Instructions to use walissoncasonatto/Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use walissoncasonatto/Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="walissoncasonatto/Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("walissoncasonatto/Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP-GGUF", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use walissoncasonatto/Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "walissoncasonatto/Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "walissoncasonatto/Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/walissoncasonatto/Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP-GGUF

SGLang

How to use walissoncasonatto/Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "walissoncasonatto/Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "walissoncasonatto/Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "walissoncasonatto/Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "walissoncasonatto/Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use walissoncasonatto/Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/walissoncasonatto/Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP-GGUF
```

Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP-GGUF

NVFP4 quantization of huihui-ai/Huihui-Qwen3.6-27B-abliterated-MTP-GGUF with the MTP (Multi-Token Prediction) head preserved in Q4_K. Targeted at NVIDIA Blackwell consumer/edge GPUs (sm_120/sm_121) such as the RTX 5090.

The motivation: huihui-ai publishes Huihui abliterated GGUFs at Q2_K through Q8_0 (no NVFP4 variant exists). Standard Q-K quants don't hit Blackwell's native FP4 tensor cores. This conversion follows the recipe documented by s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF — body tensors in NVFP4 (GGML type 40) for tensor-core acceleration, MTP head in Q4_K for draft quality, norms/biases in F32.

Why NVFP4 on Blackwell

NVFP4 is NVIDIA's native 4-bit floating point format for Blackwell. Unlike integer quantization (Q4_K, Q5_K, etc.), NVFP4 uses block floating point with E4M3 scale factors and is dequantized directly by the GPU's tensor cores. The benefits:

Hardware-native dequantization — no integer-to-float conversion overhead
Lower memory bandwidth — body at ~4.6 BPW vs ~5.5 BPW (Q5_K) or ~6.6 BPW (Q8_0)
Acceptable quality — block scaling preserves more information than uniform 4-bit
Speed boost — measured ~20-30% over Q5_K_M on RTX 5090 at the same context

Source

Quantized from huihui-ai/Huihui-Qwen3.6-27B-abliterated-MTP-GGUF (Q8_0 variant, 29 GB) via llama-quantize --allow-requantize --tensor-type nvfp4 ... Q4_K.

huihui-ai/Huihui-Qwen3.6-27B-abliterated-MTP-GGUF itself is an abliterated derivative of Qwen/Qwen3.6-27B, with refusal directions zeroed out (see remove-refusals-with-transformers). The MTP head was preserved by huihui-ai in their GGUF release (published post-llama.cpp b9180 which added MTP convert/quantize support).

Files

File	Quant	Size	Notes
`Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP.gguf`	NVFP4 body + Q4_K MTP	~15-16 GB	Recommended for RTX 5090 / GB10 / RTX PRO 6000

Usage

Requirements

llama.cpp build with NVFP4 inference enabled (BLACKWELL_NATIVE_FP4=1 in system_info). Mainline b9180+ on a CUDA 13 toolkit + Blackwell GPU has this by default.
Build flags used during compile: -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=120 -DGGML_CUDA_FA_ALL_QUANTS=ON.

Server (Windows, copy/paste, adapt paths)

.\llama-server.exe `
  -m "Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP.gguf" `
  --spec-type draft-mtp `
  --host 0.0.0.0 --port 5001 `
  -ngl all --kv-unified -np 1 -b 2048 -ub 512 `
  --ctx-size 196608 `
  --cache-type-k q8_0 --cache-type-v q8_0 `
  --flash-attn on --cache-ram 0 --jinja --no-mmap --mlock `
  --reasoning on --reasoning-budget 8192 `
  --metrics `
  --temp 0.6 --top-k 20 --top-p 0.95 --min-p 0.0

Linux / DGX Spark

Same flags, drop the .exe and ^ line continuations. On GB10 (DGX Spark) also pass --no-mmap due to unified-memory mmap slowdowns.

Measured performance

Benchmarked on personal RTX 5090 (32 GB GDDR7, 1792 GB/s, sm_120a), Windows 11, CUDA 13.2, driver 596.36, llama.cpp mainline 0d18aaa9d1a8af3df9abccd828e22eeaac7f840b (May 26 2026), MTP --spec-type draft-mtp default n_max=3, Q8 KV cache, 196k context.

Quality — multi-seed 90/90 PERFECT

10 independent runs × 9 probes (q1q5 + reasoning) = 90/90 probes pass, 100%, zero retries needed. avgTPS during multi-seed: 93 t/s on q1q5, 95.9 t/s on reasoning suite.

Single-pass q1q5 smoke (representative timings)

5/5 q1q5 smoke PASS with --reasoning on --reasoning-budget 8192:

Probe	Tokens	TPS
Q1 Tool call (calculator)	106	70.4
Q2 Strict JSON extraction	347	97.8
Q3 Go `[]rune` UTF-8 reverse	1119	95.4
Q4 CRT reasoning (bat-and-ball trap)	8366	107.4
Q5 Long-prompt multi-section + FIM marker	2636	100.7

Throughput sweep (default n_max, `temperature=0.6`)

Workload	Tokens generated	MTP acceptance	TPS
Short code (palindrome function)	256	70.9%	88.9
Short prose (CAP theorem)	256	70.3%	92.5
Long-form tech (TCP vs UDP)	256	68.4%	89.2
Sustained long code (LRU cache class)	1024	68.5%	91.7

MTP acceptance is highly consistent (68-71% across all workload categories) — predictable performance regardless of prompt domain. Compare to the same model in Q5_K_M which swings 45-78% acceptance and 71-99 t/s depending on workload.

vs other variants on the same hardware

Variant	File size	Sustained TPS @ 1024	Quality	Notes
`s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF` (Qwen base, not Huihui)	14.64 GB	105 t/s peak (different bench)	90/90 multi-seed (2 retries)	Baseline Qwen quality
`huihui-ai/Huihui-...-Q5_K.gguf` (this model, Q5_K_M)	18.19 GB	75-85 t/s	5/5 q1q5	Highly workload-dependent
`Huihui-...-NVFP4-MTP.gguf` (this repo)	19.65 GB	91-107 t/s	5/5 q1q5	Best balance: abliterated quality + Blackwell-native speed + predictable acceptance

VRAM


Model on GPU	28.9 GB
Free for OS / display / margin	3.1 GB
Context capacity (q8 KV)	196k full + 32 token output budget — paged KV handles mixed sizes up to ~12× concurrency at 16k each

Limitations

Blackwell-only fast path. Will run on older NVIDIA GPUs via emulated dequantization (slow). For Ampere/Ada/older, use the standard quants from huihui-ai/Huihui-Qwen3.6-27B-abliterated-MTP-GGUF.
-np 1 required for MTP. Multi-token prediction speculative decoding currently requires single-parallel mode in llama.cpp.
--mmproj incompatible with MTP in mainline llama.cpp. Drop the vision projector if not needed (this is a text-only file regardless).
Abliteration tradeoffs. Refusal-direction surgery occasionally affects benign refusals (legal/safety information). Validate against your workload before production.

Credits

Model author: Qwen/Qwen3.6-27B (Alibaba Cloud / Qwen Team)
Abliteration + MTP-GGUF source: huihui-ai
NVFP4 + GGUF conversion recipe: s-batman/Qwen3.6-27B-NVFP4-MTP-GGUF
MTP support in llama.cpp: PR #22673

License

Apache 2.0, same as Qwen/Qwen3.6-27B. See LICENSE.

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GGUF

Hardware compatibility

4-bit

Model tree for walissoncasonatto/Huihui-Qwen3.6-27B-abliterated-NVFP4-MTP-GGUF

Base model

Qwen/Qwen3.6-27B

Quantized

huihui-ai/Huihui-Qwen3.6-27B-abliterated-MTP-GGUF