MODEL PROFILE

Muse-Glimmer-30B-Abliterated-GGUF

The-freezer · Muse-Glimmer-30B-Abliterated-GGUF

> [!IMPORTANT] > ## Improvement update — August 15, 2026 > > This release now includes compact **abliterated Q4_K_M companions**: a **1.63 GB DFlash drafter** and a **1.40 GB multimodal projector**, matching Meta's consumer-hardware footprint while preserving this model's modified weights. The complete text quant ladder has also been refreshed with Meta's post-release Jinja correction, which normalizes `Reasoning effort` to `Reasoning strength` and prevents duplicate reasoning directives. Text generation, image input, and DFlash speculative decoding were validated together on current llama.cpp. > ## ✅ All quants live > > The full text quant ladder (`Q2_K` → `Q8_0`), compact Q4_K_M and full-precision vision projectors, and compact Q4_K_M and full-precision DFlash drafters are uploaded — see the **Files** tab. > ## ⚠️ EXPERIMENTAL > > Same-day arch, quantized. Expect sharp edges — decode, coherence, tool-parse, serve edge cases under load. Please open a Community discussion with loader/version, quant, prompt, sampling, and failure mode. Real repros get fixed faster.

Visual QAmultimodallicense:apache-2.0
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/10/30 01:32:21
OVERVIEW

Model overview

> [!IMPORTANT] > ## Improvement update — August 15, 2026 > > This release now includes compact **abliterated Q4_K_M companions**: a **1.63 GB DFlash drafter** and a **1.40 GB multimodal projector**, matching Meta's consumer-hardware footprint while preserving this model's modified weights. The complete text quant ladder has also been refreshed with Meta's post-release Jinja correction, which normalizes `Reasoning effort` to `Reasoning strength` and prevents duplicate reasoning directives. Text generation, image input, and DFlash speculative decoding were validated together on current llama.cpp. > ## ✅ All quants live > > The full text quant ladder (`Q2_K` → `Q8_0`), compact Q4_K_M and full-precision vision projectors, and compact Q4_K_M and full-precision DFlash drafters are uploaded — see the **Files** tab. > ## ⚠️ EXPERIMENTAL > > Same-day arch, quantized. Expect sharp edges — decode, coherence, tool-parse, serve edge cases under load. Please open a Community discussion with loader/version, quant, prompt, sampling, and failure mode. Real repros get fixed faster.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Source / channelInputOutputBilling unitCollected at
No verified prices yet. Check the source channel.

Estimate monthly base token costs

—

Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.

INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

View full indexed rankings →

Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

Are these real-time billing prices?

These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.

Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。