license: apache-2.0 base_model: Blackfrost-AI/Qwen3.8-27B-ABLITERATED-BF16 base_model_relation: quantized library_name: gguf tags:

• gguf
• llama.cpp
• quantized
• mixed-quant
• abliterated
• uncensored
• 27b
• qwen3.8
• nvfp4

Qwen3.8-27B Blackfrost-Abliterated · Slot-Tuned Mixed 4/5-bit GGUF (~14.95 GiB)

TL;DR: The 15 GiB tier of our Blackfrost-lineage lineup. A hand-tuned per-slot mixed quant: sensitive tensor classes pinned high, bulk at q4_K. KLD 0.0387 vs the BF16 golden — better fidelity than our previous-gen 16.9G quant — at HardBattery 14/17, the same score as both our 9.8 GiB IQ3_XXS and the bigger daily driver. All numbers measured locally on the same harness (RTX 5090, llama.cpp b10930).

中文摘要：我们 Blackfrost 血统产品线的 15G 档。手工槽位调优混合量化（敏感槽位钉高、主体 q4_K）：对 BF16 黄金基线 KLD 0.0387——优于上代 16.9G 档——HardBattery 14/17，与 9.8G 的 IQ3_XXS 和更大的日常主档同分。所有数字均为本机同 harness 实测（RTX 5090 / llama.cpp b10930）。

Part of the collection Blackfrost-abliterated Qwen3.8-27B GGUF line (RTX 5090) — 9.8G speed tier · 15G fidelity tier · base weights · method sources.

Recipe (slot-tuned, not allocation-table)

This is the same quantization family as our daily driver: per-slot --tensor-type overrides on llama-quantize with an importance matrix — not a GSQ-RCO allocation quant (the allocation-transfer method is our 9.8 GiB tier).

It replaced our 16.0G v8 build at −1.05 GiB for +0.0007 KLD (0.0387 vs 0.0380) — the slot matrix said the bits were in the wrong places, and moving them was free quality.

Measured (RTX 5090 / llama.cpp b10930 / same harness for every cell)

Protocol notes:

• KLD is against saved BF16 golden logits (f16 KV, identical corpus/chunking for all quants) — a weight-fidelity indicator, not a quality ranking.
• Easy/HardBattery: knowledge quiz + open-ended generation + IFEval-style instruction following; and math with brute-force-verified ground truth + sandbox-executed coding + hard logic. HardBattery is the only battery that ever separated model generations for us — quantization recipes have never done so at these sizes.
• Replay: 30 real user prompts, paired across quants, temp 0, with the same BF16 speculative draft model.
• PPL: llama-perplexity, official wiki.test.raw, 100 chunks × 512 tokens; stderr is the tool's own error bar over chunks.

⚠ Requires an NVFP4-capable llama.cpp

This quant contains NVFP4 tensors (attention gate / SSM-out). You need a recent llama.cpp built with Blackwell-native FP4 (tested on b10930, CUDA). Running on non-Blackwell GPUs or older builds is not supported. If that's you, use our 9.8G GSQ-RCO quant — pure IQ/K formats, runs anywhere llama.cpp runs.

Honest limits

• The 450-case refusal-bench protocol has not been run on this specific quant (our 9.8G card has that data, including the Blackfrost official 17.8% anchor).
• Speed/draft numbers assume our BF16 DFlash speculative draft; acceptance varies by workload (code peaks, prose dips).
• 202K long-context is where our daily driver stays ahead (70.1 vs 62.4 tok/s needle decode) — pick by workload, not just size.

Usage

Standard GGUF — recent llama.cpp with NVFP4:

llama-server -m <this-model>.gguf --jinja -ngl 999 -c 65536

The chat template keeps the chain-of-thought channel, so keep API max_tokens ≥ 600.

Credits & license

• Base weights: Blackfrost-AI/Qwen3.8-27B-ABLITERATED-BF16
• Quantization method: slot-tuned mixed llama-quantize recipes; imatrix-assisted
• License inherits the base model (Apache-2.0 per Qwen lineage)

Disclaimer

This is an abliterated (refusal-reduced) model intended for local research and personal use. It will comply with harmful requests more readily than the base alignment intends. You are responsible for what you do with it.