Purpose-built, not benchmark-chasing. This quantization exists for one reason: to squeeze the maximum possible performance out of one specific machine — the author's RTX 5060 Ti 16 GB (eGPU) + Ryzen 5 9600X workstation. It is not an attempt to outdo other quants, base models, or fine-tunes, and it makes no such claim. Every number below is a measurement on that single machine, published as-is.

This model has had its alignment-based refusals removed (abliterated upstream base). It will respond to prompts the stock model refuses, including harmful ones. You are the safety layer. Do not expose it to untrusted input sources, and do not connect it to autonomous tooling without understanding what that means.

Qwen3.8-27B-uncensored-IQ4_XS — uncensored, domain-calibrated, ASCII-pruned

An uncensored, domain-calibrated, vocabulary-pruned GGUF of Qwen3.8-27B (27.8B, hybrid Gated-DeltaNet + gated attention): the abliterated base produced by orcarouter, re-quantized through a measured domain-calibration pipeline (~1M-token SEC-filings/finance importance matrix blended with a general corpus), then pruned to English + accented Latin + math/typography symbols (141,141 of 248,320 tokens). Quality gates run against the BF16 reference on the same engine and data.

Requires a recent llama.cpp build with Qwen3.5-family (hybrid Gated DeltaNet) and MTP support (main after 2026-08-27; the PRs are linked below). On Blackwell GeForce cards, build with CMAKE_CUDA_ARCHITECTURES=120 and verify the smoke test — see Requirements.

English and accented-Latin text only. Scripts outside the kept set (CJK, Arabic, Cyrillic, Thai, …) fall back to one token per UTF-8 byte and the model reads them as garbled text, because it was never trained on those byte sequences. If you need multilingual text, use a full-vocabulary quant instead. See Limitations.

What makes this quant different

Three things, all measured rather than claimed:

1. The abliteration is gentle. The upstream base was ablated with directional-ablation methods while preserving quality — verified before building on it: 0/3 refusal probes answered with refusals (all produced real content), and generic-text fidelity of the full-vocab sibling against the BF16 reference measured RMS Δp 4.33% / top-p agreement 93.9% (E4 revision: token embedding at q4_K — parity with the best community calibration on both axes, 2026-09-19).

2. The importance matrix is domain-calibrated. It blends a general corpus with ~1M tokens of SEC-contract extraction and financial-analysis tasks, chat-template-formatted to match real serving traffic. On a 59-task domain benchmark, this quant scores 93.2% (55/59) vs 89.8% for the best community calibrations of the same model at the same bit rate — a +3.4-point controlled A/B, isolated to the imatrix.

3. The vocabulary is pruned with verification. 141,141 tokens survive (ASCII + Latin-1/Latin-Extended-A accents + math/typography symbols + specials + byte-fallback). Rows are gathered in quantized space — no dequantize/requantize, every kept row is bit-identical to the source. All 864 non-vocab tensors (including the MTP head) verified byte-identical. The prune buys −0.65 GiB, which on a 16 GB card is the difference between 64K context fitting and not fitting.

Benchmark	This quant (this repo)	Full-vocab build (same recipe)	Unsloth UD-IQ4_XS
59-task domain harness (temp 0)	55/59 (93.2%)	55/59 (93.2%)	53–54/59 (89.8–91.5%)
115-task extended suite @32K MTP	92/115	91–92/115	91/115
127-task suite v4 @32K (2026-09-20)	100/127 == predecessor with zero task flips	—	—
same, thinking @high effort	105/127 (+5 vs thinking-off)	—	—
KL RMS Δp / top-p vs BF16	see full-vocab build	4.33% / 93.9% (E4)	4.35% / 93.9%
Refusal probes	0 refused	—	refused

(Duplicate harness runs reproduced these scores exactly.)

Variants in this family

This card documents the served daily — the E4-ASCII revision (2026-09-20). Both variants ship in this repository:

Variant	File	Vocabulary	Size (on disk)	Suite gate	Notes
Served daily (this card)	Qwen3.8-27B-uncensored-IQ4_XS.gguf	141,141 — ASCII + Latin-1/Latin-Extended-A accents + math/typography symbols (same keep-set as the censored sibling)	13.58 GiB	100/127 @32K MTP, zero task flips vs predecessor; 105/127 at high effort	E4 revision: q4_K embedding; the predecessor E3-ASCII build remains retrievable in this repo's revision history
Full-vocabulary anchor (also in this repo)	Qwen3.8-27B-uncensored-IQ4_XS-fullvocab-E4.gguf	248,320 — multilingual (en/zh) intact	14.32 GiB	91/115 @32K	the fidelity reference: KL 4.33% RMS / 93.9% top-p vs BF16 — parity with the best community calibration on both axes

KL divergence against saved BF16 logits is structurally undefined for the pruned variant (logit dimensions differ — see Fidelity); the 4.33% / 93.9% figures belong to the full-vocab anchor and bound the shared quantized body. The stock-alignment sibling of this family is published separately.

Fidelity

• Kept-vocabulary rows are bit-identical to the source quant — the prune cannot change behavior on text the kept tokens can represent.
• Standalone perplexity (full wiki validation corpus, identical settings): 6.4248 ± 0.04. External full-file anchors for this architecture: 6.744 (BF16) and ≈6.77 (community IQ4XS). A vocab prune slightly _raises PPL mechanically (the softmax renormalizes over fewer candidates) — the sibling measurement bounds this effect at ≈+0.2%.
• KL divergence against saved BF16 logits is structurally undefined for a pruned-vocab model (logit dimensions differ) and was not faked.

Quantization recipe

Source: orcarouter F16 (abliterated) — then vocabulary pruned (rows gathered in quantized space). Per-tensor layout over a flat IQ4_XS body:

Tensor class	Type	Rationale
Body (attn, FFN, GDN projections)	IQ4_XS	bandwidth-optimal on this GPU (448 GB/s class)
token_embd	q4_K — the E4 revision (2026-09-20)	KL-guided search found the embedding tier closes the residual fidelity gap (full-vocab E4: 4.33% RMS / 93.9% top-p = parity on both axes)
ffn_down	IQ4_XS — kept high	measured KL driver at this size point; demoting it cost 0.8–0.9 top-p in A/B
MTP / nextn (blk.64, 15 tensors)	embedded, quantized with the body	enables native speculative decoding
Importance matrix	bartowski general + ~1M-token SEC-finance corpus, importance donor IQ4_XS	see the calibration study below

• Size: 13.58 GiB (~1.1B parameters removed from token_embd + output; +0.09 GiB vs the E3 revision buys the q4_K embedding — the measured fidelity upgrade)

• MTP: the 15 blk.64 tensors are embedded and verified byte-identical — speculative decoding works out of the box

• Tokenizer: specials, all 256 byte-fallback tokens, and partial-UTF-8 fragments retained; special ids remapped; merges verified to reference only kept tokens in original priority order

Serving (measured config)

llama-server -m Qwen3.8-27B-uncensored-IQ4_XS.gguf \
  --spec-type draft-mtp --spec-draft-n-max 3 \
  -ngl 99 -c 32768 -np 1 -fa on -ctk q4_0 -ctv q4_0 \
  -b 2048 -ub 512 -t 6 --jinja --cache-reuse 256 \
  --host 127.0.0.1 --port 8080 --metrics --slots

Measured on the development hardware (RTX 5060 Ti 16 GB, eGPU, custom SM120/Zen 5 build):

Metric	Value
Generation (batch 1, MTP d3)	59.5 t/s decode at ~28K position; 0.87–0.89 mean draft acceptance
Generation (plain)	~26 t/s short context; 21.0 t/s at 64K native
Prompt processing	~830–875 t/s at depth
VRAM	15.4 GiB (32K + MTP); 15.2 GiB (64K, no MTP)

• Context: 64K fits natively at q4 KV — on this card the unpruned sibling of the same recipe OOMs at 64K. 96K+ requires KV-cache streaming (out of scope here).
• Vision: the stock Qwen mmproj projector works with this quant (CPU-encode recommended on 16 GB). With the projector loaded, disable MTP — the two do not fit together reliably at 16 GB. Quantized projector variants (Q8_0 / Q4_0 / a merger-upgraded Q4_0 mix) ship in our mmproj repository, all measured at parity with F16 on objective and real-image batteries — and the 2026-09-20/21 factorial sweep proved vision exactly quality-neutral on the 127-task suite (every vision cell identical to its text twin, cell for cell).
• Recommended chat template: froggeric/Qwen-Fixed-Chat-Templates v22.5 — A/B measured on this family: identical harness pass rate, −27% reasoning tokens at xhigh effort, tool-calling unaffected, and it renders mid-conversation system messages that the stock template rejects.
• Sampling (per the Qwen3.8 model card): thinking mode — temp 1.0, top_p 0.95, top_k 20; non-thinking — temp 0.7, top_p 0.80, top_k 20, presence_penalty 1.5. Thinking toggles per request via chat_template_kwargs: {"enable_thinking": true|false}; for short answers disable thinking and keep max_tokens ≥ 256.
• OpenAI-compatible: /v1/chat/completions and /v1/completions work as expected; tool calling is clean (parallel calls included).

Context behavior (measured on this architecture)

The hybrid Gated-DeltaNet architecture has a tiny KV cache (16 of 64 layers carry attention KV), and the family has a measured decode-throughput decay above 80K of context position (upstream issue #27623): 32K ≈ 21–23 t/s plain, 64K ≈ 20 t/s, 128K ≈ 7 t/s, 232K ≈ 5 t/s. Recommendation: 32K in the MTP config for daily work; retrieval/summaries beat ever-growing context past ~80K of position. KV-cache streaming runtimes lift this further: with a community adaptive-KV-streaming fork this exact file serves 128K contexts (14.8 t/s at 105K position) and its 115-task scores stay flat out to 252K — measured on our 16 GB card; ships with those forks, not stock llama.cpp.

Best measured configuration by context window (this file, 16 GB card):

Context	Best config	Engine	Decode
8–12K	vision + MTP d3 (Q4_0 projector) — the only window where both co-exist	stock	~50–70 t/s
16–32K	text + MTP d3 (daily) — or vision-first (Q8_0 projector; MTP auto-off)	stock	~67–70 t/s text · ~27 vision
48–64K	text-only, q4_0 KV, no MTP (the MTP draft mirrors the window and OOMs past 32K)	stock	~21–27 t/s
96K	text-only, kvarn4/4 KV (variance-normalized)	beellama fork	~20 t/s @77K position
128–252K	text-only, KV-stream arena 1536 MiB, q8_0 K / q4_0 V	kv-stream fork	~14.8 t/s @105K · quality flat to 252K

Using the >64K engines — both are llama.cpp forks; pick by context window:

kv-stream fork (RaymondHuang210129/llama.cpp-adaptive-kv-streaming, branch feature/kv-stream-phase-arena) — for 128K–252K. Build with the fork's pre-rename FA flag (-DGGML_CUDA=ON -DGGML_CUDA_FA_ALL_QUANTS=ON -DCMAKE_CUDA_ARCHITECTURES=120), then serve 128K+ from a ~15 GiB footprint:

llama-server -m Qwen3.8-27B-<variant>.gguf -ngl 99 -c 131072 -np 1 -fa on \
  -ctk q8_0 -ctv q4_0 -b 512 -ub 512 --kv-stream-arena-mib 1536

Text-only, no MTP (the shared arena rejects speculative batches), no --cache-reuse. Non-resident KV pages live pinned in host RAM — budget ~6 GiB of system memory at 252K.

beellama fork (Anbeeld/beellama.cpp) — for 96K native, no streaming machinery, with its variance-normalized KV quant:

llama-server -m Qwen3.8-27B-<variant>.gguf -ngl 99 -c 98304 -np 1 -fa on \
  -ctk kvarn4 -ctv kvarn4 -b 2048 -ub 512

Keep speculative decoding off on hybrid-GDN models with this fork (its prompt-cache rollback path is measured-unsafe with spec until their fix ships), and treat 96K as the ceiling — deeper contexts fit at load but OOM on real long prompts.

Requirements

• llama.cpp with Qwen3.5-family hybrid architecture support (merged upstream 2026-08-27 or later) and MTP speculative decoding (PR #22673)
• ~15.4 GiB VRAM for the 32K MTP config; 16K ctx runs comfortably alongside other GPU apps

Limitations

• Non-Latin scripts break (see the warning above). Accented Latin (é, ü, ñ) is kept — European names in financial documents survive. (Correction, 2026-09-17: an earlier 1×1-pixel result suggested a projector-quantization effect; follow-up testing disproved it — all projector levels fail degenerate images identically. It is a model-family artifact, not a projector effect.)
• The domain benchmark is a custom suite built for this deployment — not a public academic benchmark; other domains will see different (likely smaller) gains.
• The upstream abliteration method is unpublished (gated repository); its quality was validated by our gates rather than by method disclosure.
• The MTP draft head was not itself ablated; draft acceptance may dip on sensitive completions (greedy verification keeps output lossless).
• Decode throughput decays past ~80K of context position (upstream issue); this is an architecture/runtime limitation, not specific to this quant.
• Not a general-purpose uncensored model for public deployment — alignment removal is total, and the security implications of that are yours.

Calibration study summary

Three imatrix variants of the identical recipe were built and gated (duplicate runs reproduced every score):

Variant	Domain corpus	Domain harness (59 tasks)	wiki KL RMS / top-p
General only	none	52/59 (88.1%)	—
General + 377K finance	SEC 377K	54/59 (91.5%)	4.93 / 91.7
General + ~1M finance (this family)	SEC ~1M	55/59 (93.2%)	5.10 / 91.8 (V-C layout) → 4.33 / 93.9 (E4 layout)
Unsloth UD-IQ4_XS reference	Unsloth general	53/59 (89.8%)	4.35 / 93.9

Domain calibration scales monotonically with corpus size. A Q8-donor importance matrix was tested as a control and showed no measurable difference (negative result, documented).

Acknowledgements

• Qwen team — the base model (Apache-2.0) and architecture.
• orcarouter — the abliterated full-precision base of the uncensored lineage.
• bartowski — the general-tier calibration corpus and the per-tensor layout methodology this family's sensitivity work builds on.
• Unsloth — the UD-IQ4_XS reference quant used as the comparison baseline, and the MTP-bearing BF16 repack used as a quantize source.
• bsaleh03 — the ASCII-Condensed vocabulary-pruning toolchain (audit → prune → verify with policy replay).
• froggeric — the Qwen-Fixed-Chat-Templates jinja template (v22.5), the measured A/B winner this family serves with.
• ggml-org / llama.cpp — the runtime, the hybrid-architecture support, and MTP speculative decoding.

License

Apache-2.0, inherited from the base model. The SEC-contract calibration corpus is derived from public SEC filings (EDGAR); users are responsible for compliance with their own use case, and — this being an alignment-removed model — for every output it produces.