unsloth/Muse-Glimmer-30B

Muse Glimmer: Autonomous Agentic Model for Local Deployment

Muse Glimmer is a 30-billion-parameter causal language model from Meta Superintelligence Lab, designed specifically for autonomous agentic tasks on consumer hardware. It integrates a dedicated perception encoder for multimodal understanding, allowing it to process interleaved text and images. The model is optimized for local deployment, utilizing quantization techniques to run efficiently on devices with 24GB or 32GB VRAM, and features speculative decoding for faster generation.

Key Capabilities

• End-to-end Agentic Task Completion: Achieves strong success rates on benchmarks like DeepSearch QA, MCP-Atlas, and SWE-Bench, handling complex multi-turn requests.
• Reliable Tool Use & Multi-Step Reasoning: Manages a wide range of function calls and chains reasoning over long horizons for complex workflows.
• Failure Recovery: Diagnoses errors and retries failed tool calls instead of halting.
• Multimodal Input: Accepts interleaved text and images, enabling agents to interpret visual data like screenshots and charts.
• Optimized for Local Deployment: Quantized to run on consumer hardware with minimal degradation, and uses DFlash speculative decoding for up to 3.1x faster generation.
• Multilingual: Trained on data from over 100 languages.

Good For

• Local AI agents: Multi-step planning, sequential tool invocation, and long-horizon task execution on consumer devices.
• Coding agents: Writing, debugging, and resolving software engineering tasks.
• Multimodal reasoning: Interpreting visual information alongside text for agentic and information-rich environments.
• Synthetic data generation and LLM-as-a-judge evaluation.