Huihui-GLM-5.3-Flash-abliterated-GGUF

Instructions to use moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use moixx/Huihui-GLM-5.3-Flash-abliterated-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 moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF:UD-Q4_K_XL

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama cli -hf moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF:UD-Q4_K_XL

Llama new
LM Studio
Jan

vLLM

How to use moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "moixx/Huihui-GLM-5.3-Flash-abliterated-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": "moixx/Huihui-GLM-5.3-Flash-abliterated-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/moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF:UD-Q4_K_XL

SGLang

How to use moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moixx/Huihui-GLM-5.3-Flash-abliterated-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 images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moixx/Huihui-GLM-5.3-Flash-abliterated-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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF with Ollama:
```
ollama run hf.co/moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF:UD-Q4_K_XL
```
Unsloth Desktop

How to use moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF:UD-Q4_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF:UD-Q4_K_XL
```

Lemonade

How to use moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.Huihui-GLM-5.3-Flash-abliterated-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use moixx/Huihui-GLM-5.3-Flash-abliterated-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 moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF:UD-Q4_K_XL

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 moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF:UD-Q4_K_XL

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 "moixx/Huihui-GLM-5.3-Flash-abliterated-GGUF:UD-Q4_K_XL" \
  --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"

Huihui-GLM-5.3-Flash-abliterated-GGUF / README.md

moixx

huihui-ai

Duplicate from huihui-ai/Huihui-GLM-5.3-Flash-abliterated-GGUF

7dec0e5 about 1 month ago

preview code

Raw History Blame Contribute Delete

2.9 kB

---
language:
- en
- zh
library_name: transformers
license: mit
pipeline_tag: image-text-to-text
base_model:
- zai-org/GLM-5.3-Flash
tags:
- abliterated
- uncensored
- huihui
- unsloth
- glm5_next
---
# huihui-ai/Huihui-GLM-5.3-Flash-abliterated-GGUF

This is an uncensored version of [zai-org/GLM-5.3-Flash](https://huggingface.co/zai-org/GLM-5.3-Flash) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
This is a crude, proof-of-concept implementation to remove refusals from an LLM model.

## Latest update
The latest version of [llama.cpp](https://github.com/ggml-org/llama.cpp/releases) now officially supports zai-org/GLM-5.3-Flash. 

We have updated the first file of each quantized version, so you only need to download the first file.

## Note
GGUFs come from [unsloth/GLM-5.3-Flash-GGUF](https://huggingface.co/unsloth/GLM-5.3-Flash-GGUF). 

Only layers **15 to 35 (0-based indexing)** have been ablated, while the other layers and all expert modules remain unablated. 

## llama.cpp
To run, please [unslothai llama.cpp PR](https://github.com/unslothai/llama.cpp/tree/glm5next/upstream),
```
llama-cli -m huihui-ai/Huihui-GLM-5.3-Flash-abliterated-GGUF/UD-Q4_K_XL/GLM-5.3-Flash-UD-Q4_K_XL-00001-of-00006.gguf -c 262144
```

### Usage Warnings


 - **Risk of Sensitive or Controversial Outputs**: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.

 - **Not Suitable for All Audiences**: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.

 - **Legal and Ethical Responsibilities**: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.

 - **Research and Experimental Use**: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.

 - **Monitoring and Review Recommendations**: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.

 - **No Default Safety Guarantees**: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.


### Donation
##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
- bitcoin:
```
  bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
```
- Support our work on [Ko-fi](https://ko-fi.com/huihuiai)!

	---
	language:
	- en
	- zh
	library_name: transformers
	license: mit
	pipeline_tag: image-text-to-text
	base_model:
	- zai-org/GLM-5.3-Flash
	tags:
	- abliterated
	- uncensored
	- huihui
	- unsloth
	- glm5_next
	---

	# huihui-ai/Huihui-GLM-5.3-Flash-abliterated-GGUF

	This is an uncensored version of [zai-org/GLM-5.3-Flash](https://huggingface.co/zai-org/GLM-5.3-Flash) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
	This is a crude, proof-of-concept implementation to remove refusals from an LLM model.

	## Latest update
	The latest version of [llama.cpp](https://github.com/ggml-org/llama.cpp/releases) now officially supports zai-org/GLM-5.3-Flash.

	We have updated the first file of each quantized version, so you only need to download the first file.

	## Note
	GGUFs come from [unsloth/GLM-5.3-Flash-GGUF](https://huggingface.co/unsloth/GLM-5.3-Flash-GGUF).

	Only layers 15 to 35 (0-based indexing) have been ablated, while the other layers and all expert modules remain unablated.

	## llama.cpp
	To run, please [unslothai llama.cpp PR](https://github.com/unslothai/llama.cpp/tree/glm5next/upstream),
	```
	llama-cli -m huihui-ai/Huihui-GLM-5.3-Flash-abliterated-GGUF/UD-Q4_K_XL/GLM-5.3-Flash-UD-Q4_K_XL-00001-of-00006.gguf -c 262144
	```

	### Usage Warnings


	- Risk of Sensitive or Controversial Outputs: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.

	- Not Suitable for All Audiences: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.

	- Legal and Ethical Responsibilities: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.

	- Research and Experimental Use: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.

	- Monitoring and Review Recommendations: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.

	- No Default Safety Guarantees: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.


	### Donation
	##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
	- bitcoin:
	```
	bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
	```
	- Support our work on [Ko-fi](https://ko-fi.com/huihuiai)!