MODEL PROFILE

Qwen3.8-27B-Uncensored-FIT-GGUF

SC117 · Qwen3.8-27B-Uncensored-FIT-GGUF

FIT-GGUF v0.1 ZERO-BYTE VERIFIED 14 TIERS · 7–13.5 GiB MTP HEAD REMOVED MEASURED KL + SAME-TOP APACHE-2.0 Qwen3.8-27B-Uncensored · FIT-GGUF Fourteen continuous-size GGUF quantizations — every file exactly its predicted size, every tier measured. 🧭 About FIT-GGUF — the tool behind these files Every file in this repository was planned, executed and verified by FIT-GGUF , an open-source, deterministic tensor-level planning layer on top of llama.cpp quantization presets. Standard GGUF quantization asks you to pick one of a handful of presets; FIT-GGUF starts from the largest supported preset below your requested byte budget, then spends the remaining bytes on deterministic tensor-level precision upgrades. Traditional GGUF gives you presets. FIT gives you a size slider. Deterministic size prediction & recipe execution ✅ Validated Universally optimal tensor allocation ⚠️ Not established — FIT claims precise size control, not a universal quality optimum The method, the full preregistered research record and the fit CLI are open source: github.com/Scorp1o117/FIT-GGUF ⚠️ Safety notice / 安全提示 The source model is an abliterated, refusal-removed model with no meaningful built-in guardrails, and may comply with harmful, illegal or unsafe requests. Use it only where you can provide appropriate moderation, access control and legal review. Do not deploy it to end users without your own safety layer. 源模型经过拒答方向移除,不具备可靠的内置安全护栏;请仅在合法、受控、具备审核与滥用防护的环境中使用,使用者自行承担部署责任。

Text generationlicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/30 02:15:29
OVERVIEW

Model overview

FIT-GGUF v0.1 ZERO-BYTE VERIFIED 14 TIERS · 7–13.5 GiB MTP HEAD REMOVED MEASURED KL + SAME-TOP APACHE-2.0 Qwen3.8-27B-Uncensored · FIT-GGUF Fourteen continuous-size GGUF quantizations — every file exactly its predicted size, every tier measured. 🧭 About FIT-GGUF — the tool behind these files Every file in this repository was planned, executed and verified by FIT-GGUF , an open-source, deterministic tensor-level planning layer on top of llama.cpp quantization presets. Standard GGUF quantization asks you to pick one of a handful of presets; FIT-GGUF starts from the largest supported preset below your requested byte budget, then spends the remaining bytes on deterministic tensor-level precision upgrades. Traditional GGUF gives you presets. FIT gives you a size slider. Deterministic size prediction & recipe execution ✅ Validated Universally optimal tensor allocation ⚠️ Not established — FIT claims precise size control, not a universal quality optimum The method, the full preregistered research record and the fit CLI are open source: github.com/Scorp1o117/FIT-GGUF ⚠️ Safety notice / 安全提示 The source model is an abliterated, refusal-removed model with no meaningful built-in guardrails, and may comply with harmful, illegal or unsafe requests. Use it only where you can provide appropriate moderation, access control and legal review. Do not deploy it to end users without your own safety layer. 源模型经过拒答方向移除,不具备可靠的内置安全护栏;请仅在合法、受控、具备审核与滥用防护的环境中使用,使用者自行承担部署责任。

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.

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Estimate monthly base token costs

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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。

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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。