Instructions to use ubergarm/Qwen3.5-397B-A17B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ubergarm/Qwen3.5-397B-A17B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ubergarm/Qwen3.5-397B-A17B-GGUF",
	filename="IQ2_KL/Qwen3.5-397B-A17B-IQ2_KL-00001-of-00004.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ubergarm/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 ubergarm/Qwen3.5-397B-A17B-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/Qwen3.5-397B-A17B-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/Qwen3.5-397B-A17B-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/Qwen3.5-397B-A17B-GGUF:Q2_K

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 ubergarm/Qwen3.5-397B-A17B-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/Qwen3.5-397B-A17B-GGUF:Q2_K

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 ubergarm/Qwen3.5-397B-A17B-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/Qwen3.5-397B-A17B-GGUF:Q2_K

Use Docker

docker model run hf.co/ubergarm/Qwen3.5-397B-A17B-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use ubergarm/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 "ubergarm/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": "ubergarm/Qwen3.5-397B-A17B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/Qwen3.5-397B-A17B-GGUF:Q2_K

Ollama
How to use ubergarm/Qwen3.5-397B-A17B-GGUF with Ollama:
```
ollama run hf.co/ubergarm/Qwen3.5-397B-A17B-GGUF:Q2_K
```

Unsloth Studio

How to use ubergarm/Qwen3.5-397B-A17B-GGUF 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 ubergarm/Qwen3.5-397B-A17B-GGUF 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 ubergarm/Qwen3.5-397B-A17B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ubergarm/Qwen3.5-397B-A17B-GGUF to start chatting

How to use ubergarm/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 ubergarm/Qwen3.5-397B-A17B-GGUF:Q2_K

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": "ubergarm/Qwen3.5-397B-A17B-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ubergarm/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 ubergarm/Qwen3.5-397B-A17B-GGUF:Q2_K

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 ubergarm/Qwen3.5-397B-A17B-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use ubergarm/Qwen3.5-397B-A17B-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/Qwen3.5-397B-A17B-GGUF:Q2_K
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/Qwen3.5-397B-A17B-GGUF:Q2_K

Run and chat with the model

lemonade run user.Qwen3.5-397B-A17B-GGUF-Q2_K

List all available models

lemonade list

Qwen3.5-397B-A17B-GGUF

File size: 2,181 Bytes

---
quantized_by: ubergarm
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
- ik_llama.cpp
---

## WIP

There is not yet support in [ik_llama.cpp though an open issue](https://github.com/ikawrakow/ik_llama.cpp/issues/1229).

For now to help out with testing, used mainline llama.cpp to make imatrix (gguf format) if others would like to use it to make their own imatrix custom quants.

Check the `logs/` directory for details on imatrix calculation.

I'll upload more if/when ik_llama.cpp support is merged.

It seems to inference very slowly on CPU-only and probably requires at least one GPU to handle attention/kv-cache/delta-net stuff as it is much faster even hybrid CPU+GPU.

## Q3_K 179.97 GiB (3.90 BPW)
TODO Perplexity Calculations

<details>

<summary>👈 Secret Recipe</summary>

```bash
./build/bin/llama-quantize \
    --tensor-type ffn_down_exps=q4_K \
    --tensor-type ffn_gate_exps=q3_K \
    --tensor-type ffn_up_exps=q3_K \
    --token-embedding-type q4_K \
    --output-tensor-type q6_K \
    --imatrix /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/imatrix-Qwen3.5-397B-A17B-BF16-mainline.gguf \
    /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/Qwen3.5-397B-A17B-BF16-00001-of-00017.gguf \
    /mnt/data/models/ubergarm/Qwen3.5-397B-A17B-GGUF/Qwen3.5-397B-A17B-Q3_K.gguf \
    Q8_0 \
    128
```

</details>

## References
* [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp)
* [ubergarm on quantizing LLMs and tuning GPUs with aifoundry.org](https://blog.aifoundry.org/p/adventures-in-model-quantization)
* [ubergarm-imatrix-calibration-corpus-v02.txt](https://gist.github.com/ubergarm/edfeb3ff9c6ec8b49e88cdf627b0711a?permalink_comment_id=5682584#gistcomment-5682584)
* [Getting Started Guide (out of date)](https://github.com/ikawrakow/ik_llama.cpp/discussions/258)
* [Quant Cookers Guide (out of date)](https://github.com/ikawrakow/ik_llama.cpp/discussions/434)
* [ik_llama.cpp Qwen3Next Issue](https://github.com/ikawrakow/ik_llama.cpp/issues/1229)