Instructions to use Chungulus/Qwen3.8-27B-Q2_K-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 Chungulus/Qwen3.8-27B-Q2_K-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 Chungulus/Qwen3.8-27B-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf Chungulus/Qwen3.8-27B-Q2_K-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Chungulus/Qwen3.8-27B-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf Chungulus/Qwen3.8-27B-Q2_K-GGUF:Q2_K

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 Chungulus/Qwen3.8-27B-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf Chungulus/Qwen3.8-27B-Q2_K-GGUF:Q2_K

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 Chungulus/Qwen3.8-27B-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Chungulus/Qwen3.8-27B-Q2_K-GGUF:Q2_K

Use Docker

docker model run hf.co/Chungulus/Qwen3.8-27B-Q2_K-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use Chungulus/Qwen3.8-27B-Q2_K-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Chungulus/Qwen3.8-27B-Q2_K-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Chungulus/Qwen3.8-27B-Q2_K-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Chungulus/Qwen3.8-27B-Q2_K-GGUF:Q2_K

Ollama
How to use Chungulus/Qwen3.8-27B-Q2_K-GGUF with Ollama:
```
ollama run hf.co/Chungulus/Qwen3.8-27B-Q2_K-GGUF:Q2_K
```

Unsloth Studio

How to use Chungulus/Qwen3.8-27B-Q2_K-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 Chungulus/Qwen3.8-27B-Q2_K-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 Chungulus/Qwen3.8-27B-Q2_K-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Chungulus/Qwen3.8-27B-Q2_K-GGUF to start chatting

How to use Chungulus/Qwen3.8-27B-Q2_K-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Chungulus/Qwen3.8-27B-Q2_K-GGUF:Q2_K

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": "Chungulus/Qwen3.8-27B-Q2_K-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Chungulus/Qwen3.8-27B-Q2_K-GGUF with Docker Model Runner:
```
docker model run hf.co/Chungulus/Qwen3.8-27B-Q2_K-GGUF:Q2_K
```

Lemonade

How to use Chungulus/Qwen3.8-27B-Q2_K-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Chungulus/Qwen3.8-27B-Q2_K-GGUF:Q2_K

Run and chat with the model

lemonade run user.Qwen3.8-27B-Q2_K-GGUF-Q2_K

List all available models

lemonade list

Hermes Agent

How to use Chungulus/Qwen3.8-27B-Q2_K-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 Chungulus/Qwen3.8-27B-Q2_K-GGUF:Q2_K

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 Chungulus/Qwen3.8-27B-Q2_K-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Chungulus/Qwen3.8-27B-Q2_K-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Chungulus/Qwen3.8-27B-Q2_K-GGUF:Q2_K

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 "Chungulus/Qwen3.8-27B-Q2_K-GGUF:Q2_K" \
  --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 Q2_K GGUF

This repository contains one independently downloadable Q2_K GGUF of Qwen/Qwen3.8-27B. It is an unmodified vanilla quantization, not a fine-tune, merge, ablation, alignment change, or chat-template modification.

The source weights are pinned to 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0. The official checkpoint's internal Qwen3_5ForConditionalGeneration / qwen3_5 architecture identifier does not mean this is a Qwen3.5 model.

Files and conversion

Main model: Qwen3.8-27B-Q2_K.gguf (10.865 GB decimal)
Vision projector: mmproj-Qwen3.8-27B-F16.gguf
Complete repository size: 11.805 GB decimal
Quantization: Q2_K using llama.cpp K/IQ quantization
llama.cpp revision: 5f754ea0e2fd21e1213db7ebebfd65d938d9d69c
Calibration: none for K-quants; representative local prompts where required by IQ conversion
Source inventory: 1,199 tensors, including 333 vision tensors and 15 source MTP tensors

Validated component status

Text generation: passed
Tool calling: passed all five native-format cases
Vision and video: passed all three deterministic local-image cases
Thinking controls and chat template: preserved and checked against the locked source
Hybrid Gated DeltaNet/full-attention structure: retained
MTP tensors and metadata: retained; speculative acceleration is not advertised
Per-file runtime smoke: passed at 18.920043894501834 generated tokens/s on the validation host
Weight SHA-256: 185bc2aa1f15a34ef9d2b14f51f9edda69f4e43b5ee9e39e3c2ae8c439ddd1a5
Validation lineage: the same bytes passed in the combined release at f519a212d6c15cd3292b6ca835dd8ebf235642c0

validation_result.json records the exact per-file smoke result and its inherited aggregate functional gates. These split repositories do not claim a new benchmark run; they preserve byte-identical, already-validated artifacts.

Inference

git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp && git checkout 5f754ea0e2fd21e1213db7ebebfd65d938d9d69c
cmake -S . -B build -DGGML_METAL=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build --config Release -j

hf download Chungulus/Qwen3.8-27B-Q2_K-GGUF --local-dir ../qwen38-q2_k
cd ../qwen38-q2_k
../llama.cpp/build/bin/llama-mtmd-cli \
  -m ./Qwen3.8-27B-Q2_K.gguf \
  --mmproj ./mmproj-Qwen3.8-27B-F16.gguf \
  -p 'Describe the image.' --image ./image.png

Allow at least approximately 15 GB of available memory for the model, vision projector, and modest runtime overhead; KV-cache needs grow with context. This is planning guidance, not a measured peak-memory guarantee.

Use the source chat-template controls for enable_thinking, reasoning_effort, and preserve_thinking, together with Qwen's native tool-call format.

Limitations

Quantization can reduce quality, particularly at lower bit widths. The longest recorded validation prompt was 73 tokens; do not infer that the architectural maximum context was tested. A runtime must support the Qwen3.8 hybrid language graph, vision tower/projector, tokenizer, and retained MTP metadata rather than loading only a single language tensor.

License and attribution

The parent model and this vanilla quantization use the source model's Apache-2.0 license. See Qwen/Qwen3.8-27B.

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GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

2-bit

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Qwen3.8-27B Quantizations

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Deployment quantizations of the vanilla Qwen/Qwen3.8-27B model. • 37 items • Updated 8 days ago