Instructions to use FreedomAISVR/GLM-4.7-Flash-NVFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomAISVR/GLM-4.7-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-4.7-Flash-NVFP4-GGUF",
	filename="glm-4.7-flash-nvfp4.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/GLM-4.7-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-4.7-Flash-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama-cli -hf FreedomAISVR/GLM-4.7-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-4.7-Flash-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama-cli -hf FreedomAISVR/GLM-4.7-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-4.7-Flash-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./llama-cli -hf FreedomAISVR/GLM-4.7-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-4.7-Flash-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FreedomAISVR/GLM-4.7-Flash-NVFP4-GGUF:NVFP4

Use Docker

docker model run hf.co/FreedomAISVR/GLM-4.7-Flash-NVFP4-GGUF:NVFP4

LM Studio
Jan

vLLM

How to use FreedomAISVR/GLM-4.7-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-4.7-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-4.7-Flash-NVFP4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FreedomAISVR/GLM-4.7-Flash-NVFP4-GGUF:NVFP4

Ollama
How to use FreedomAISVR/GLM-4.7-Flash-NVFP4-GGUF with Ollama:
```
ollama run hf.co/FreedomAISVR/GLM-4.7-Flash-NVFP4-GGUF:NVFP4
```

Unsloth Studio

How to use FreedomAISVR/GLM-4.7-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-4.7-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-4.7-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-4.7-Flash-NVFP4-GGUF to start chatting

How to use FreedomAISVR/GLM-4.7-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-4.7-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-4.7-Flash-NVFP4-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FreedomAISVR/GLM-4.7-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-4.7-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-4.7-Flash-NVFP4-GGUF:NVFP4

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use FreedomAISVR/GLM-4.7-Flash-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/FreedomAISVR/GLM-4.7-Flash-NVFP4-GGUF:NVFP4
```

Lemonade

How to use FreedomAISVR/GLM-4.7-Flash-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FreedomAISVR/GLM-4.7-Flash-NVFP4-GGUF:NVFP4

Run and chat with the model

lemonade run user.GLM-4.7-Flash-NVFP4-GGUF-NVFP4

List all available models

lemonade list

GLM-4.7-Flash-NVFP4-GGUF

GGUF quantization of zai-org/GLM-4.7-Flash — a 30B-parameter Mixture-of-Experts language model with ~3.2B active parameters per token, built on the DeepSeek2 architecture with Multi-head Latent Attention (MLA) and 64 routed experts.

Quantized to NVFP4 format for efficient inference with minimal quality loss.

About NVFP4

NVFP4 is NVIDIA's native 4-bit floating-point format (E4M3) for Blackwell GPUs. It stores weights in FP4 with a shared per-block scale, enabling native Blackwell tensor core acceleration with no dequantization overhead during inference. Compared to INT4 formats, NVFP4 offers better dynamic range (E4M3 vs E2M1) and maintains higher quality at similar bit widths.

Files

Filename	Type	Size	Description
`glm-4.7-flash-nvfp4.gguf`	GGUF (NVFP4)	15.79 GB	Quantized model weights
`README.md`	Markdown	-	Model card

Quantization Details

Property	Value
Format	NVFP4
Bits Per Weight	4.53 BPW
File Size	15.79 GB
Tensor Count	844
Architecture	DeepSeek2 (custom for GLM-4.7-Flash)

Model Description

Developer: Zhipu AI
Architecture: Mixture-of-Experts (MoE) with DeepSeek2-style MLA
Parameters: ~30B total, ~3.2B active per token
Context Length: 200,000 tokens
Layers: 47 transformer layers
Attention: Multi-head Latent Attention (q_lora_rank=768, kv_lora_rank=512)
Experts: 64 routed experts (4 per token) + 1 shared expert
Vocab Size: 151,936
Languages: English, Chinese
Thinking: Enabled by default (native <think>/</think> tokens, hidden in history for clean multi-turn reasoning)
Pipeline: text-generation only (no vision encoder)

Usage

llama.cpp

# Basic generation
./llama-cli -m glm-4.7-flash-nvfp4.gguf \
  -p "Hello, how are you?" \
  -n 256

# With thinking/reasoning controlled
./llama-cli -m glm-4.7-flash-nvfp4.gguf \
  -p "Solve this step by step: 23 * 47" \
  -n 512 \
  -no-cnv

HuggingFace Hub

from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="FreedomAISVR/GLM-4.7-Flash-NVFP4-GGUF",
    filename="glm-4.7-flash-nvfp4.gguf",
    repo_type="model"
)

Pipeline Commands

Source: zai-org/GLM-4.7-Flash (58 GB, 48 safetensor shards)

F16 GGUF Conversion:

python convert_hf_to_gguf.py D:\AI_MODELS\glm-4.7-src --outfile glm-4.7-f16.gguf --outtype f16

Output: 55.79 GB, 844 tensors (DeepSeek2 arch, Glm4MoeLiteModel)

NVFP4 Quantization:

llama-quantize.exe glm-4.7-f16.gguf glm-4.7-flash-nvfp4.gguf NVFP4

Duration: ~310s on RTX 5060 Ti

Hardware

Component	Specification
GPU	NVIDIA RTX 5060 Ti 16 GB (Blackwell)
System RAM	64 GB
Storage	D: (NVMe)

License

MIT — same as the original zai-org/GLM-4.7-Flash.

Downloads last month: -

GGUF

Model size

30B params

Architecture

deepseek2

Hardware compatibility

4-bit

Model tree for FreedomAISVR/GLM-4.7-Flash-NVFP4-GGUF

Base model

zai-org/GLM-4.7-Flash

Quantized

(83)

this model