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

Use Docker

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

LM Studio
Jan

vLLM

How to use FreedomAISVR/Qwen3.5-9B-Instruct-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-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-NVFP4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Unsloth Studio

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

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

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

Run Hermes

hermes

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

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

NVFP4 (NVIDIA Blackwell FP4) GGUF quantization of the Qwen3.5-9B-Instruct multimodal language model with thinking disabled by default.

About NVFP4

NVFP4 is NVIDIA's native 4-bit floating-point format (E4M3 — 1 sign, 4 exponent, 3 mantissa) designed for Blackwell GPU architectures. Key characteristics:

Aspect	NVFP4	INT4 (e.g. Q4_K_M)
Format	FP4 (E4M3)	Integer
Block size	Per-tensor	Block 32
Dynamic range	~240 (wide)	Fixed
Zero representation	Exact	Exact
Hardware acceleration	Blackwell tensor cores	CPU / any GPU
Dequantization overhead	None (native)	Required

When to use NVFP4: You are running on an NVIDIA Blackwell GPU (RTX 5060, 5070, 5080, 5090, B100, B200, etc.) and want maximum performance with native 4-bit tensor core acceleration.

When to use a traditional format (Q4_K_M, Q5_K_M, etc.): You are running on pre-Blackwell hardware (Ampere, Ada Lovelace, Hopper), AMD GPUs, or CPU inference.

Files

File	Type	Size	Description
`qwen35-9b-instruct-nvfp4.gguf`	Text model	5.31 GB	Qwen3.5-9B-Instruct text model, NVFP4 quantized
`mmproj-qwen35-9b-f16.gguf`	Vision encoder	0.92 GB	Multimodal projector (SigLIP ViT), F16

Quantization Details

Parameter	Value
Quantization format	NVFP4 (E4M3)
Block size	Per-tensor
Bits per weight	~4.74
Hardware target	NVIDIA Blackwell (RTX 5000 series, B-series)
VRAM requirement	~3 GB (text) + ~0.7 GB (vision)

Model Description

Qwen3.5-9B-Instruct is a 9 billion-parameter multimodal language model from the Qwen team at Alibaba. It supports:

Text generation with instruction following
Image understanding (multimodal via SigLIP vision encoder)
Code generation and reasoning
Multilingual support (English, Chinese, and more)
32 transformer layers, 4096 hidden dimension, GQA + MLA hybrid attention with SSM (Mamba-2) interleaving

Usage

Thinking Control

By default, thinking is disabled. To enable reasoning, set enable_thinking=true:

# llama.cpp CLI
./llama-cli -m qwen35-9b-instruct-nvfp4.gguf \
  --mmproj mmproj-qwen35-9b-f16.gguf \
  --chat-template tokenizer.chat_template \
  -p "What is 2+2?" \
  -n 256

# Enable thinking:
./llama-cli -m qwen35-9b-instruct-nvfp4.gguf \
  --mmproj mmproj-qwen35-9b-f16.gguf \
  -p "<|im_start|>system\nenable_thinking=true<|im_end|>\n<|im_start|>user\nWhat is 2+2?<|im_end|>\n<|im_start|>assistant\n" \
  -n 512

llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="qwen35-9b-instruct-nvfp4.gguf",
    mmproj="mmproj-qwen35-9b-f16.gguf",
    n_ctx=32768,
    chat_format="qwen3",
)

# Without thinking (default):
response = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Hello!"}],
)

# With thinking enabled:
response = llm.create_chat_completion(
    messages=[
        {"role": "system", "content": "enable_thinking=true"},
        {"role": "user", "content": "Solve this step by step."},
    ],
)

Download

huggingface-cli download FreedomAISVR/Qwen3.5-9B-Instruct-NVFP4-GGUF \
  --include "*.gguf" \
  --local-dir .

Conversion Pipeline

1. Download original model: Qwen/Qwen3.5-9B-Instruct
2. Convert to F16 GGUF (text):   convert_hf_to_gguf.py --outtype f16 --no-mtp
3. Extract mmproj (vision):      convert_hf_to_gguf.py --mmproj --outtype f16
4. Quantize to NVFP4:            llama-quantize.exe text-f16.gguf output-nvfp4.gguf NVFP4
5. Patch chat template:          Disable thinking by default (enable_thinking opt-in)

Hardware

Component	Specification
GPU	NVIDIA RTX 5060 Ti (Blackwell)
VRAM	16 GB GDDR7
System RAM	32 GB
Quantization time	~1 min (9B)

License

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

Downloads last month: 1,807

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

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

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Quantized

(272)

this model