Instructions to use FreedomAISVR/Qwen3.5-4B-MTP-NVFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomAISVR/Qwen3.5-4B-MTP-NVFP4-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FreedomAISVR/Qwen3.5-4B-MTP-NVFP4-GGUF",
	filename="mmproj-qwen35-4b-f16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use FreedomAISVR/Qwen3.5-4B-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/Qwen3.5-4B-MTP-NVFP4-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf FreedomAISVR/Qwen3.5-4B-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/Qwen3.5-4B-MTP-NVFP4-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf FreedomAISVR/Qwen3.5-4B-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/Qwen3.5-4B-MTP-NVFP4-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf FreedomAISVR/Qwen3.5-4B-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/Qwen3.5-4B-MTP-NVFP4-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FreedomAISVR/Qwen3.5-4B-MTP-NVFP4-GGUF:F16

Use Docker

docker model run hf.co/FreedomAISVR/Qwen3.5-4B-MTP-NVFP4-GGUF:F16

LM Studio
Jan

vLLM

How to use FreedomAISVR/Qwen3.5-4B-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/Qwen3.5-4B-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/Qwen3.5-4B-MTP-NVFP4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FreedomAISVR/Qwen3.5-4B-MTP-NVFP4-GGUF:F16

Ollama
How to use FreedomAISVR/Qwen3.5-4B-MTP-NVFP4-GGUF with Ollama:
```
ollama run hf.co/FreedomAISVR/Qwen3.5-4B-MTP-NVFP4-GGUF:F16
```

Unsloth Studio new

How to use FreedomAISVR/Qwen3.5-4B-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/Qwen3.5-4B-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/Qwen3.5-4B-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/Qwen3.5-4B-MTP-NVFP4-GGUF to start chatting

Pi new

How to use FreedomAISVR/Qwen3.5-4B-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/Qwen3.5-4B-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/Qwen3.5-4B-MTP-NVFP4-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FreedomAISVR/Qwen3.5-4B-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/Qwen3.5-4B-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/Qwen3.5-4B-MTP-NVFP4-GGUF:F16

Run Hermes

hermes

Docker Model Runner
How to use FreedomAISVR/Qwen3.5-4B-MTP-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/FreedomAISVR/Qwen3.5-4B-MTP-NVFP4-GGUF:F16
```

Lemonade

How to use FreedomAISVR/Qwen3.5-4B-MTP-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FreedomAISVR/Qwen3.5-4B-MTP-NVFP4-GGUF:F16

Run and chat with the model

lemonade run user.Qwen3.5-4B-MTP-NVFP4-GGUF-F16

List all available models

lemonade list

Qwen3.5-4B-MTP-NVFP4-GGUF

NVFP4 (NVIDIA Blackwell FP4) GGUF quantization of Qwen/Qwen3.5-4B with Multi-Token Prediction (MTP) support.

Thinking is enabled by default. You may disable it by passing enable_thinking=false in the generation parameters. This matches the original model's behavior.

About NVFP4

NVFP4 is NVIDIA's native 4-bit floating-point format (E4M3) designed for Blackwell architecture GPUs (RTX 5000 series). It offers several advantages over traditional integer quantization formats:

Feature	NVFP4 (E4M3)	INT4 (Q4_K_M, etc.)
Block size	16 elements	32 elements
Dynamic range	±448	±7
Hardware acceleration	Blackwell Tensor Cores	CPU/CUDA cores
Dequantization overhead	None (native)	Required
BPW	~4.5-4.8	~4.5

When to use NVFP4:

You have an RTX 5060/5070/5080/5090 (Blackwell GPU)
You want optimal inference speed with native FP4 tensor cores
You need the best quality-per-bit among 4-bit formats

When to use other formats:

Pre-Blackwell GPUs: Use Q4_K_M or Q4_0
CPU inference: Use Q4_K_M or Q5_K_M
Maximum quality: Use Q6_K or Q8_0

With MTP enabled, the model predicts 2 tokens per inference step (1 base + 1 predicted), increasing throughput by up to 30-40% for text generation while maintaining quality.

Files

Filename	Type	Size	Description
`qwen35-4b-mtp-nvfp4.gguf`	Model	2.48 GB	NVFP4 quantized text model with MTP
`mmproj-qwen35-4b-f16.gguf`	Vision encoder	672 MB	Multimodal projector (SigLIP ViT, F16)

Quantization Details

Property	Value
Format	NVFP4 (E4M3)
Block size	16
BPW	4.81
HW target	NVIDIA Blackwell (RTX 5000 series)
VRAM required	~3 GB (with mmproj)
MTP heads	1 (`nextn_predict_layers=1`)
Thinking	Enabled by default (opt-out via `enable_thinking=false`)

Model Description

Qwen3.5-4B is a multilingual multimodal model from the Qwen team at Alibaba, featuring:

2560-dimensional hidden states
9216-dimensional feed-forward layers
32 base transformer layers + 1 MTP head (33 total blocks)
Multi-Head Latent Attention (MLA) with per-head KV norms
Hybrid architecture: Attention + SSM (Mamba-2) layers
262,144 token context window
3D MRoPE (Multi-modal Rotary Position Embedding)
Built-in vision encoder (SigLIP ViT)

Usage

llama.cpp CLI (text-only)

./llama-cli -m qwen35-4b-mtp-nvfp4.gguf \
  -p "Hello, how are you?" \
  -n 256

llama.cpp CLI (multimodal — with vision)

./llama-cli -m qwen35-4b-mtp-nvfp4.gguf \
  --mmproj mmproj-qwen35-4b-f16.gguf \
  --image photo.jpg \
  -p "Describe this image" \
  -n 256

llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="qwen35-4b-mtp-nvfp4.gguf",
    n_ctx=32768,
    n_gpu_layers=-1,  # Full GPU offload
)

output = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=256,
)
print(output["choices"][0]["message"]["content"])

Download from HuggingFace Hub

huggingface-cli download FreedomAISVR/Qwen3.5-4B-MTP-NVFP4-GGUF \
  qwen35-4b-mtp-nvfp4.gguf \
  --local-dir . --local-dir-use-symlinks False

# Also download mmproj for vision support:
huggingface-cli download FreedomAISVR/Qwen3.5-4B-MTP-NVFP4-GGUF \
  mmproj-qwen35-4b-f16.gguf \
  --local-dir . --local-dir-use-symlinks False

Conversion Pipeline

# 1. Download source model
huggingface-cli download Qwen/Qwen3.5-4B --local-dir ./qwen35-4b-src

# 2. Convert to F16 GGUF (MTP auto-included)
python convert_hf_to_gguf.py ./qwen35-4b-src \
  --outfile qwen35-4b-f16.gguf --outtype f16

# 3. Extract vision encoder mmproj
python convert_hf_to_gguf.py ./qwen35-4b-src \
  --mmproj --outfile mmproj-qwen35-4b-f16.gguf --outtype f16

# 4. Quantize to NVFP4
./llama-quantize qwen35-4b-f16.gguf \
  qwen35-4b-mtp-nvfp4.gguf NVFP4

Hardware

Component	Specification
GPU	RTX 5060 Ti 16GB (Blackwell)
VRAM	16 GB GDDR7
System RAM	64 GB
OS	Windows 11

License

This quantization is distributed under Apache 2.0. The original Qwen3.5-4B model is released under Apache 2.0 by Qwen (Alibaba Group).

Downloads last month: 376

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for FreedomAISVR/Qwen3.5-4B-MTP-NVFP4-GGUF

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Quantized

(209)

this model