Instructions to use Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-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 Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-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 Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF:IQ4_XS

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 Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF:IQ4_XS

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 Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF:IQ4_XS

Use Docker

docker model run hf.co/Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF:IQ4_XS

LM Studio
Jan
Ollama
How to use Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF with Ollama:
```
ollama run hf.co/Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF:IQ4_XS
```

Unsloth Studio

How to use Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-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 Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-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 Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF to start chatting

How to use Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF:IQ4_XS

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": "Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF with Docker Model Runner:
```
docker model run hf.co/Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF:IQ4_XS
```

Lemonade

How to use Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF-IQ4_XS

List all available models

lemonade list

Hermes Agent

How to use Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-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 Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF:IQ4_XS

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 Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF:IQ4_XS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF:IQ4_XS

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 "Bucoid/Qwen3.8-27B-Uncensored-IQ4-XS-MTP-16GB-VRAM-GGUF:IQ4_XS" \
  --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"

此模型可能短期更新，我发布了一个基于Heretic Arbitrary-Rank Ablation的无审查版本模型，性能更好且体积更小链接： https://huggingface.co/Bucoid/Qwen3.8-27B-Heretic-Ara-IQ4-XS-16GB-VRAM-GGUF 这个模型可能过一段时间我会更新让他不那么菜，如果你需要无审查版本的模型，基于下载这个Ara的

This model may receive short-term updates. I have released an uncensored version based on Heretic Arbitrary-Rank Ablation, which offers better performance and a smaller file size. Link: https://huggingface.co/Bucoid/Qwen3.8-27B-Heretic-Ara-IQ4-XS-16GB-VRAM-GGUF This model may be updated in a while to make it less underwhelming If you need an uncensored version, please download this Ara-based one instead.

Qwen3.8-27B Uncensored IQ4_XS 量化模型（适配 16GB 显存）本模型基于 Qwen3.8-27B Uncensored 进行 IQ4_XS 量化（4‑bit），文件体积为 12.9 GiB，专为 16GB 显存的显卡优化，在保持较低困惑度的同时，兼顾推理速度和显存占用。

与同体积的 UD_IQ3_K_XL（12.5 GiB）量化方案进行了全面对比，评估指标如下。

📊 量化质量对比

评估指标	IQ4_XS (本模型)	UD_IQ3_K_XL (对比)
文件大小	12.9 GB	12.5 GB
量化精度	IQ4_XS (4‑bit)	UD_IQ3_K_XL (约 3‑bit?)
量化模型困惑度 (Mean PPL)	7.1481 ± 0.0465	7.1117 ± 0.0459
与基座模型 PPL 相关性	99.28%	99.31%
平均 KL 散度 (Mean KLD)	0.03268 ± 0.00030	0.03130 ± 0.00032
最大 KL 散度 (Max KLD)	16.017（更小）	21.409
99.9% KL 分位数	1.075	1.219
Top‑1 一致率 (Same top p)	91.655% ± 0.072%	92.419% ± 0.069%
平均概率变化 (Mean Δp)	-0.343% ± 0.013%（更接近 0）	-0.738% ± 0.013%
RMS 概率变化 (RMS Δp)	4.986% ± 0.039%（更小）	5.120% ± 0.046%

在不启用MTP的情况下可以做到16GiB净空VRAM（不作为Windows的显示显卡）的情况下110k上下文

开启MTP大概80k上下文。

license: apache-2.0 base_model: - Qwen/Qwen3.8-27B

Qwen3.8-27B Uncensored IQ4_XS Quantized Model (Optimized for 16GB VRAM) This model is based on Qwen3.8-27B Uncensored and quantized with IQ4_XS (4‑bit), with a file size of 12.9 GiB. It is tailored for GPUs with 16GB VRAM, balancing low perplexity, inference speed, and memory usage.

We conducted a comprehensive comparison against the UD_IQ3_K_XL quantization scheme (12.5 GiB, roughly 3‑bit) of the same model size. The evaluation metrics are as follows.

📊 Quantization Quality Comparison

Metric	IQ4_XS (this model)	UD_IQ3_K_XL (baseline)
File size	12.9 GB	12.5 GB
Quantization precision	IQ4_XS (4‑bit)	UD_IQ3_K_XL (~3‑bit)
Mean perplexity (quantized)	7.1481 ± 0.0465	7.1117 ± 0.0459
Correlation with base model PPL	99.28%	99.31%
Mean KL divergence	0.03268 ± 0.00030	0.03130 ± 0.00032
Maximum KL divergence	16.017 (lower)	21.409
99.9% KL quantile	1.075	1.219
Top‑1 agreement rate	91.655% ± 0.072%	92.419% ± 0.069%
Mean probability change (Mean Δp)	-0.343% ± 0.013% (closer to 0)	-0.738% ± 0.013%
RMS probability change (RMS Δp)	4.986% ± 0.039% (lower)	5.120% ± 0.046%

With MTP disabled, the model can achieve ~110k context length while keeping ~16 GiB free VRAM (when not used as the primary display GPU on Windows). With MTP enabled, the context length is around 80k.

Downloads last month: 9,697

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

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