Instructions to use jakeatx/Qwen3.8-27B-Ternary-PQ2_0-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 jakeatx/Qwen3.8-27B-Ternary-PQ2_0-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 jakeatx/Qwen3.8-27B-Ternary-PQ2_0-MTP-GGUF:Q2_0
# Run inference directly in the terminal:
llama cli -hf jakeatx/Qwen3.8-27B-Ternary-PQ2_0-MTP-GGUF:Q2_0

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-Ternary-PQ2_0-MTP-GGUF:Q2_0
# Run inference directly in the terminal:
llama cli -hf jakeatx/Qwen3.8-27B-Ternary-PQ2_0-MTP-GGUF:Q2_0

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-Ternary-PQ2_0-MTP-GGUF:Q2_0
# Run inference directly in the terminal:
./llama-cli -hf jakeatx/Qwen3.8-27B-Ternary-PQ2_0-MTP-GGUF:Q2_0

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-Ternary-PQ2_0-MTP-GGUF:Q2_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jakeatx/Qwen3.8-27B-Ternary-PQ2_0-MTP-GGUF:Q2_0

Use Docker

docker model run hf.co/jakeatx/Qwen3.8-27B-Ternary-PQ2_0-MTP-GGUF:Q2_0

LM Studio
Jan
Ollama
How to use jakeatx/Qwen3.8-27B-Ternary-PQ2_0-MTP-GGUF with Ollama:
```
ollama run hf.co/jakeatx/Qwen3.8-27B-Ternary-PQ2_0-MTP-GGUF:Q2_0
```
Unsloth Desktop

How to use jakeatx/Qwen3.8-27B-Ternary-PQ2_0-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 jakeatx/Qwen3.8-27B-Ternary-PQ2_0-MTP-GGUF:Q2_0

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-Ternary-PQ2_0-MTP-GGUF:Q2_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jakeatx/Qwen3.8-27B-Ternary-PQ2_0-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/jakeatx/Qwen3.8-27B-Ternary-PQ2_0-MTP-GGUF:Q2_0
```

Lemonade

How to use jakeatx/Qwen3.8-27B-Ternary-PQ2_0-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jakeatx/Qwen3.8-27B-Ternary-PQ2_0-MTP-GGUF:Q2_0

Run and chat with the model

lemonade run user.Qwen3.8-27B-Ternary-PQ2_0-MTP-GGUF-Q2_0

List all available models

lemonade list

Hermes Agent

How to use jakeatx/Qwen3.8-27B-Ternary-PQ2_0-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 jakeatx/Qwen3.8-27B-Ternary-PQ2_0-MTP-GGUF:Q2_0

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-Ternary-PQ2_0-MTP-GGUF:Q2_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jakeatx/Qwen3.8-27B-Ternary-PQ2_0-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 jakeatx/Qwen3.8-27B-Ternary-PQ2_0-MTP-GGUF:Q2_0

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-Ternary-PQ2_0-MTP-GGUF:Q2_0" \
  --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 Ternary PQ2_0 (2.13 bpw) with Native MTP

2-bit / 2.13 bpw ternary quantization of Qwen3.8-27B (Prism ML Ternary Bonsai 2 architecture) with native 1-layer MTP speculative drafting.

Format: PQ2_0 (group 128, 34 bytes/block, ~2.13 bpw)
Model Size: 7.66 GB
MTP Drafter: Native 1-layer MTP head (Q8_0 projection matrices fine-tuned on Bonsai 2)
Target Hardware: Single 24 GB GPU (RTX 3090 / 3090 Ti) with 245K+ context capacity under full offload
Runtime: Optimized for llamAmpere (branch feature/v0.3.1-bonsai2)

Usage with llamAmpere

./build-sm86/bin/llama-server \
  -m Qwen3.8-27B-Ternary-PQ2_0-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: 1,076

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

2-bit

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Collection including jakeatx/Qwen3.8-27B-Ternary-PQ2_0-MTP-GGUF

Qwen3.8-27B Models

Collection

Collection of Qwen3.8-27B quantizations, fine-tunes, and speculative decoding models. • 9 items • Updated 1 day ago