license: apache-2.0 base_model: Qwen/Qwen3.8-27B base_model_relation: quantized pipeline_tag: text-generation library_name: gguf language:

• en
• zh tags:
• abliterated
• uncensored
• qwen3.8
• gguf
• dynamic-quantization
• ud-quants
• ud-3.0
• quantized
• llama.cpp
• red-teaming
• reasoning
• function-calling

Qwen3.8-27B-Uncensored Dynamic 3.0 (UD 3.0) GGUF Suite

Base Model License UD 3.0 11 Quants 9.99 to 27.05 GiB 3.18 to 8.61 BPW 262K context llama.cpp

What are Unsloth Dynamic 3.0 (UD 3.0) Quants?

Dynamic 3.0 (UD 3.0) is the modern standard for mixed-precision GGUF quantization pioneered by Unsloth AI (introduced for Qwen3.8 in August 2026). Rather than applying uniform bit-widths across an entire model, UD 3.0 uses architecture-aware tensor routing to protect the most sensitive mathematical structures while aggressively quantizing non-critical bulk parameters.

Why Standard Naive K-Quants Fail on Qwen3.8

Qwen3.8-27B is not a standard transformer. It is a 64-layer hybrid consisting of 48 linear-attention Gated DeltaNet layers and 16 full-attention layers (interleaved every 4 layers).

In Gated DeltaNet layers, sequence memory is maintained via a recursive recurrent state update:

$$S_t = (I - \beta_t K_t^\top) S_{t-1} + \alpha_t V_t Q_t^\top$$

• Standard K-quantization (naive Q2_K through Q5_K) crushes the recurrent gating matrices (ssm_alpha, ssm_beta) down to 2 to 4 bits.
• Because recurrence multiplies state updates recursively over sequence steps, this introduces a 7.5% to 21.8% RMS noise floor into every token transition. Over long contexts (32k–262k tokens), this error accumulates exponentially, causing context drift, hallucinations, and tool-calling breakdown.
• The UD 3.0 Solution: In this UD 3.0 suite, all recurrent gating tensors (ssm_alpha, ssm_beta, and in XL tiers ssm_out) are isolated and pinned to Q8_0 (and f32 for recurrence normalization). This enforces a constant high-precision 43.8 dB SQNR floor across all tiers, eliminating recursive decay noise.

The UD 3.0 Multi-Token Prediction (MTP) Strategy

Qwen3.8 includes a native Multi-Token Prediction (nextn) speculative head located at Layer 64 (~500 MB in BF16, ~146 MB in Q2_K).

• On hardware with 12 GB to 24 GB+ VRAM, retaining the MTP head provides built-in speculative decoding speedups without requiring external draft models.
• On strict 10 GB VRAM cards (such as RTX 3080 10GB or systems with 16 GB RAM), retaining this head pushes the model size to 10.13 GiB, triggering catastrophic OS swapping and CUDA out-of-memory errors.
• Following UD 3.0 design guidelines, this release provides both:
  • UD-Q2_K_XL-MTP (10.13 GiB) with MTP included for systems with adequate headroom.
  • UD-Q2_K_XL (9.99 GiB) with Layer 64 pruned, cleanly breaking the 10 GiB memory barrier.

Beyond Perplexity: Multi-Token Trajectory Divergence

Traditional quantization evaluation relies on single-token perplexity on Wikipedia text. However, as demonstrated by the Unsloth research team ("Accuracy is Not All You Need"), perplexity is easily gamed due to positive and negative error cancellation across individual tokens.

UD 3.0 evaluates models via KL Divergence and Divergence-300 @ 32 (greedy argmax decoding across 300 held-out prompts from Terminal-Bench 2.1, DeepSWE, and MathArena). This guarantees that multi-token reasoning trajectories and agentic tool-calling capabilities remain aligned with the full-precision baseline.

Empirical Drift Pareto Frontier

Below is the measured Pareto frontier across all 11 dynamic quant models, evaluating overall model reconstruction fidelity and demonstrating the elimination of recurrent gating noise:

Empirical Drift Pareto Frontier

Complete Model Specification Table

All metrics below are empirically measured via tensor-level dequantization audits against baseline ground truth:

Note on Recurrent Gating Fidelity: In standard naive quants, ssm_alpha and ssm_beta suffer from 7.5% (in Q4) to 21.8% (in Q2) RMSE. In this UD 3.0 suite, recurrent gating is pinned to Q8_0 across all tiers, capping error at the 0.82% Q8_0 mathematical precision barrier. Furthermore, our ssm_alpha and ssm_beta weights are 100% bit-for-bit identical (0.000% difference) to the official Unsloth Base UD-Q4_K_XL weights.

Cryptographic Verification (SHA-256)

Every model file in this repository has been validated bit-for-bit against remote Hugging Face LFS storage:

Hardware Sizing & Deployment Guide

1. 10 GB VRAM GPUs (e.g. RTX 3080 10GB, Mobile Workstations)

• Select: UD-Q2_K_XL (9.99 GiB)
• Why: Full in-VRAM execution with zero OS page swapping. Pruning Layer 64 eliminates 146 MiB of overhead, avoiding system memory thrashing.

2. 12 GB GPUs (e.g. RTX 3060 12GB, RTX 4070 12GB)

• Select: UD-Q3_K_XL-MTP (13.57 GiB) or UD-Q2_K_XL-MTP (10.13 GiB)
• Why: UD-Q2_K_XL-MTP fits 100% in VRAM with room for 32k context and built-in MTP acceleration. UD-Q3_K_XL-MTP delivers higher precision (21.1 dB SQNR) with light CPU offload (-ngl 56).

3. 16 GB GPUs (e.g. RTX 4080 16GB, RTX 4060 Ti 16GB, V100 16GB)

• Select: UD-Q4_K_S-MTP (14.75 GiB) or UD-Q4_K_M-MTP (15.67 GiB)
• Why: UD-Q4_K_S-MTP provides comfortable headroom for 8k–16k context buffers using --cache-type-k q8_0 --cache-type-v q8_0.

4. 24 GB GPUs (e.g. RTX 3090, RTX 4090, A10G, Apple Silicon 32GB)

• Select: UD-Q4_K_XL-MTP (16.36 GiB) (Gold Standard)
• Why: Allocating 16.36 GiB leaves 7.64 GiB of VRAM headroom on a 24 GB GPU for full 64k+ FlashAttention context buffers and concurrent speculative drafters. For shorter context tasks, step up to UD-Q5_K_XL-MTP (19.46 GiB) or UD-Q6_K-MTP (20.90 GiB) for near-lossless output.

5. 32 GB+ Workstations & Apple Silicon Unified Memory

• Select: UD-Q6_K_XL-MTP (21.81 GiB) or UD-Q8_K_XL-MTP (27.05 GiB)
• Why: Reference-grade fidelity achieving 36.4 dB to 44.2 dB SQNR.

Speculative Decoding Acceleration

Qwen3.8-27B supports dual speculative acceleration options:

1. In-Tree MTP Head (Next-N): Included in 10 of the 11 models (all -MTP files; excluded only in UD-Q2_K_XL). Enables multi-token speculative verification without downloading additional models.
2. External DFlash-2 Drafter: For higher speculative acceptance ratios with minimal VRAM overhead, the verified compatible drafter is HermiHg/Qwen3.8-27B-DFlash2-Q2_K_S-MIX-GGUF (Qwen3.8-27B-DFlash2-Q2_K_S-MIX.gguf, 0.52 GB). Running this drafter with q4/q4 KV cache fits comfortably alongside UD-Q4_K_XL-MTP within 24 GB VRAM:

llama-server \
  -m Qwen3.8-27B-Uncensored-UD-Q4_K_XL-MTP.gguf \
  -md Qwen3.8-27B-DFlash2-Q2_K_S-MIX.gguf \
  -ngl 99 -ngld 99 \
  --cache-type-k q8_0 --cache-type-v q8_0 \
  --cache-type-k-draft q4_0 --cache-type-v-draft q4_0 \
  -c 32768 -fa

About the Source Model & Abliteration

orcarouter/Qwen3.8-27B-Uncensored-FP8 is an abliterated (refusal-removed) build of Qwen/Qwen3.8-27B produced by OrcaRouter.

Abliteration follows Arditi et al. (2024): the refusal direction was isolated at layer 38 using harmful vs. harmless residual activations, then orthogonalized out from residual-writing projections (o_proj, out_proj, down_proj, embed_tokens). The MTP head was abliterated in parity.

⚠️ Disclaimer — Research Use Only

This model inherits the abliteration applied by OrcaRouter. Its refusal alignment has been substantially removed:

• It will comply with harmful, unethical, offensive, or illegal requests that the original Qwen3.8-27B would refuse.
• It is released strictly for legitimate research — interpretability, AI-safety study, red-teaming, robustness evaluation, and controlled experiments.
• You assume full responsibility and liability for every use and for everything it generates.
• Do not deploy to end users or in production without adding your own safety, moderation, and abuse-prevention layers.
• Use must comply with the Apache 2.0 License and all applicable laws and regulations.

License

Apache 2.0 — inherited from Qwen/Qwen3.8-27B.