Instructions to use jakeatx/Qwen3.8-27B-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 jakeatx/Qwen3.8-27B-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 jakeatx/Qwen3.8-27B-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf jakeatx/Qwen3.8-27B-GGUF:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jakeatx/Qwen3.8-27B-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf jakeatx/Qwen3.8-27B-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 jakeatx/Qwen3.8-27B-GGUF:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf jakeatx/Qwen3.8-27B-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 jakeatx/Qwen3.8-27B-GGUF:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jakeatx/Qwen3.8-27B-GGUF:IQ4_XS

Use Docker

docker model run hf.co/jakeatx/Qwen3.8-27B-GGUF:IQ4_XS

LM Studio
Jan
Ollama
How to use jakeatx/Qwen3.8-27B-GGUF with Ollama:
```
ollama run hf.co/jakeatx/Qwen3.8-27B-GGUF:IQ4_XS
```
Unsloth Desktop

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jakeatx/Qwen3.8-27B-GGUF:IQ4_XS

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "jakeatx/Qwen3.8-27B-GGUF:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jakeatx/Qwen3.8-27B-GGUF with Docker Model Runner:
```
docker model run hf.co/jakeatx/Qwen3.8-27B-GGUF:IQ4_XS
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jakeatx/Qwen3.8-27B-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.Qwen3.8-27B-GGUF-IQ4_XS

List all available models

lemonade list

Hermes Agent

How to use jakeatx/Qwen3.8-27B-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 jakeatx/Qwen3.8-27B-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 jakeatx/Qwen3.8-27B-GGUF:IQ4_XS

Run Hermes

hermes

Atomic Chat

OpenClaw

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

Start the llama.cpp server

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

Qwen3.8-27B GGUF Model Hub

Unified release repository for Qwen3.8-27B quantizations, fine-tunes, and speculative decoding configurations optimized for llamAmpere on SM86 (RTX 3090 / 3090 Ti).

Available Models

File	Base Architecture	BPW / Quant	MTP Drafter	Target Hardware / Fit
`qwen3.8-27b-ternary-mtp.gguf`	Prism ML Ternary Bonsai 2 27B	1.75 bpw (`PTQ1_0`)	Native 1-layer MTP	5.81 GiB; fits 262K context under 24 GB VRAM
`Qwen3.8-27B-ATX-4-XS.gguf`	Qwen3.8-27B Base	4.56 bpw (`ATX-IQ4_XS-M`)	Native 1-layer MTP	14.52 GiB; 245K context populated under 22.6 GB ready
`ATX-Swift-Qwen3.8-27B-Uncensored-IQ4_XS-M.gguf`	Qwen3.8-27B Swift Uncensored	4.56 bpw (`IQ4_XS-M`)	Native 1-layer MTP	15.58 GiB
`Qwopus3.8-27B-Flash-ATX-IQ4_XS-M.gguf`	Qwopus3.8-27B Flash	4.56 bpw (`IQ4_XS-M`)	Native 1-layer MTP	15.58 GiB

Running with llamAmpere

Build the SM86 runtime:

cmake -S . -B build-sm86 -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON -DGGML_CUDA_FA=ON \
      -DCMAKE_CUDA_ARCHITECTURES=86 -DGGML_NATIVE=ON
cmake --build build-sm86 -j8 --target llama-server

Run with native MTP speculative drafting:

./build-sm86/bin/llama-server \
  -m qwen3.8-27b-ternary-mtp.gguf \
  -c 32768 -b 4096 -ub 1024 -t 8 -ngl 99 -fa on -ctk q8_0 -ctv turbo3 \
  --spec-type draft-mtp --spec-draft-n-max 3 --spec-draft-p-min 0

Downloads last month: 807

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

View +4 variants

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