eyes-ml/Muse-Glimmer-30B
Muse Glimmer-30B 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. This model is specifically designed for efficient local deployment on consumer hardware, supporting a 131,072+ token context length and multimodal input (text + image).
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Muse Glimmer-30B: Autonomous Agents on Consumer Hardware
Muse Glimmer-30B, developed by Meta Superintelligence Lab, is a 30-billion-parameter causal language model with a dedicated perception encoder, designed for autonomous agentic tasks. Distilled from Muse Spark, it focuses on local deployment on consumer hardware, integrating advanced capabilities without requiring cloud infrastructure.
Key Capabilities
- End-to-end Agentic Task Completion: Achieves strong success rates on benchmarks like DeepSearch QA, MCP-Atlas, and SWE-Bench, handling code debugging and multi-turn requests.
- Reliable Tool Use & Multi-Step Reasoning: Excels at invoking tools with precise schemas and chaining reasoning over long horizons for complex workflows.
- Failure Recovery: Diagnoses and retries failed tool calls or unexpected results.
- Multimodal Understanding: Accepts interleaved text and images via a dedicated perception encoder, enabling interpretation of screenshots, charts, and documents.
- Optimized for Local Deployment: Quantized to 4-bit precision, allowing the model to run efficiently on 24GB or 32GB VRAM consumer GPUs. Features speculative decoding with a DFlash drafter for significantly faster generation (e.g., 3.1x speedup on Nvidia RTX 5090).
- Multilingual Support: Trained on data from over 100 languages.
Benchmarks & Performance
Muse Glimmer-30B demonstrates strong performance in its size class against models like Gemma4-31B and Qwen3.6-27B across various agentic, coding, and multimodal benchmarks. It leads in categories such as MCP Atlas (75.5), DeepSearch QA (74.6), SWE-Bench Pro (51.2), and Charxiv Reasoning (78.8).
Intended Use Cases
- Local AI Agents: Multi-step planning, tool invocation, and task execution on consumer devices.
- Coding Agents: Writing, debugging, and resolving software engineering tasks.
- Multimodal Reasoning: Interpreting visual information alongside text for agentic environments.
- Synthetic Data Generation & LLM-as-a-Judge: Generating training data and evaluating other model outputs.