Instructions to use ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF
# Run inference directly in the terminal:
llama cli -hf ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF
# Run inference directly in the terminal:
llama cli -hf ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF

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 ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF
# Run inference directly in the terminal:
./llama-cli -hf ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF

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 ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF

Use Docker

docker model run hf.co/ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF

LM Studio
Jan

vLLM

How to use ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-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": "ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF

Ollama
How to use ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF with Ollama:
```
ollama run hf.co/ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF
```

Unsloth Studio

How to use ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-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 ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-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 ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF to start chatting

How to use ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF

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": "ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF

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 ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF
```

Lemonade

How to use ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF

Run and chat with the model

lemonade run user.GLM-4.7-Flash-Uncensored-750-v1-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Experimental global target bits‑per‑weight quantization of HauhauCS/GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive

Using non-standard (forked) LLaMA C++ branch for quantization.
Using a CLI tool to build KLD evaluation and imatrix calibration datasets for GGUF models, sourced from eaddario/imatrix-calibration.
Using dataset sources: tools, math, code, text_en, text_ru.
Using dataset chunks: 750.
Tensors quantinization F16 instead of BF16, Nvidia Pascal architecture friendly like P100.
Small set of patches added.

Many thanks to Ed Addario for an impressive job.

Quantization comparison

BPW/TGS	PPL correlation	PPL mean ratio	ΔPPL	Mean KLD	Median KLD	Maximum KLD	99.9% KLD	Mean Δp	RMS Δp
3.50	93.18%	1.205301 ± 0.003693	3.066378 ± 0.059410	0.346790 ± 0.003130	0.113532	36.079124	17.036463	-1.548 ± 0.027 %	12.313 ± 0.063 %
4.00	92.50%	1.081636 ± 0.003399	1.219317 ± 0.050468	0.350924 ± 0.003492	0.094924	35.051384	19.111687	-0.644 ± 0.027 %	11.874 ± 0.066 %
4.50	93.92%	1.159525 ± 0.003402	2.382673 ± 0.054462	0.212138 ± 0.003037	0.035486	37.016945	19.404484	-0.722 ± 0.019 %	8.384 ± 0.070 %
5.00	94.32%	1.030399 ± 0.002807	0.454046 ± 0.041667	0.223456 ± 0.003202	0.029363	33.802094	20.041710	-0.307 ± 0.018 %	7.986 ± 0.072 %
5.50	93.59%	0.970038 ± 0.002771	-0.447509 ± 0.042194	0.234948 ± 0.003451	0.024535	32.256840	19.587420	0.123 ± 0.020 %	8.691 ± 0.081 %
6.00	96.85%	1.028155 ± 0.002101	0.420519 ± 0.031506	0.107335 ± 0.002290	0.008626	36.048412	17.149174	-0.060 ± 0.012 %	5.211 ± 0.072 %
6.50	97.55%	1.037597 ± 0.001880	0.561555 ± 0.028552	0.080116 ± 0.001919	0.007975	32.534607	14.545952	-0.128 ± 0.011 %	4.691 ± 0.069 %
7.00	96.92%	1.015746 ± 0.002049	0.235178 ± 0.030606	0.099637 ± 0.002328	0.003383	35.733624	17.560083	0.007 ± 0.010 %	4.330 ± 0.078 %
7.50	97.57%	1.030245 ± 0.001857	0.451735 ± 0.028043	0.067106 ± 0.001900	0.002916	37.250160	14.828805	-0.069 ± 0.009 %	3.827 ± 0.077 %
8.00	97.42%	1.022089 ± 0.001892	0.329923 ± 0.028377	0.077545 ± 0.002062	0.002760	33.393574	16.344349	-0.035 ± 0.009 %	3.835 ± 0.077 %
8.50	97.26%	1.026630 ± 0.001957	0.397746 ± 0.029396	0.082303 ± 0.002123	0.002307	31.664230	17.153591	-0.049 ± 0.009 %	3.815 ± 0.079 %
9.00	98.34%	1.019983 ± 0.001520	0.298461 ± 0.022937	0.044058 ± 0.001500	0.001003	35.295940	10.766310	-0.013 ± 0.007 %	3.132 ± 0.075 %
9.50	98.27%	1.010330 ± 0.001530	0.154286 ± 0.022915	0.051995 ± 0.001669	0.000858	32.079849	13.546452	0.032 ± 0.007 %	3.195 ± 0.078 %
10.00	98.40%	1.013286 ± 0.001478	0.198433 ± 0.022200	0.044456 ± 0.001528	0.000833	31.551548	12.255160	0.002 ± 0.007 %	3.022 ± 0.076 %
10.50	98.30%	1.012990 ± 0.001525	0.194020 ± 0.022882	0.047429 ± 0.001597	0.000826	33.701038	13.457508	0.019 ± 0.007 %	3.073 ± 0.078 %
11.00	98.35%	1.019113 ± 0.001514	0.285470 ± 0.022865	0.042238 ± 0.001490	0.000819	31.194330	11.399836	0.001 ± 0.006 %	2.878 ± 0.075 %

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GGUF

Model size

30B params

Architecture

deepseek2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF

Base model

HauhauCS/GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive

Quantized

(2)

this model

ENOSYS
/

GLM-4.7-Flash-Uncensored-750-v1-GGUF

Experimental global target bits‑per‑weight quantization of HauhauCS/GLM-4.7-Flash-Uncensored-HauhauCS-Aggressive

Quantization comparison

Model tree for ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF

Dataset used to train ENOSYS/GLM-4.7-Flash-Uncensored-750-v1-GGUF