meta-models/Muse-Glimmer-30B
Muse Glimmer is a 30-billion-parameter causal language model developed by Meta Superintelligence Lab, featuring a dedicated perception encoder for multimodal understanding. Optimized for autonomous agentic tasks, it integrates multi-step reasoning, reliable tool use, and failure recovery, designed to run efficiently on consumer hardware. The model excels at end-to-end agentic task completion, coding, and multimodal reasoning, supporting local deployments with quantized versions for 24GB/32GB VRAM. It is multilingual, trained on over 100 languages, and includes speculative decoding for faster generation.
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Muse Glimmer-30B: Autonomous Agentic AI for Local Deployment
Muse Glimmer is a 30-billion-parameter causal language model from Meta Superintelligence Lab, specifically engineered for autonomous agentic tasks on consumer hardware. It integrates a dedicated perception encoder, enabling multimodal understanding by accepting interleaved text and images. The model is designed for local execution, offering capabilities like multi-step reasoning, reliable tool use, and robust failure recovery without cloud infrastructure.
Key Capabilities & Features
- End-to-end Agentic Task Completion: Achieves strong success rates on benchmarks like DeepSearch QA, MCP-Atlas, and SWE-Bench, demonstrating proficiency in code writing, debugging, and multi-turn request resolution.
- Multimodal Understanding: Processes text and images, allowing agents to interpret screenshots, charts, and documents.
- Optimized for Local Deployment: Utilizes 4-bit quantization to fit within 24GB or 32GB VRAM, with minimal degradation on agentic tasks. Includes a DFlash speculative decoding drafter for significantly faster text generation (e.g., 3.1x speedup on Nvidia RTX 5090).
- Reliable Tool Use & Failure Recovery: Handles diverse function calls with precise schemas and diagnoses/retries failed tool calls.
- Multilingual Support: Trained on data from over 100 languages.
- Controllable Effort: Supports varying reasoning strengths (low/medium/high/xhigh) for balancing quality and speed.
Ideal Use Cases
- Local AI Agents: For multi-step planning, sequential tool invocation, and long-horizon task execution on consumer devices.
- Coding Agents: Excels at writing, debugging, and resolving software engineering tasks.
- Multimodal Reasoning: Interpreting visual information alongside conversational context.
- Synthetic Data Generation & LLM-as-a-Judge: For developing and evaluating other models.