Instructions to use orcarouter/Qwen3.8-27B-Uncensored-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 orcarouter/Qwen3.8-27B-Uncensored-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 orcarouter/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf orcarouter/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf orcarouter/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf orcarouter/Qwen3.8-27B-Uncensored-GGUF:Q4_K_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 orcarouter/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf orcarouter/Qwen3.8-27B-Uncensored-GGUF:Q4_K_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 orcarouter/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf orcarouter/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/orcarouter/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use orcarouter/Qwen3.8-27B-Uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "orcarouter/Qwen3.8-27B-Uncensored-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": "orcarouter/Qwen3.8-27B-Uncensored-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/orcarouter/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M

Ollama
How to use orcarouter/Qwen3.8-27B-Uncensored-GGUF with Ollama:
```
ollama run hf.co/orcarouter/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M
```

Unsloth Studio

How to use orcarouter/Qwen3.8-27B-Uncensored-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 orcarouter/Qwen3.8-27B-Uncensored-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 orcarouter/Qwen3.8-27B-Uncensored-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use orcarouter/Qwen3.8-27B-Uncensored-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf orcarouter/Qwen3.8-27B-Uncensored-GGUF:Q4_K_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": "orcarouter/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use orcarouter/Qwen3.8-27B-Uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/orcarouter/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M
```

Lemonade

How to use orcarouter/Qwen3.8-27B-Uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull orcarouter/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-27B-Uncensored-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use orcarouter/Qwen3.8-27B-Uncensored-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 orcarouter/Qwen3.8-27B-Uncensored-GGUF:Q4_K_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 orcarouter/Qwen3.8-27B-Uncensored-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use orcarouter/Qwen3.8-27B-Uncensored-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf orcarouter/Qwen3.8-27B-Uncensored-GGUF:Q4_K_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 "orcarouter/Qwen3.8-27B-Uncensored-GGUF:Q4_K_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"

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Qwen3.8-27B-Uncensored-GGUF

GGUF quants (2-bit → 16-bit) of the abliterated (refusal-removed) Qwen3.8-27B — for llama.cpp

One Gateway. Every Model. — Route Smarter · Ship Safer · Spend Less.

Website · Model Catalog · Model Card · GitHub · Ollama · Discord · X

GGUF conversions of Qwen3.8-27B-Uncensored — an abliterated (refusal-removed) build of Qwen's Qwen3.8-27B, a 27B dense hybrid-attention (Gated DeltaNet linear + full attention) native vision-language model with reasoning, tool-calling, and an MTP speculative-decoding head. These files run in llama.cpp (CPU / CUDA / Metal / ROCm), quantized from 2-bit to 16-bit, with a separate mmproj file that restores vision. Browse all models in the OrcaRouter Model Catalog. Qwen3.8 27B is deployed as API on OrcaRouter.

⚠️ Disclaimer — read before use

This model has had its safety alignment substantially removed via abliteration (orthogonalizing the refusal direction out of the residual stream). It will comply with harmful, unethical, or illegal requests the original Qwen3.8-27B would refuse. Released strictly for legitimate research — interpretability, AI-safety / refusal-mechanism study, red-teaming, and robustness evaluation. You assume full responsibility for how you use it and everything it generates; add your own safety and moderation layers before any deployment. Use must comply with the Apache 2.0 License inherited from the base model and all applicable law. The authors accept no liability for misuse.

Requirements

A recent llama.cpp built from source (the qwen35 hybrid-GDN architecture and the MTP / nextn speculative head — merged 2026-05 — must be present). Older releases will not load these files.
The GDN linear-attention layers are stored as SSM-style tensors (ssm_*); full-attention layers as attn_*; the MTP head as block nextn.* (qwen35.nextn_predict_layers).

Files

Standard K-quants

File	Bits	Size	Notes / recommendation
`…-Q2_K.gguf`	2-bit	10.9 GB	Smallest K-quant; noticeable quality drop — low-VRAM only
`…-Q3_K_S.gguf`	3-bit	12.3 GB
`…-Q3_K_M.gguf`	3-bit	13.5 GB	Good small option
`…-Q3_K_L.gguf`	3-bit	14.6 GB
`…-Q4_K_S.gguf`	4-bit	15.8 GB
`…-Q4_K_M.gguf`	4-bit	16.8 GB	Recommended default — best quality/size balance
`…-Q5_K_S.gguf`	5-bit	17.7 GB
`…-Q5_K_M.gguf`	5-bit	18.2 GB	High quality
`…-Q6_K.gguf`	6-bit	20.9 GB	Very high quality
`…-Q8_0.gguf`	8-bit	27.1 GB	Near-lossless
`…-F16-0000*-of-00002.gguf`	16-bit	54.7 GB	Full precision (split into 2 parts; point llama.cpp at part 00001)

IQ quants (imatrix)

Lower-bit quants built with an importance matrix (computed on English + Chinese calibration text) — better quality-per-bit than plain K-quants at the low end, especially IQ3/IQ2.

File	Bits	Size	Notes / recommendation
`…-IQ4_XS.gguf`	~4.25-bit	15.3 GB	Best low-bit pick — ≈ Q4_K_S quality at smaller size
`…-IQ3_M.gguf`	~3.7-bit	12.8 GB	Solid 3-bit
`…-IQ3_XXS.gguf`	~3.1-bit	11.6 GB	Smaller 3-bit
`…-IQ2_M.gguf`	~2.7-bit	10.5 GB	Runs in low VRAM; some quality loss
`…-IQ2_XXS.gguf`	~2.1-bit	8.9 GB	Smallest runnable; most degraded

Vision

File	Size	Notes
`mmproj-…-f16.gguf`	0.9 GB	Vision projector — download this too for image input

All quants (K-quant and IQ) preserve the MTP (nextn) head and the GDN hybrid architecture; vision is provided by the separate mmproj file. The IQ files were quantized with an importance matrix (computed on English + Chinese calibration text) for better low-bit fidelity; the matrix itself is not shipped, as it is only needed to re-quantize these files, not to run them.

Usage (llama.cpp)

Download

hf download orcarouter/Qwen3.8-27B-Uncensored-GGUF \
  Qwen3.8-27B-Uncensored-Q4_K_M.gguf mmproj-Qwen3.8-27B-Uncensored-f16.gguf \
  --local-dir ./qwen38-uncensored

Chat (text)

./llama-cli -m Qwen3.8-27B-Uncensored-Q4_K_M.gguf --jinja -c 8192 -p "Hello!"

OpenAI-compatible server (tool calling + reasoning + vision)

./llama-server -m Qwen3.8-27B-Uncensored-Q4_K_M.gguf \
  --mmproj mmproj-Qwen3.8-27B-Uncensored-f16.gguf \
  --host 0.0.0.0 --port 8000 -c 8192 --jinja

Vision: pass --mmproj …, then send OpenAI image_url content parts (base64 data-URI or URL).
Tool calling: --jinja enables the Qwen tool template; use standard OpenAI tools + tool_calls.
Reasoning (thinking): thinking is on by default; toggle per request via chat_template_kwargs.enable_thinking. The reasoning trace is returned in reasoning_content (give max_tokens room, e.g. ≥ 2048, so the final answer isn't truncated by the thinking budget).
MTP speculative decoding: the nextn head is embedded; enable llama.cpp's MTP/speculative path for a decode speed-up (optional — the model runs fine without it).

Or skip llama.cpp — run it with Ollama

The same quants are published as Ollama tags, with the mmproj projector already bundled in (no second file, no --mmproj flag) and vision / tools / thinking wired up:

ollama run orcarouter/Qwen3.8-27B-Uncensored          # q4_K_M by default
ollama run orcarouter/Qwen3.8-27B-Uncensored:iq4_xs   # any of the 16 tags

Sixteen tags from q2_K to q8_0, each verified on the actual quantized artifact before release — ollama.com/orcarouter.

Evaluation

Measured on this exact abliterated build (served with vLLM) vs the official Qwen/Qwen3.8-27B-FP8, using the same scripts and settings. Refusal is judged by a rule-based opening-phrase classifier — indicative, not an LLM-judge / publication-grade number. GGUF quants are deterministic derivations and inherit these behaviours; lower bits trade some quality (see the file table).

Safety — harmful-prompt refusal, thinking OFF (lower = more uncensored)

Benchmark	n	Base FP8	This model
AdvBench	100	99.0%	0.0%
JailbreakBench (harmful)	100	94.0%	0.0%
StrongREJECT	150	97.3%	2.0%
HarmBench (standard)	150	98.7%	2.7%
MaliciousInstruct	100	99.0%	0.0%
SimpleSafetyTests	50	64.0%	6.0%
ForbiddenQuestions	150	73.3%	4.7%
Custom probes (zh/en)	11	63.6%	0.0%

Safety — harmful-prompt refusal, thinking ON (`enable_thinking=true`)

Benchmark	n	Base FP8	This model
AdvBench	60	66.7%	1.7%
JailbreakBench (harmful)	60	43.3%	0.0%
StrongREJECT	60	35.0%	0.0%
HarmBench (standard)	60	46.7%	0.0%
MaliciousInstruct	60	83.3%	0.0%
SimpleSafetyTests	50	44.0%	0.0%
ForbiddenQuestions	60	48.3%	0.0%
Custom probes (zh/en)	11	45.5%	0.0%

Over-refusal — benign prompts wrongly refused (lower = better)

Benchmark	n	Base FP8 (no-think / think)	This model (no-think / think)
XSTest-safe	250	5.6% / 0.0%	0.4% / 0.0%

Capability retention — vs the official base FP8 (same scripts)

Benchmark	n	Base FP8	This model	Δ
MMLU (all, 0-shot)	300	84.3%	84.7%	+0.4
MMLU-Pro (CoT)	250	77.6%	76.8%	−0.8
GSM8K (CoT)	150	90.0%	88.7%	−1.3
CMMLU (0-shot, Chinese)	500	81.4%	80.8%	−0.6
WikiText-2 perplexity	—	—	6.96	fluency sanity check

Harmful-prompt refusal collapses from 64–99% (base) to 0–6%; benign over-refusal drops (5.6%→0.4%); capability stays within ±1.3 pts of the base. Reasoning (enable_thinking), multi-turn tool calling (qwen3_coder), and vision (image + OCR via mmproj) all verified working on the GGUF build. Note: the above are full-precision/FP8 numbers; expect small additional degradation at lower quants (most visible at Q2_K / Q3).

Hardware

Runs on CPU, CUDA, Metal, or ROCm via llama.cpp. VRAM/RAM ≈ the file size + KV cache + (for vision) the ~0.9 GB mmproj. E.g. Q4_K_M fits comfortably on a 24 GB GPU with room for context.

License

Apache 2.0, inherited from Qwen/Qwen3.8-27B. Abliteration and quantization do not change the underlying license obligations.

Downloads last month: 68,275

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for orcarouter/Qwen3.8-27B-Uncensored-GGUF

Base model

Qwen/Qwen3.8-27B

Quantized

(714)

this model

Quantizations

6 models

Space using orcarouter/Qwen3.8-27B-Uncensored-GGUF 1

Collection including orcarouter/Qwen3.8-27B-Uncensored-GGUF

Qwen3.8 27B Uncensored / Abliterated

Collection

The Qwen3.8-27B-Uncensored family from OrcaRouter 🐋 FP8 ✅ NVFP4 ✅ GGUF ✅ INT8 ✅ MLX ✅ Full precision ✅ • 6 items • Updated 1 day ago • 16

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Qwen3.8-27B-Uncensored-GGUF

⚠️ Disclaimer — read before use

Requirements

Files

Standard K-quants

IQ quants (imatrix)

Vision

Usage (llama.cpp)

Download

Chat (text)

OpenAI-compatible server (tool calling + reasoning + vision)

Or skip llama.cpp — run it with Ollama

Evaluation

Safety — harmful-prompt refusal, thinking OFF (lower = more uncensored)

Safety — harmful-prompt refusal, thinking ON (enable_thinking=true)

Over-refusal — benign prompts wrongly refused (lower = better)

Capability retention — vs the official base FP8 (same scripts)

Hardware

License

Model tree for orcarouter/Qwen3.8-27B-Uncensored-GGUF

Space using orcarouter/Qwen3.8-27B-Uncensored-GGUF 1

Collection including orcarouter/Qwen3.8-27B-Uncensored-GGUF

Safety — harmful-prompt refusal, thinking ON (`enable_thinking=true`)