Instructions to use CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-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 CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-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 CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-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 CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-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 CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-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 CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF with Ollama:
```
ollama run hf.co/CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF:Q4_K_M
```
Unsloth Desktop

How to use CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF:Q4_K_M

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": "CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF with Docker Model Runner:
```
docker model run hf.co/CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF:Q4_K_M
```

Lemonade

How to use CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-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 CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-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 CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-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 "CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-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"

Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF

GGUF quantizations of: vvsotnikov/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1

Source Model Card

Qwen3.8-27B 0.9 + Qwen3.6-27B 0.1

This checkpoint is a direct linear interpolation of the complete upstream checkpoints:

merged = 0.1 * Qwen/Qwen3.6-27B + 0.9 * Qwen/Qwen3.8-27B

Merge details

Method: element-wise linear interpolation in float32, cast to the original bfloat16 dtype after each tensor is merged
Tensors merged: 1,199, including the embedded mtp.* drafter tensors and vision tower
Shard layout, configuration, tokenizer, processor, and chat template: Qwen/Qwen3.8-27B
Qwen/Qwen3.6-27B revision: 6a9e13bd6fc8f0983b9b99948120bc37f49c13e9
Qwen/Qwen3.8-27B revision: 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0
Quantization: none

The two source checkpoints have identical tensor names, shapes, dtypes, and model configuration; their configs differ only in the recorded Transformers version. The newer Qwen/Qwen3.8-27B non-weight files are used because it has the larger merge coefficient.

License and use

Both source checkpoints are Apache-2.0. Their upstream model cards describe capabilities, limitations, and the expected qwen3_5 runtime support.

Downloads last month: 245

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

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Model tree for CreekHop/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1-GGUF

Base model

vvsotnikov/Qwen3.8-27B-0.9-Qwen3.6-27B-0.1

Quantized

(1)

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