Instructions to use jkim96/Qwen3.8-27B-DASHQ-Q2-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 jkim96/Qwen3.8-27B-DASHQ-Q2-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 jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF:IQ2_XS
# Run inference directly in the terminal:
llama cli -hf jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF:IQ2_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF:IQ2_XS
# Run inference directly in the terminal:
llama cli -hf jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF:IQ2_XS

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF:IQ2_XS
# Run inference directly in the terminal:
./build/bin/llama cli -hf jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF:IQ2_XS

Use Docker

docker model run hf.co/jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF:IQ2_XS

Llama new
LM Studio
Jan

vLLM

How to use jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jkim96/Qwen3.8-27B-DASHQ-Q2-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": "jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF:IQ2_XS

Ollama
How to use jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF with Ollama:
```
ollama run hf.co/jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF:IQ2_XS
```
Unsloth Desktop

How to use jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF:IQ2_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": "jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF:IQ2_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF with Docker Model Runner:
```
docker model run hf.co/jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF:IQ2_XS
```

Lemonade

How to use jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF:IQ2_XS

Run and chat with the model

lemonade run user.Qwen3.8-27B-DASHQ-Q2-GGUF-IQ2_XS

List all available models

lemonade list

Hermes Agent

How to use jkim96/Qwen3.8-27B-DASHQ-Q2-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 jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF:IQ2_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 jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF:IQ2_XS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF:IQ2_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 "jkim96/Qwen3.8-27B-DASHQ-Q2-GGUF:IQ2_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-DASHQ-Q2-GGUF / README.md

jkim96

Update model card

d65ad1f verified about 1 month ago

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metadata

license: apache-2.0
base_model: Qwen/Qwen3.8-27B
base_model_relation: quantized
library_name: gguf
pipeline_tag: text-generation
tags:
  - gguf
  - llama.cpp
  - dashq
  - quantized

Qwen3.8-27B - DASH-Q 2-bit GGUF

llama.cpp GGUF files of Qwen/Qwen3.8-27B quantized with DASH-Q, at several 2-bit-class sizes. Every file uses only standard llama.cpp tensor types (no tensor above 4 bits) and loads in any recent llama.cpp build.

File	Type	Size	Bits / weight
`Qwen3.8-27B-DASHQ-IQ2_XXS.gguf`	IQ2_XXS	7.77 GB	2.28
`Qwen3.8-27B-DASHQ-IQ2_S.gguf`	IQ2_S	8.70 GB	2.55
`Qwen3.8-27B-DASHQ-Q2_K_XL.gguf`	Q2_K_XL	9.80 GB	2.87

Text only: the vision tower is not included.

Perplexity (lower is better)

Type	Model	Size	WikiText-2	C4
IQ2_XXS	llama.cpp IQ2_XXS (imatrix)	8.67 GB	7.77	12.63
IQ2_XXS	unsloth UD-IQ2_XXS	7.27 GB	7.34	12.53
IQ2_XXS	DASH-Q IQ2_XXS	7.77 GB	7.62	13.39
IQ2_XS	llama.cpp IQ2_XS (imatrix)	9.33 GB	7.30	11.93
IQ2_S	unsloth UD-IQ2_S	8.37 GB	6.87	11.46
IQ2_S	DASH-Q IQ2_S	8.70 GB	6.72	11.51
IQ2_M	llama.cpp IQ2_M (imatrix)	10.24 GB	6.91	11.33
Q2_K_XL	DASH-Q Q2_K_XL	9.80 GB	6.40	10.92

llama-perplexity, context 2048; WikiText-2 test, C4 validation (256 x 2048 tokens).

Usage

llama-cli -m Qwen3.8-27B-DASHQ-Q2_K_XL.gguf -ngl 99 -c 8192

License

Inherits the license of the base model (Qwen/Qwen3.8-27B).