Instructions to use Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound 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 Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S
# Run inference directly in the terminal:
llama cli -hf Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S
# Run inference directly in the terminal:
llama cli -hf Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-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/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S
# Run inference directly in the terminal:
./llama-cli -hf Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-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/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S

Use Docker

docker model run hf.co/Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S

LM Studio
Jan
Ollama
How to use Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound with Ollama:
```
ollama run hf.co/Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S
```

Unsloth Studio

How to use Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-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/Qwen3.5-397B-A17B-gguf-q2ks-mixed-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/Qwen3.5-397B-A17B-gguf-q2ks-mixed-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/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound to start chatting

How to use Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-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/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-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/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S

Run Hermes

hermes

OpenClaw new

How to use Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S

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 "Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S" \
  --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"

Docker Model Runner
How to use Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound with Docker Model Runner:
```
docker model run hf.co/Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S
```

Lemonade

How to use Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S

Run and chat with the model

lemonade run user.Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound-Q2_K_S

List all available models

lemonade list

Atomic Chat

Performance indicators

by dehnhaide - opened Mar 24

Discussion

dehnhaide

Mar 24

Since llama.cpp has KLD and PPL tools available, would be nice if you could also publish such figures related to the quantization performance against other similar (e.g., Aessedai, Unsloth, ubergarm, etc.). This would create some more visibility and trust to your quants! :)
Something like: "Quant" vs "Size" vs "Q-Mixture PPL" vs "Mean PPL(Q)/PPL(base)" vs "KLD".

Trilogix1

Mar 24

They do not have the resources for it or simply they ignore their users request.
Is intel afraid to show some figures?

MartinPatterson

Mar 24

I too would love to see some comps autoround vs others.

wenhuach

Intel org Mar 25

Sorry, this is a great suggestion. However, as a very small team focused on engineering and algorithms, we currently don’t have the resources to support this effort, especially for large models. For large models, due to resource constraints, we use a similar algorithm in llama.cpp, but with a different mixed-bit strategy.

We have run some accuracy tests on smaller models, https://github.com/intel/auto-round/blob/main/docs/gguf_alg_ext_acc.md for gguf and https://huggingface.co/spaces/Intel/low_bit_open_llm_leaderboard for int4 . If you notice a gap between this model and others, please let us know. We will definitely look into it and investigate further..

Trilogix1

Mar 25

At least they are honest about it, that will do it for now. After all your mixed-bit strategy, squeeze quants more than everything available in huggingface (for what I have seen) and the models are still fairly usable. But the inference speed is affected very much.
@Wenhuach , take a look here: https://huggingface.co/AesSedai/Qwen3.5-397B-A17B-GGUF/discussions/7 you may benefit from it. Can you apply same PR to your autoround?

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