Instructions to use FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF",
	filename="glm-4.6v-flash-nvfp4.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use FreedomAISVR/GLM-4.6V-Flash-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/GLM-4.6V-Flash-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama-cli -hf FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF:NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama-cli -hf FreedomAISVR/GLM-4.6V-Flash-NVFP4-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 FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./llama-cli -hf FreedomAISVR/GLM-4.6V-Flash-NVFP4-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 FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF:NVFP4

Use Docker

docker model run hf.co/FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF:NVFP4

LM Studio
Jan

vLLM

How to use FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomAISVR/GLM-4.6V-Flash-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/GLM-4.6V-Flash-NVFP4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF:NVFP4

Ollama
How to use FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF with Ollama:
```
ollama run hf.co/FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF:NVFP4
```

Unsloth Studio

How to use FreedomAISVR/GLM-4.6V-Flash-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/GLM-4.6V-Flash-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/GLM-4.6V-Flash-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/GLM-4.6V-Flash-NVFP4-GGUF to start chatting

How to use FreedomAISVR/GLM-4.6V-Flash-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/GLM-4.6V-Flash-NVFP4-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": "FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FreedomAISVR/GLM-4.6V-Flash-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/GLM-4.6V-Flash-NVFP4-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 FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF:NVFP4

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF:NVFP4
```

Lemonade

How to use FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF:NVFP4

Run and chat with the model

lemonade run user.GLM-4.6V-Flash-NVFP4-GGUF-NVFP4

List all available models

lemonade list

GLM-4.6V-Flash NVFP4 GGUF

Quantized GGUF version of zai-org/GLM-4.6V-Flash by Z.ai (Zhipu AI), converted to NVFP4 (4-bit NVIDIA FP4) format.

Model Details

Base model: zai-org/GLM-4.6V-Flash — 9B parameter vision-language model by Z.ai with 40 transformer layers, 4096 hidden dim, 32 attention heads (8 KV heads), SwiGLU activation. Paper: 2507.01006.
Vision encoder: 24-layer ViT (1536 hidden dim, 1536/4096 attention dim, 13696 intermediate FFN)
Context length: 128K tokens
Quantization: NVFP4 — NVIDIA 4-bit FP4 format with Per-Group UE4M3 scales (4.64 BPW, 5.08 GB)
Thinking: Enabled by default (native <think>/</think> tokens, opt-out via enable_thinking=false)

Files

File	Size	Description
`glm-4.6v-flash-nvfp4.gguf`	5.08 GB	Quantized text model (523 tensors, 4.64 BPW)
`mmproj-glm-4.6v-flash-f16.gguf`	1.66 GB	Vision encoder projector (182 tensors, F16)

Usage

LM Studio

Load both files — the text GGUF as the main model and the mmproj as the vision encoder. Supports multimodal inputs (images + text).

llama.cpp

./llama-llava-cli \
  -m glm-4.6v-flash-nvfp4.gguf \
  --mmproj mmproj-glm-4.6v-flash-f16.gguf \
  -p "Describe this image in detail." \
  --image path/to/image.jpg

Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(
    model_path="glm-4.6v-flash-nvfp4.gguf",
    mmproj="mmproj-glm-4.6v-flash-f16.gguf",
    n_ctx=32768
)

output = llm.create_chat_completion(
    messages=[{
        "role": "user",
        "content": [
            {"type": "image_url", "image_url": {"url": "image.jpg"}},
            {"type": "text", "text": "What's in this image?"}
        ]
    }]
)
print(output["choices"][0]["message"]["content"])

Quantization Details

Source: zai-org/GLM-4.6V-Flash → F16 GGUF → llama-quantize.exe NVFP4
Block size: 64 elements; Per-Group UE4M3 scales (4 scales per block)
Output tensor: Q6_K (higher precision for the final projection)
Architecture: glm4 with 523 tensors (40 transformer layers, vision embedder)

Hardware Compatibility

Requires NVIDIA Blackwell (RTX 50 series) for native FP4 compute via CUDA Blackwall
Falls back to FP4 dequantization on older GPUs (slower but functional)
CPU inference supported via software dequant (significantly slower)

Downloads last month: -

GGUF

Model size

9B params

Architecture

glm4

Hardware compatibility

4-bit

Model tree for FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF

Base model

zai-org/GLM-4.6V-Flash

Quantized

(45)

this model

Paper for FreedomAISVR/GLM-4.6V-Flash-NVFP4-GGUF

GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

Paper • 2507.01006 • Published Jul 1, 2025 • 256