Instructions to use Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound",
	filename="Qwen3-8B-8.2B-Q2_K_S.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound:Q2_K_S
# Run inference directly in the terminal:
llama-cli -hf Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound:Q2_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound:Q2_K_S
# Run inference directly in the terminal:
llama-cli -hf Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound:Q2_K_S

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 Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound:Q2_K_S
# Run inference directly in the terminal:
./llama-cli -hf Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound:Q2_K_S

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 Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound:Q2_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound:Q2_K_S

Use Docker

docker model run hf.co/Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound:Q2_K_S

LM Studio
Jan
Ollama
How to use Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound with Ollama:
```
ollama run hf.co/Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound:Q2_K_S
```

Unsloth Studio

How to use Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound 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 Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound 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 Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound to start chatting

How to use Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound:Q2_K_S

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound:Q2_K_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound:Q2_K_S

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 Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound:Q2_K_S

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound with Docker Model Runner:
```
docker model run hf.co/Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound:Q2_K_S
```

Lemonade

How to use Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound:Q2_K_S

Run and chat with the model

lemonade run user.Qwen3-8B-GGUF-Q2KS-AS-AutoRound-Q2_K_S

List all available models

lemonade list

Model Details

This example model demonstrates how to use AutoRound’s compatibility to automatically generate a mixed-bit quantization recipe.

For more details, please refer to user guide

Generate the model

auto-round > 0.8.0

from auto_round import AutoRound, AutoScheme

model_name = "Qwen/Qwen3-8B"
avg_bits = 3.0
scheme = AutoScheme(avg_bits=avg_bits, options=("GGUF:Q2_K_S", "GGUF:Q4_K_S"), ignore_scale_zp_bits=True)
layer_config = {"lm_head": "GGUF:Q6_K"}

ar = AutoRound(model=model_name, scheme=scheme, layer_config=layer_config, iters=0)
ar.quantize_and_save()

Downloads last month: 14

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

2-bit

Inference Providers NEW

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Model tree for Intel/Qwen3-8B-GGUF-Q2KS-AS-AutoRound

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