GLM-4.7-Flash

Instructions to use VECTORVV1/GLM-4.7-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VECTORVV1/GLM-4.7-Flash with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="VECTORVV1/GLM-4.7-Flash",
	filename="GLM-4.7-Flash-Q4_K_P.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

llama.cpp

How to use VECTORVV1/GLM-4.7-Flash with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf VECTORVV1/GLM-4.7-Flash
# Run inference directly in the terminal:
llama-cli -hf VECTORVV1/GLM-4.7-Flash

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf VECTORVV1/GLM-4.7-Flash
# Run inference directly in the terminal:
llama-cli -hf VECTORVV1/GLM-4.7-Flash

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 VECTORVV1/GLM-4.7-Flash
# Run inference directly in the terminal:
./llama-cli -hf VECTORVV1/GLM-4.7-Flash

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 VECTORVV1/GLM-4.7-Flash
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VECTORVV1/GLM-4.7-Flash

Use Docker

docker model run hf.co/VECTORVV1/GLM-4.7-Flash

LM Studio
Jan

vLLM

How to use VECTORVV1/GLM-4.7-Flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VECTORVV1/GLM-4.7-Flash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VECTORVV1/GLM-4.7-Flash",
		"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/VECTORVV1/GLM-4.7-Flash

Ollama
How to use VECTORVV1/GLM-4.7-Flash with Ollama:
```
ollama run hf.co/VECTORVV1/GLM-4.7-Flash
```

Unsloth Studio new

How to use VECTORVV1/GLM-4.7-Flash 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 VECTORVV1/GLM-4.7-Flash 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 VECTORVV1/GLM-4.7-Flash to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for VECTORVV1/GLM-4.7-Flash to start chatting

Pi new

How to use VECTORVV1/GLM-4.7-Flash with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf VECTORVV1/GLM-4.7-Flash

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": "GLM-4.7-Flash"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use VECTORVV1/GLM-4.7-Flash with Docker Model Runner:
```
docker model run hf.co/VECTORVV1/GLM-4.7-Flash
```

Lemonade

How to use VECTORVV1/GLM-4.7-Flash with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VECTORVV1/GLM-4.7-Flash

Run and chat with the model

lemonade run user.GLM-4.7-Flash-{{QUANT_TAG}}

List all available models

lemonade list

Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive

Join the Discord for updates, roadmaps, projects, or just to chat.

Qwen3.5-122B-A10B uncensored by HauhauCS. 0/465 refusals.

About

No changes to datasets or capabilities. Fully functional, 100% of what the original authors intended - just without the refusals.

These are meant to be the best lossless uncensored models out there.

Aggressive Variant

Stronger uncensoring — model is fully unlocked and won't refuse prompts. Disclaimers that were present in previous releases have been significantly reduced in this version.

For a more conservative uncensor that keeps some safety guardrails, check the Balanced variant when it's available.

What are K_P quants?

K_P ("Perfect") quants are HauhauCS custom quantizations that use model-specific analysis to selectively preserve quality where it matters most. Each model gets its own optimized quantization profile.

A K_P quant effectively bumps quality up by 1-2 quant levels at only ~5-15% larger file size than the base quant. Fully compatible with llama.cpp, LM Studio, and any GGUF-compatible runtime — no special builds needed.

Downloads

File	Quant	Size
Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive-Q8_K_P.gguf	Q8_K_P	145 GB
Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive-Q6_K_P.gguf	Q6_K_P	105 GB
Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive-Q6_K.gguf	Q6_K	100 GB
Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive-Q5_K_P.gguf	Q5_K_P	94 GB
Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive-Q5_K_M.gguf	Q5_K_M	87 GB
Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive-Q4_K_P.gguf	Q4_K_P	79 GB
Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive-Q4_K_M.gguf	Q4_K_M	74 GB
Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive-IQ4_XS.gguf	IQ4_XS	65 GB
Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive-Q3_K_P.gguf	Q3_K_P	63 GB
Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive-Q3_K_M.gguf	Q3_K_M	59 GB
Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive-IQ3_M.gguf	IQ3_M	54 GB
Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive-IQ3_XXS.gguf	IQ3_XXS	47 GB
Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive-IQ2_M.gguf	IQ2_M	40 GB
mmproj-Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive-f16.gguf	mmproj (f16)	867 MB

Note: K_P quants may show as "?" in LM Studio's quant column. This is a display issue only — the model loads and runs fine.

Specs

122B total parameters, ~10B active per forward pass (MoE)
256 experts, 8 routed + 1 shared per token
Hybrid architecture: Gated DeltaNet linear attention + full softmax attention (3:1 ratio)
48 layers, pattern: 12 x (3 x DeltaNet-MoE + 1 x Attention-MoE)
262K native context
Natively multimodal (text, image, video)
248K vocabulary, 201 languages
Based on Qwen/Qwen3.5-122B-A10B

Recommended Settings

From the official Qwen authors:

Thinking mode (default):

General: temperature=1.0, top_p=0.95, top_k=20, min_p=0, presence_penalty=1.5
Coding/precise tasks: temperature=0.6, top_p=0.95, top_k=20, min_p=0, presence_penalty=0

Non-thinking mode:

General: temperature=0.7, top_p=0.8, top_k=20, min_p=0, presence_penalty=1.5
Reasoning tasks: temperature=1.0, top_p=1.0, top_k=40, min_p=0, presence_penalty=2.0

Important:

Use --jinja flag with llama.cpp for proper chat template handling
Thinking mode is on by default — to disable, use --chat-template-kwargs '{"enable_thinking":false}' or edit the jinja template
Vision support requires the mmproj file alongside the main GGUF

Usage

Works with llama.cpp, LM Studio, Jan, koboldcpp, and other GGUF-compatible runtimes.

# Text only
llama-cli -m Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive-Q4_K_P.gguf \
  --jinja -c 131072 -ngl 99

# With vision
llama-cli -m Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive-Q4_K_P.gguf \
  --mmproj mmproj-Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive-f16.gguf \
  --jinja -c 131072 -ngl 99

Other Models

Downloads last month: 218

GGUF

Model size

122B params

Architecture

qwen35moe

Hardware compatibility

Model tree for VECTORVV1/GLM-4.7-Flash

Base model

Qwen/Qwen3.5-122B-A10B

Quantized

(110)

this model