.
Performance Optimizations: Unsloth trains approximately 1.5x faster and uses 50% less VRAM compared to FA2 setups, with no accuracy loss.
Hardware Requirements: Through techniques like dynamic 4-bit BnB quantization, the model size is compressed from 56GB to 21GB. Fine-tuning the 30B model with QLoRA requires a minimum of 24GB VRAM, while LoRA needs >40GB. It can be run locally or via free Kaggle notebooks.
Model Capabilities: Muse Glimmer is a multimodal agentic model supporting vision, text, and audio inputs, designed for autonomous agents requiring planning, tool execution, and multimodal understanding.
Related event: Unsloth Enables Local Fine-Tuning for 30B Models(2 posts)→
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