Instructions to use tarruda/Qwen3.5-397B-A17B-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 tarruda/Qwen3.5-397B-A17B-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 tarruda/Qwen3.5-397B-A17B-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf tarruda/Qwen3.5-397B-A17B-GGUF:IQ3_XXS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tarruda/Qwen3.5-397B-A17B-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf tarruda/Qwen3.5-397B-A17B-GGUF:IQ3_XXS

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 tarruda/Qwen3.5-397B-A17B-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./llama-cli -hf tarruda/Qwen3.5-397B-A17B-GGUF:IQ3_XXS

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 tarruda/Qwen3.5-397B-A17B-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tarruda/Qwen3.5-397B-A17B-GGUF:IQ3_XXS

Use Docker

docker model run hf.co/tarruda/Qwen3.5-397B-A17B-GGUF:IQ3_XXS

LM Studio
Jan

vLLM

How to use tarruda/Qwen3.5-397B-A17B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tarruda/Qwen3.5-397B-A17B-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": "tarruda/Qwen3.5-397B-A17B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tarruda/Qwen3.5-397B-A17B-GGUF:IQ3_XXS

Ollama
How to use tarruda/Qwen3.5-397B-A17B-GGUF with Ollama:
```
ollama run hf.co/tarruda/Qwen3.5-397B-A17B-GGUF:IQ3_XXS
```
Unsloth Desktop

How to use tarruda/Qwen3.5-397B-A17B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tarruda/Qwen3.5-397B-A17B-GGUF:IQ3_XXS

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": "tarruda/Qwen3.5-397B-A17B-GGUF:IQ3_XXS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tarruda/Qwen3.5-397B-A17B-GGUF with Docker Model Runner:
```
docker model run hf.co/tarruda/Qwen3.5-397B-A17B-GGUF:IQ3_XXS
```

Lemonade

How to use tarruda/Qwen3.5-397B-A17B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tarruda/Qwen3.5-397B-A17B-GGUF:IQ3_XXS

Run and chat with the model

lemonade run user.Qwen3.5-397B-A17B-GGUF-IQ3_XXS

List all available models

lemonade list

Hermes Agent

How to use tarruda/Qwen3.5-397B-A17B-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 tarruda/Qwen3.5-397B-A17B-GGUF:IQ3_XXS

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 tarruda/Qwen3.5-397B-A17B-GGUF:IQ3_XXS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tarruda/Qwen3.5-397B-A17B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tarruda/Qwen3.5-397B-A17B-GGUF:IQ3_XXS

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 "tarruda/Qwen3.5-397B-A17B-GGUF:IQ3_XXS" \
  --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"

Qwen3.5-397B-A17B-GGUF / README.md

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metadata

quantized_by: tarruda
pipeline_tag: text-generation
base_model: Qwen/Qwen3.5-397B-A17B
base_model_relation: quantized
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3.5-397B-A17B/blob/main/LICENSE
tags:
  - imatrix
  - conversational
  - qwen3_5_moe

Intro

This is a 2.54 BPW Qwen 3.5 397B quantization using a recipe inspired by @AesSedai and @ubergarm.

My goal was to maximize BPW for my hardware (128G M1 ultra) while allowing up for 128K context.

The recipe is:

TYPE_FFN_GATE_UP_EXPS=IQ2_XXS
TYPE_FFN_DOWN_EXPS=IQ3_XXS
TYPE_TOKEN_EMBEDDING=Q4_K
TYPE_OUTPUT=Q6_K
TYPE_DEFAULT=Q8_0

Running

This is the command I use to run it locally:

llama-server --no-mmap --no-warmup -fa on --model IQ3_XXS/Qwen3.5-397B-A17B-IQ3_XXS-00001-of-00004.gguf --mmproj mmproj-F16.gguf --ctx-size 131072 --jinja --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0.00 -cram 0

I use -cram 0 because the model + context will take 100% of my available RAM.

Quantizing

Assuming the original model is located at ../../Qwen/Qwen3.5-397B-A17B and llama.cpp (with built binaries) is located at ~/llama.cpp, the full quantization is done with:

./scripts/convert-to-gguf.sh ~/llama.cpp ../../Qwen/Qwen3.5-397B-A17B
./scripts/quantize.sh ~/code/llama.cpp IQ3_XXS

The quantization depends on imatrix.gguf, which was copied from @ubergarm's 397B repo.