Instructions to use michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF",
	filename="Qwen3.6-35B-A3B-NVFP4-MTP-HQ.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 michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF:NVFP4
# Run inference directly in the terminal:
llama-cli -hf michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF:NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF:NVFP4
# Run inference directly in the terminal:
llama-cli -hf michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF:NVFP4

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 michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF:NVFP4
# Run inference directly in the terminal:
./llama-cli -hf michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF:NVFP4

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 michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF:NVFP4

Use Docker

docker model run hf.co/michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF:NVFP4

LM Studio
Jan

vLLM

How to use michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-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": "michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF:NVFP4

Ollama
How to use michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF with Ollama:
```
ollama run hf.co/michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF:NVFP4
```

Unsloth Studio

How to use michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-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 michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-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 michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF to start chatting

How to use michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF:NVFP4

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": "michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF:NVFP4

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 michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF:NVFP4

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF:NVFP4
```

Lemonade

How to use michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF:NVFP4

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-NVFP4-MTP-GGUF-NVFP4

List all available models

lemonade list

Qwen3.6-35B-A3B-NVFP4-MTP-GGUF

This repo contains two experimental NVFP4 GGUF quantizations of Qwen3.6-35B-A3B for llama.cpp.
This was quantized using my experimental advanced-gguf-quantizer tool.
Both models were imatrix calibrated for the first time using a new custom dataset that I am evaluating.

This repository contains two NVFP4 variants:

Variant	File	Best for	Notes
TURBO	`Qwen3.6-35B-A3B-NVFP4-MTP-TURBO.gguf`	Max speed	More NVFP4. Lower quality metrics.
HQ	`Qwen3.6-35B-A3B-NVFP4-MTP-HQ.gguf`	Better quality	More tensors promoted. Slightly slower.

Quality & Speed Results

All PPL/KLD results were measured against the same BF16 wikitest KLD base, and then compared to the official NVFP4 release by NVIDIA.

Metric	TURBO	HQ	NVIDIA-NVFP4
Size	18.56 GiB	18.64 GiB	22.20 GiB
Mean PPL(Q)	6.987392	6.897796	7.014030
Mean PPL(Q)-PPL(base)	0.268551	0.178955	—
Mean PPL ratio	1.039970	1.026635	1.043935
Mean ln(PPL ratio)	0.039192	0.026286	—
Mean KLD	0.063228	0.050759	0.066331
99.9% KLD	1.924147	1.565143	1.560988
99.0% KLD	0.598519	0.488387	0.495896
95.0% KLD	0.221030	0.178889	0.207580
Max KLD	11.946571	10.093911	6.972712
Same top p	89.023%	90.255%	87.608%
Top flip weight	0.012068	0.009575	—
pp512	11593.57 t/s	10936.20 t/s	10426.32 t/s
tg128	271.21 t/s	270.49 t/s	221.86 t/s

Evaluation Results

Further evaluation tests are underway to identify real world performance differences between TURBO and HQ.

Benchmark	Samples	TURBO	HQ	NVIDIA-NVFP4
GSM8K	103	98%	98%	97%
HellaSwag	100	89%	89%	89%
HumanEval	164	96.34%	95.12%	95.12%

Downloads last month: 2,651

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

4-bit

Model tree for michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

(502)

this model

Collection including michaelw9999/Qwen3.6-35B-A3B-NVFP4-MTP-GGUF

Quantized NVFP4 Models

Collection

6 items • Updated 8 days ago • 1