Muse-Glimmer-30B-GGUF

Muse Glimmer 30B — llama.cpp GGUF quants

GGUF builds of meta-models/Muse-Glimmer-30B — Meta Superintelligence Lab's ~30B multimodal agent model, distilled from Muse Spark and purpose-built for autonomous agentic work on consumer hardware. Every file here was cut from the same BF16 source weights; the repo holds full-precision copies, a vision projector, and 29 quants from Q8_0 down to Q1_0. Quantized 2026-09-07.

These are llama.cpp GGUF files. They are not loaded by vLLM, SGLang or transformers the way the source repo is.

Which file should I grab?

• Default for most people: Q4_K_M (15.77 GB) — best quality-per-byte, in the same size class Meta targeted for mainstream local deployments.
• Quality first: Q8_0 (27.58 GB) is effectively lossless; Q6_K (21.3 GB) is near-lossless.
• Tighter on memory: IQ4_XS (14.29 GB) usually beats Q4_K_S at a smaller size; IQ3_M (11.94 GB) is the strong ~3.7 bpw pick; IQ2_M (9.17 GB) is the smallest size most people still find usable.
• Legacy Q4_0 / Q4_1 / Q5_0 / Q5_1 / Q2_0 / Q1_0 are included for completeness or older runtimes — prefer the K-quants otherwise.
• IQ2_XS, IQ2_XXS, IQ1_S, IQ1_M are extreme and experimental; expect substantial degradation.

About Muse Glimmer

Muse Glimmer is a 30B-class dense causal transformer with a dedicated perception encoder (image + text input, text output), trained for the capabilities an autonomous agent needs working together:

• End-to-end agentic task completion — works inside scaffolds, writes and debugs code, resolves multi-turn requests from start to finish.
• Reliable tool use — precise schema-based function calls across extended workflows.
• Multi-step reasoning and failure recovery — sustains long-horizon plans; when a tool call fails, diagnoses and retries instead of halting.
• Multimodal input — interleaved text and images: screenshots, charts, documents; up to 4,096 visual tokens per image.
• Controllable effort — reasoning strength set in the system prompt (low / medium / high / xhigh).
• Multilingual — trained on data from more than 100 languages.

Benchmarks reported by Meta (High Reasoning; compared against Gemma4-31B and Qwen3.6-27B Thinking Mode):

Full tables and methodology are on the source model card and Meta's evaluation report.

Note on speed: Meta ships a DFlash speculative-decoding drafter with the source release (up to 3.1× faster generation on an RTX 5090 in their measurements). That drafter is not part of this GGUF repo — it contains only the main model and its vision projector.

Files

Full precision and the vision projector:

Quantized (largest → smallest):

32 files, ~483 GB total.

Running

# one-off prompt
llama-cli -hf NANI-Nithin/Muse-Glimmer-30B-GGUF:Q4_K_M -p "Hello"

# OpenAI-compatible server
llama-server -hf NANI-Nithin/Muse-Glimmer-30B-GGUF:Q4_K_M

# download just one file
huggingface-cli download NANI-Nithin/Muse-Glimmer-30B-GGUF Muse-Glimmer-30B-Q4_K_M.gguf --local-dir .

# run a local file
llama-cli -m Muse-Glimmer-30B-Q4_K_M.gguf -p "Hello"

For image understanding, also download mmproj-Muse-Glimmer-30B-F16.gguf and load it as the projector with --mmproj.

Recommended settings (from Meta)

• temperature = 1.0, top_p = 0.95, top_k = 64
• Reasoning strength in the system prompt: Reasoning strength: <low | medium | high | xhigh>. Use high or xhigh for complex problem solving, coding, and agentic tasks.

Architecture

From the source model card:

Limitations

From the source card:

• May produce inaccurate, biased, or objectionable responses; may still err in multi-step reasoning, especially in novel scenarios.
• Not explicitly optimized for video (video is processed as individual frames). Audio input/output is not supported.
• Quantized inference may show minor quality differences in edge cases compared to full precision — applies to every file in this repo.
• Not intended to be downloaded by or used by individuals under 18.
• Meta recommends deploying the model as part of a system with additional guardrails — in particular human-in-the-loop confirmation for irreversible actions in agentic contexts — rather than as an endpoint in itself.

Licence

Apache 2.0, same as the source model. Meta's usage policy is linked from the source repo.