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 / scripts /convert-to-gguf.sh

tarruda

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	#!/usr/bin/env bash

	set -euo pipefail

	# Validate that exactly 2 arguments are provided
	if [ $# -ne 2 ]; then
	echo "Error: Exactly 2 arguments required."
	echo "Usage: $0 <llama_cpp_dir> <original_safetensors_path>"
	echo "Example: $0 ~/code/llama.cpp ../../Qwen/Qwen3.5-397B-A10B"
	exit 1
	fi

	# Assign arguments to variables for clarity
	LLAMA_CPP_DIR="$1"
	ORIGINAL_SAFETENSORS_PATH="$2"

	# Validate that the llama.cpp directory exists
	if [ ! -d "$LLAMA_CPP_DIR" ]; then
	echo "Error: llama.cpp directory not found: $LLAMA_CPP_DIR"
	exit 1
	fi

	# Construct the path to the conversion script
	CONVERT_TO_GGUF_PATH="$LLAMA_CPP_DIR/convert_hf_to_gguf.py"

	# Validate that the Python script exists
	if [ ! -f "$CONVERT_TO_GGUF_PATH" ]; then
	echo "Error: Python script not found: $CONVERT_TO_GGUF_PATH"
	exit 1
	fi

	# Validate that the model path exists
	if [ ! -e "$ORIGINAL_SAFETENSORS_PATH" ]; then
	echo "Error: Model path not found: $ORIGINAL_SAFETENSORS_PATH"
	exit 1
	fi

	# Get the directory where the script is located
	SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"

	# Resolve GGUF_OUT_DIR absolutely (parent of script directory)
	# We cd into the parent and run pwd to get the absolute path
	if [ ! -d "$SCRIPT_DIR/.." ]; then
	echo "Error: Could not resolve parent directory for output."
	exit 1
	fi
	GGUF_OUT_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"

	# Define BF16_DIR inside GGUF_OUT_DIR
	BF16_DIR="$GGUF_OUT_DIR/BF16"
	mkdir -pv "$BF16_DIR"

	# Activate virtual environment inside llama.cpp directory
	cd "$LLAMA_CPP_DIR"
	source .venv/bin/activate
	cd -

	# # Execute the Python script with the provided arguments
	# # Main conversion output goes to BF16_DIR
	# "$CONVERT_TO_GGUF_PATH" --fuse-gate-up-exps --no-tensor-first-split --split-max-size 50G --outfile "$BF16_DIR/." "$ORIGINAL_SAFETENSORS_PATH"
	#
	# # mmproj output goes to GGUF_OUT_DIR
	# "$CONVERT_TO_GGUF_PATH" --fuse-gate-up-exps --mmproj --outtype bf16 --outfile "$GGUF_OUT_DIR/mmproj-BF16.gguf" "$ORIGINAL_SAFETENSORS_PATH"
	# # also do a FP16 version
	# "$CONVERT_TO_GGUF_PATH" --fuse-gate-up-exps --mmproj --outtype f16 --outfile "$GGUF_OUT_DIR/mmproj-F16.gguf" "$ORIGINAL_SAFETENSORS_PATH"

	"$CONVERT_TO_GGUF_PATH" --no-tensor-first-split --split-max-size 50G --outfile "$BF16_DIR/." "$ORIGINAL_SAFETENSORS_PATH"

	# mmproj output goes to GGUF_OUT_DIR
	"$CONVERT_TO_GGUF_PATH" --mmproj --outtype bf16 --outfile "$GGUF_OUT_DIR/mmproj-BF16.gguf" "$ORIGINAL_SAFETENSORS_PATH"
	# also do a FP16 version
	"$CONVERT_TO_GGUF_PATH" --mmproj --outtype f16 --outfile "$GGUF_OUT_DIR/mmproj-F16.gguf" "$ORIGINAL_SAFETENSORS_PATH"