Instructions to use FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF",
	filename="mmproj-qwen35-9b-f16.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 FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF:F16

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 FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF:F16

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 FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF:F16

Use Docker

docker model run hf.co/FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF:F16

LM Studio
Jan

vLLM

How to use FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-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": "FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF:F16

Ollama
How to use FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF with Ollama:
```
ollama run hf.co/FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF:F16
```

Unsloth Studio

How to use FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-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 FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-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 FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF to start chatting

How to use FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF:F16

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": "FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-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 FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF:F16

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 FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF:F16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF:F16
```

Lemonade

How to use FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF:F16

Run and chat with the model

lemonade run user.Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF-F16

List all available models

lemonade list

Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF

NVFP4 (E4M3) GGUF quantization of Qwen/Qwen3.5-9B, an instruct-tuned multimodal 9B model with hybrid Mamba2-Attention architecture, 262K context window, and MTP (Multi-Token Prediction) support.

Quantized to NVIDIA's native FP4 format (NVFP4, E4M3) for Blackwell GPUs, delivering 4.73 bits per weight with Blackwell-native tensor core acceleration.

About NVFP4

Feature	NVFP4 (E4M3)	INT4 (e.g., Q4_K_M)
Block size	128 elements	Variable (K-quant blocks)
Dynamic range	±57344 (FP4 E4M3)	0–7 (INT4, symmetric)
Hardware target	Blackwell tensor cores	All GPUs / CPU
Dequantization overhead	None (native FP4 compute)	Required
BPW	~4.73	~4.50–5.50

Files

File	Type	Size	Description
`qwen35-9b-mtp-nvfp4.gguf`	NVFP4 quantized model	5.08 GB	Main model weights with MTP head (33 layers, 442 tensors)
`mmproj-qwen35-9b-f16.gguf`	Vision encoder (F16)	0.86 GB	Multimodal projector for image/video input

Quantization Details

Parameter	Value
Quantization format	NVFP4 (E4M3, block 128)
BPW	4.73
File type	39 (NVFP4)
Hardware target	NVIDIA Blackwell (RTX 50-series)
MTP layers	1 (nextn)

Original Model Description

Qwen3.5-9B is a 9-billion-parameter multimodal model from the Qwen team at Alibaba. Key features:

Hybrid Mamba2-Attention: Alternating Mamba2 and full attention layers (32 layers, 4:1 ratio)
Multimodal: Native image/video support via SigLIP vision encoder
262K context window with MRoPE
MTP (Multi-Token Prediction): Multi-step prediction head for improved generation quality
Instruct-tuned with tool-use support
Thinking control: Reasoning via enable_thinking parameter (off by default)

Usage

llama.cpp CLI

# Text-only
llama-cli -m qwen35-9b-mtp-nvfp4.gguf \
  -p "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nHello!<|im_end|>\n<|im_start|>assistant\n"

# Multimodal
llama-cli -m qwen35-9b-mtp-nvfp4.gguf \
  --mmproj mmproj-qwen35-9b-f16.gguf \
  --image photo.jpg \
  -p "Describe this image"

Python (llama-cpp-python)

from llama_cpp import Llama
llm = Llama(model_path="qwen35-9b-mtp-nvfp4.gguf", n_gpu_layers=-1)
response = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Hello!"}]
)

Download via huggingface-hub

from huggingface_hub import hf_hub_download
model_path = hf_hub_download(
    repo_id="FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF",
    filename="qwen35-9b-mtp-nvfp4.gguf"
)

Thinking Control

Thinking (reasoning mode) is disabled by default. Pass enable_thinking=true to enable.

Conversion Pipeline

# 1. Download source
huggingface-cli download Qwen/Qwen3.5-9B --local-dir qwen35-9b-src
# 2. Convert to F16 GGUF (MTP auto-included)
python convert_hf_to_gguf.py qwen35-9b-src --outfile qwen35-9b-bf16.gguf
# 3. Extract mmproj
python convert_hf_to_gguf.py qwen35-9b-src --outfile qwen35-9b-bf16.gguf --mmproj
# 4. Quantize to NVFP4
llama-quantize qwen35-9b-bf16.gguf qwen35-9b-nvfp4.gguf NVFP4

Verification

from gguf import GGUFReader
r = GGUFReader("qwen35-9b-mtp-nvfp4.gguf")
print(f"Architecture: {r.fields['general.architecture'].parts[-1]}")
print(f"Block count: {r.fields['qwen35.block_count'].parts[-1]}")
print(f"MTP layers: {r.fields['qwen35.nextn_predict_layers'].parts[-1]}")
print(f"Tensors: {len(r.tensors)}")
# Expected: qwen35, 33, 1, 442

License

Apache-2.0 (same as the original Qwen3.5-9B model).

Downloads last month: 712

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for FreedomAISVR/Qwen3.5-9B-Instruct-MTP-NVFP4-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Quantized

(280)

this model