Instructions to use HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ2_M
# Run inference directly in the terminal:
llama cli -hf HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ2_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ2_M
# Run inference directly in the terminal:
llama cli -hf HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ2_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ2_M
# Run inference directly in the terminal:
./llama-cli -hf HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ2_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ2_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ2_M

Use Docker

docker model run hf.co/HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ2_M

LM Studio
Jan

vLLM

How to use HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ2_M

Ollama
How to use HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF with Ollama:
```
ollama run hf.co/HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ2_M
```

Unsloth Studio

How to use HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF to start chatting

How to use HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ2_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ2_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ2_M
```

Lemonade

How to use HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ2_M

Run and chat with the model

lemonade run user.Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF-IQ2_M

List all available models

lemonade list

Hermes Agent

How to use HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ2_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ2_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ2_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ2_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP

HauhauCS FastMTP: up to 3.02x document TG and 1.93x reasoning TG versus non-MTP — plus up to 35.2% more document TG and 21.1% more reasoning TG than standard embedded MTP.

Join the Discord for updates, roadmaps, projects, or just to chat.

Qwen3.8-27B uncensored by HauhauCS 0/465 Refusals* .

This is the Aggressive variant: direct answers, no refusal behavior, and minimal preamble on hard prompts.

Every text GGUF preserves Qwen3.8's native NextN head, and this release adds HauhauCS FastMTP: a specific acceleration sidecar qualified across the complete quant lineup at maximum native context. Vision is included through the separate BF16 projector.

Hugging Face's Hardware Compatibility widget may not recognize K_P quants. If files appear to be missing, click View variants or open Files and versions.

About

No changes to datasets or intended capabilities. This release preserves Qwen3.8-27B's text, reasoning, agentic, image, and video capabilities while applying the HauhauCS Aggressive uncensoring profile.

Pick Aggressive when you specifically want the model to get to the answer without first talking itself into compliance. For reliability-critical, specifically long-context agentic work, a Balanced release is normally the safer default when/if one is available.

Downloads

File	Quant	BPW	Size
Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-Q8_K_P.gguf	Q8_K_P	9.21	31.46 GB
—	Q8_0	8.50	—
Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-Q6_K_P.gguf	Q6_K_P	7.59	25.92 GB
—	Q6_K	6.60	—
Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-Q5_K_P.gguf	Q5_K_P	5.92	20.22 GB
—	Q5_K_M	5.70	—
Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-Q4_K_P.gguf	Q4_K_P	5.25	17.92 GB
—	Q4_K_M	4.88	—
Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-IQ4_XS.gguf	IQ4_XS	4.60	15.71 GB
Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-Q3_K_P.gguf	Q3_K_P	3.93	13.44 GB
—	Q3_K_M	3.90	—
Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-IQ3_M.gguf	IQ3_M	3.74	12.79 GB
Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-IQ3_XS.gguf	IQ3_XS	3.56	12.18 GB
Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-Q2_K_P.gguf	Q2_K_P	3.12	10.68 GB
Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-IQ2_M.gguf	IQ2_M	3.02	10.32 GB
mmproj-Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-BF16.gguf	Vision projector	—	931 MB
Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-FastMTP-32K.gguf	HauhauCS FastMTP	—	903 MB

BPW is the encoded tensor-payload average across the complete text model, including its embedded MTP tensors, rounded to two decimals. The projector and FastMTP sidecar work with every text quant; download the projector only for image or video input.

What are K_P quants?

K_P ("Perfect") quants are HauhauCS custom quantizations that use model-specific analysis to selectively preserve quality where it matters most. Every model gets its own optimized quantization profile.

A K_P quant effectively bumps quality up by one or two quant levels at only around 5–15% more size than the base quant. The files remain standard GGUFs and work with llama.cpp, LM Studio, and other GGUF-compatible runtimes with no special build or plugin.

Note: K_P quants may show as ? in LM Studio's quant column. This is a display issue only—the model loads and runs normally.

Specs

Dense 27B causal language model with a vision encoder
64 language-model layers
Hidden size 5,120; FFN size 17,408
248,320-token padded vocabulary
48 Gated DeltaNet layers and 16 gated-attention layers
Native embedded MTP/NextN preserved, plus the HauhauCS FastMTP 32K acceleration profile
262,144-token native context; extensible up to 1,000,000 with framework-specific configuration
Native text, image, and video understanding
Based on Qwen/Qwen3.8-27B

What is HauhauCS FastMTP?

HauhauCS FastMTP is the custom, variant-specific acceleration profile built for this exact Aggressive release: a compact 32K draft sidecar and per-quant serving profiles qualified for TG, acceptance, maximum native context, and VRAM.

It delivers up to 3.02x document TG and 1.93x reasoning TG versus non-MTP, plus up to 35.2% more document TG and 21.1% more reasoning TG than the standard embedded-MTP profile. The unchanged full target verifies every drafted token, so FastMTP accelerates generation without replacing the target model or changing its answers. The construction and selection methodology is exclusive to HauhauCS releases.

The benchmark ladder:

Comparison	Document TG	Reasoning TG	Scope
Standard embedded MTP vs MTP disabled	2.23x (`+123.4%`)	1.60x (`+59.6%`)	Final Q8_K_P, depth 2
HauhauCS FastMTP profile vs standard embedded MTP	+35.2%	+21.1%	Final Q8_K_P, depth 3 vs depth 2
HauhauCS FastMTP vs embedded MTP at identical depth	+11.1%	+18.2%	Final Q8_K_P, depth 3
HauhauCS FastMTP vs MTP disabled	3.02x (`+202.0%`)	1.93x (`+93.3%`)	Final Q8_K_P service

These results were measured on one RTX PRO 6000 Blackwell 96 GB per isolated lane at 204800 configured context, full CUDA offload, --no-mmap, and the official reasoning sampler. FastMTP accelerates TG; PP is reported alongside it for a complete serving comparison.

There are two acceleration paths:

Embedded MTP: use any target GGUF by itself with --spec-type draft-mtp in a current upstream llama.cpp build.
HauhauCS FastMTP: pair that same target with Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-FastMTP-32K.gguf and the HauhauCS runtime patch below.

Run HauhauCS FastMTP

The compact draft uses a standard GGUF d2t token map plus a minimal Qwen3.8 runtime consumer. Build it once. The example below uses CUDA; for ROCm/HIP or Vulkan, replace -DGGML_CUDA=ON with -DGGML_HIP=ON or -DGGML_VULKAN=ON. For CPU-only, omit the backend flag.

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
git checkout 4df29be4f4c3673f428170fda944a5b19f743bb8

curl -L -o HauhauCS-FastMTP-llama.cpp.patch \
  https://huggingface.co/HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF/resolve/main/HauhauCS-FastMTP-llama.cpp.patch
git apply --check HauhauCS-FastMTP-llama.cpp.patch
git apply HauhauCS-FastMTP-llama.cpp.patch

cmake -S . -B build -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build --config Release -j"$(nproc)"

If draft loading reports expected 5120, 248320, got 5120, 32768, the FastMTP sidecar is correct but the executable is unpatched. Launch the freshly built ./build/bin/llama-server from this checkout.

Then serve any target quant with the one shared FastMTP sidecar:

MODEL=Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-Q4_K_P.gguf
DRAFT=Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-FastMTP-32K.gguf
DEPTH=3

CUDA_VISIBLE_DEVICES=0 ./build/bin/llama-server \
  --model "$MODEL" \
  --spec-draft-model "$DRAFT" \
  --spec-draft-ngl all \
  --spec-type draft-mtp \
  --spec-draft-n-max "$DEPTH" \
  --spec-draft-p-min 0 \
  --ctx-size 204800 \
  --parallel 1 \
  --batch-size 2048 \
  --ubatch-size 512 \
  --n-gpu-layers all \
  --split-mode none \
  --flash-attn on \
  --no-mmap \
  --temp 1.0 \
  --top-k 20 \
  --top-p 0.95 \
  --min-p 0 \
  --presence-penalty 0 \
  --repeat-penalty 1.0 \
  --jinja \
  --reasoning on \
  --reasoning-effort xhigh \
  --reasoning-preserve \
  --reasoning-format deepseek \
  --host 127.0.0.1 \
  --port 8080

RTX PRO 6000 Blackwell FastMTP reference speeds

Three-run medians for the uncached 9.8K-token document fixture and three-case means for reasoning. Every FastMTP result reproduced the corresponding embedded-MTP output hashes.

Quant	Depth	PP tok/s	Document TG	Reasoning TG	vs embedded n2, Doc / Reason	vs MTP-off, Doc / Reason
Q2_K_P	3	3351.29	213.95	145.09	+11.6% / +1.7%	2.27x / 1.48x
Q3_K_P	3	3317.16	216.15	137.99	+20.5% / +8.8%	2.54x / 1.56x
Q4_K_P	3	3204.98	187.26	123.52	+18.0% / +2.3%	2.67x / 1.71x
Q5_K_P	3	2842.05	168.29	110.50	+17.8% / +4.6%	2.61x / 1.66x
Q6_K_P	3	3081.90	156.57	103.51	+26.5% / +13.8%	2.95x / 1.91x
Q8_K_P	3	3285.86	138.18	90.07	+35.2% / +21.1%	3.02x / 1.93x
IQ2_M	3	3050.94	219.19	135.00	+13.8% / +0.4%	2.33x / 1.39x
IQ3_M	3	3269.27	204.98	128.45	+21.5% / +7.9%	2.40x / 1.45x
IQ3_XS	3	3165.75	210.64	138.34	+19.1% / +5.9%	2.38x / 1.51x
IQ4_XS	3	3445.30	211.09	135.77	+21.7% / +9.3%	2.68x / 1.66x

The full-window gate used the final scrubbed Q3_K_P and FastMTP files: 190,000 uncached prompt tokens plus 64 generated tokens completed at 1613.81 PP tok/s and 131.81 TG tok/s, with 92.0% draft acceptance and no truncation inside the configured maximum native context.

RTX 6000 Ada embedded-MTP reference speeds

Single-run reference results from the final public files at a configured max token context, full CUDA offload, --no-mmap, the official thinking sampler, and embedded MTP. The workload used an uncached 9.8K-token document-continuation prompt followed by 512 generated tokens.

Quant	PP tok/s	TG tok/s
Q2_K_P	1959.14	121.88
Q3_K_P	1944.73	112.76
Q4_K_P	1860.34	92.60
Q5_K_P	1737.51	83.25
Q6_K_P	1747.60	72.89
Q8_K_P	1827.29	59.00
IQ2_M	1884.83	121.25
IQ3_M	1867.48	108.45
IQ3_XS	1880.59	111.77
IQ4_XS	1978.46	104.25

With HauhauCS FastMTP enabled, the final Q3_K_P reached 138.37 document TG and 87.95 reasoning TG on the same Ada—23.5% and 3.9% faster than the pinned Unsloth Q3 control.

Recommended settings

From the official Qwen3.8-27B model card:

Thinking mode (default):

temperature=1.0
top_p=0.95
top_k=20
min_p=0.0
presence_penalty=0.0
repetition_penalty=1.0
reasoning_effort=xhigh for the deepest reasoning

Instruct / non-thinking mode:

temperature=0.7
top_p=0.80
top_k=20
min_p=0.0
presence_penalty=1.5
repetition_penalty=1.0
enable_thinking=false

Qwen3.8 supports xhigh, medium, and low reasoning effort. Thinking and preserved reasoning are enabled by default in the official model contract.

Important:

Use --jinja for the embedded chat template.
Use the BF16 projector for Vision.
The model's native maximum is 262144.
Context length and KV precision have a large VRAM cost. Reduce context before reducing model quality if your workload does not need maximum native context.
Keep default F16 K/V on the lower tiers unless memory pressure requires otherwise.

If your llama.cpp build does not recognize the reasoning or MTP flags, update it. Older builds may still load the GGUF but will not expose the full Qwen3.8 serving path.

Turning thinking off

Qwen3.8 uses thinking mode by default. Disable it when you want shorter, faster direct responses.

Example llama-server default for all requests:

--chat-template-kwargs '{"enable_thinking":false}'

Example per request through the OpenAI-compatible API:

{
  "model": "qwen3.8-27b-aggressive-q3",
  "messages": [{"role": "user", "content": "..."}],
  "chat_template_kwargs": {"enable_thinking": false}
}

Example for multi-turn agents, preserve prior reasoning context with:

{
  "chat_template_kwargs": {"preserve_thinking": true}
}

Compatibility

llama.cpp: recommended; use a current Qwen3.8/MTP-capable build
LM Studio, Jan, KoboldCpp, and other GGUF frontends: base compatibility depends on their bundled llama.cpp version
Embedded MTP: optional and stock-compatible in current llama.cpp
HauhauCS FastMTP: optional; requires the sidecar and HauhauCS-FastMTP-llama.cpp.patch
Vision: requires the separate BF16 projector
K_P display: may appear as ? in UIs that do not recognize the suffix

Authenticity

Every GGUF is covered by the signed HauhauCS release manifest. Exact SHA-256 values identify byte-for-byte mirrors after renaming; canonical tensor fingerprints continue to identify HauhauCS tensors after metadata-only rewriting.

The FastMTP sidecar's exact file SHA-256 is 115e618e1f73cb50817ed5856f0551c6bf9c3d94df96f440eaca78dc63b8968b; its canonical tensor fingerprint is 49e248e799f169b6ccc6a8127b9300a95f06cf3d96a8353266f5d457e81d1c87. The public-key DER fingerprint is f7be4a2335582ab7b2e393ca1c40ce70e483f1492c0f57b8c6e05d8a7223833c.

Download HauhauCS-RELEASE-MANIFEST.json, its signature, FastMTP-PROVENANCE.json, its signature, and HauhauCS-FastMTP-Ed25519-PUBLIC.pem, then verify:

openssl pkeyutl -verify -rawin -pubin \
  -inkey HauhauCS-FastMTP-Ed25519-PUBLIC.pem \
  -in FastMTP-PROVENANCE.json \
  -sigfile FastMTP-PROVENANCE.json.sig

openssl pkeyutl -verify -rawin -pubin \
  -inkey HauhauCS-FastMTP-Ed25519-PUBLIC.pem \
  -in HauhauCS-RELEASE-MANIFEST.json \
  -sigfile HauhauCS-RELEASE-MANIFEST.json.sig

Other models

HauhauCS models on Hugging Face

Qwen3.8-27B is released by Qwen under the Apache 2.0 license. This quantized Aggressive variant retains that license.

Downloads last month: 486,221

GGUF

Model size

2B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

6-bit

View +2 variants

Model tree for HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF

Base model

Qwen/Qwen3.8-27B

Quantized

(751)

this model

Quantizations

3 models

HauhauCS
/

Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF

Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP

About

Downloads

What are K_P quants?

Specs

What is HauhauCS FastMTP?

Run HauhauCS FastMTP

RTX PRO 6000 Blackwell FastMTP reference speeds

RTX 6000 Ada embedded-MTP reference speeds

Recommended settings

Turning thinking off

Compatibility

Authenticity

Other models

Model tree for HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF

Space using HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF 1