license: apache-2.0
base_model: DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU
pipeline_tag: text-generation
library_name: gguf
description: "Six GGUFs of DavidAU's Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU (TURBO Fable Cold Fusion Heretic Uncensored, 735/882): byte-identical 448-tensor native-NVFP4 backbone across all tiers with a per-tier quality ladder for lm_head / token_embd / MTP head. MTP speculative decoding baked into every file. Pairs with the original Qwen3.8 vision projector. Blackwell sm_120."
tags:

• gguf
• nvfp4
• qwen3.8
• qwen3.5
• blackwell
• mtp
• speculative-decoding
• turbo
• fable
• cold-fusion
• llama.cpp
  language:
• en
• multilingual

Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-NVFP4-GGUF

HF repos: esatapedico/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-NVFP4 (safetensors, compressed-tensors NVFP4) and esatapedico/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-NVFP4-GGUF (this GGUF family, 6 tiers).

A family of six GGUF files of DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU, DavidAU's TURBO 735-882 tune (Fable plus Cold Fusion plus Heretic/Uncensored, 735/882 Heretic merge and DPO layers) on Qwen3.8-27B: a 27B dense hybrid model (Gated DeltaNet plus Gated Attention every fourth layer, 262K native context, embedded MTP speculative head, native vision tower). NVFP4 here means W4A16 with FP8 scales, group size 16, weight only. Linear layers are NVFP4, vision tower, linear attention path, lm_head, embeddings and MTP head are kept in BF16 at the quantization source. The MTP head is baked into every file, no separate drafter is needed (--spec-type draft-mtp).

My part here is only the numerics: I converted the NVFP4 checkpoint to GGUF and built a size and precision ladder for the tensors that most affect output quality and decode speed. All credit for the model itself belongs upstream (full chain below).

Follow along & support

I post updates on new conversions, benchmarks, and what I'm working on over on Ko-fi. Follow along there to keep up with new releases and the work in progress. If you'd like to support more of it, a coffee is always welcome. I do this on consumer hardware and like seeing how far it goes. More is on the way.

☕ ko-fi.com/esatapedico. Updates, work-in-progress, and an optional coffee.

The six files

Every tier carries the same byte-identical 448-tensor native NVFP4 backbone and differs only in the precision of the LM head, token embedding, and MTP draft head:

Sizes are decimal GB as shown in the HF file browser. Picking a tier: MID-HIGH is the highest precision compact option (all three head groups at Q8_0) and our expected fastest compact decode on dual GPU split; LOW and VERY-LOW trade some head precision for about 2 GB less VRAM; HIGH and VERY-HIGH restore BF16 heads where VRAM allows. VERY-HIGH needs about 18 GiB VRAM plus KV, so single 16 GB cards will fall back to CPU offload.

Tensor layout

At a high level, the GGUFs contain 1,122 tensors. The language model backbone contributes a uniform 448-tensor NVFP4 block shared byte identical across all tiers. The NVFP4 format is W4A16 with group size 16 and FP8 E4M3 scales. Remaining tensors are F32 norms and scales, with lm_head, token embeddings and MTP head varying per tier as in the table above.

Vision

The TURBO 735-882 tune leaves the original Qwen3.8 vision tower untouched. Pair any tier with the matching vision projector mmproj-BF16.gguf via --mmproj. No mmproj is bundled in this repo, use the projector from the base model.

How this was made

At a high level, the steps were:

1. Converted the BF16 base model to compressed tensors NVFP4 (W4A16, vision, linear attention, lm_head and MTP kept in BF16, no calibration).
2. Verified NVFP4 metadata and ran a short vLLM smoke check.
3. Converted the NVFP4 checkpoint to GGUF.
4. Built each tier over a shared 448 tensor NVFP4 backbone, varying only lm_head, token embedding and MTP head precision per tier.
5. Verified per tier NVFP4 tensor count and backbone byte identity across all tiers, and patched GGUF KV for name, description and license without changing tensor data.

Benchmarks (naive, single GPU, not comparable across setups)

These are rough sanity checks to confirm the files load and generate, not a formal benchmark. Method and hardware are noted so you can interpret them in context. Results will vary with hardware, sampling and context length.

All runs used direct GPU via llama.cpp tools, with a diverse synthetic English payload (about 75 kB, 28 chunks times 512 context). No gateway cycle was used for these numbers.

Smoke: 6 of 6 coherent

Prompt What is a black hole? via single turn sampling, each tier produces a natural coherent completion with no repetition or truncation:

Perplexity (PPL)

Spread is 0.065 between best and worst, so the quantization costs almost nothing even at the smallest tier. VERY-LOW is highest as expected (Q3_K and Q2_K heads least precise).

Speed (llama-bench, pp512 prompt processing, tg128 generation, tok/s)

Single 5070 Ti (16 GB class) with full CUDA offload, three runs per tier:

The four compact tiers show flat prefill around 2700 tok/s and decode around 46 tok/s with less than 4 percent variance, consistent with an identical backbone and heads that are decode bound. HIGH and VERY-HIGH exceed single 16 GB VRAM and fall back to partial CPU offload on this single GPU bench, on dual GPU split or 24 GB cards they run at full GPU speed.

vLLM NVFP4 smoke

A short vLLM smoke check with tensor parallel 2 and 2048 context on the NVFP4 checkpoint passed with coherent output, confirming the checkpoint loads and generates before GGUF tier building.

SHA-256

c58f0a932957825cea5e6b73966ddf451f33b287668f670e1293a8ef132bf561  Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-NVFP4-VERY-LOW.gguf
6b2d5f6d5795ceef5a9dcc18a444ef9d03dd47b1d3bffaf80dd392f5a6cc425b  Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-NVFP4-LOW.gguf
1c9e7f3e77c6938fb0f1218ec0da9de93dcd7a83357e244d76e4ebff99e36058  Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-NVFP4-MEDIUM.gguf
4e1b87edfc2b7f58a78c657afe965344dea2415ae71264f93ecea1f94d4ac24f  Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-NVFP4-MID-HIGH.gguf
10f60fd0f0597e9d97641e6c77b637fc247e343fb39b84b145f17254dd588bc8  Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-NVFP4-HIGH.gguf
b08f4dcd1c5c48463c185746dd6ba594cae466c24c5e2a26628351dbadabc702  Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-NVFP4-VERY-HIGH.gguf

Attribution & provenance

This is a derivative work built entirely from existing Apache 2.0 artifacts. Nothing here was trained or fine tuned. Credit belongs to:

1. Alibaba and Qwen team for the base model, Qwen/Qwen3.8-27B (Apache 2.0): 27B dense, 64 blocks, Gated DeltaNet plus Gated Attention hybrid, native vision language, 262,144 token context, MTP head.
2. DavidAU for the tune itself, Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU (Apache 2.0): the TURBO 735-882 Fable plus Cold Fusion plus Heretic/Uncensored DPO stack that this family converts.
3. Unsloth, whose trainers and systems power the underlying training methods.
4. This repo's author for the GGUF conversion and the tier ladder only.

Repository contents

• Six tier GGUFs (table above, 15.15 to 19.33 GB)
• Corresponding safetensors NVFP4 checkpoint (single model.safetensors, compressed tensors nvfp4-pack-quantized): esatapedico/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-NVFP4

License

apache-2.0 (inherits from Qwen base and DavidAU tune). general.license = apache-2.0 is set inside every GGUF. general.name equals filename without extension, general.description mentions full base Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU, qwen35.block_count 65, qwen35.context_length 262144.

Card written with AI assistance.