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"

jakeatx commited on 26 days ago

Commit

77298a9

verified ·

1 Parent(s): f793ef1

Initial release of Qwen3.8-27B Ternary PQ2_0 MTP (2.13 bpw)

Browse files

Files changed (3) hide show

.gitattributes +1 -0
Qwen3.8-27B-Ternary-PQ2_0-MTP.gguf +3 -0
README.md +30 -0

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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+version https://git-lfs.github.com/spec/v1
+oid sha256:83a0aea0d7c3e7c8a9bd4e8d4ef85a3a14b33c92d1eebb161b40afb239c81bb3
+size 7657489728

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+---
+license: apache-2.0
+tags:
+- gguf
+- qwen3_5
+- ternary
+- mtp
+- speculative-decoding
+- ampere
+- sm86
+---
+# 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](https://github.com/JakeATX/llamAmpere) (branch `feature/v0.3.1-bonsai2`)
+## Usage with llamAmpere
+```bash
+./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
+```