Instructions to use sydneyfong/glm4-9b-chat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sydneyfong/glm4-9b-chat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sydneyfong/glm4-9b-chat-GGUF",
	filename="glm4-9b-chat-IQ3_S.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sydneyfong/glm4-9b-chat-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sydneyfong/glm4-9b-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sydneyfong/glm4-9b-chat-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sydneyfong/glm4-9b-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sydneyfong/glm4-9b-chat-GGUF:Q4_K_M

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 sydneyfong/glm4-9b-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sydneyfong/glm4-9b-chat-GGUF:Q4_K_M

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 sydneyfong/glm4-9b-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sydneyfong/glm4-9b-chat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/sydneyfong/glm4-9b-chat-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use sydneyfong/glm4-9b-chat-GGUF with Ollama:
```
ollama run hf.co/sydneyfong/glm4-9b-chat-GGUF:Q4_K_M
```

Unsloth Studio

How to use sydneyfong/glm4-9b-chat-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 sydneyfong/glm4-9b-chat-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 sydneyfong/glm4-9b-chat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sydneyfong/glm4-9b-chat-GGUF to start chatting

How to use sydneyfong/glm4-9b-chat-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sydneyfong/glm4-9b-chat-GGUF:Q4_K_M

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": "sydneyfong/glm4-9b-chat-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sydneyfong/glm4-9b-chat-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 sydneyfong/glm4-9b-chat-GGUF:Q4_K_M

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 sydneyfong/glm4-9b-chat-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use sydneyfong/glm4-9b-chat-GGUF with Docker Model Runner:
```
docker model run hf.co/sydneyfong/glm4-9b-chat-GGUF:Q4_K_M
```

Lemonade

How to use sydneyfong/glm4-9b-chat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sydneyfong/glm4-9b-chat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.glm4-9b-chat-GGUF-Q4_K_M

List all available models

lemonade list

Llamacpp Quantizations of THUDM/glm-4-9b-chat

Using llama.cpp commit hash 7d0e23d72ef4540d0d4409cb63ae682c17d53926 for quantization. Notably this includes b3333, the first official llama.cpp release that supports GLM-3 and GLM-4.

Original model: https://huggingface.co/THUDM/glm-4-9b-chat

I have tested the gguf files with some simple prompts and they seem to work fine.

Prompt format

[gMASK]<sop><|user|>
{prompt}
<|assistant|>

Apparently the model supports function calling as well if you supply a more elaborate system prompt. The original chat template is provided in https://huggingface.co/THUDM/glm-4-9b-chat/blob/main/tokenizer_config.json , and it is too complicated if you don't want that functionality. (If you don't read Chinese, you're advised to translate it to a language you understand and read it first before adopting that prompt for your purposes.)

Quantizations

Due to resource limitations we only have a select handful of quantizations. Hopefully they are useful for your purposes.

MD5 (glm4-9b-chat-IQ3_S.gguf) = d6f4f51c5c4e7d3e8c1d93044fd92b9d
MD5 (glm4-9b-chat-Q4_K_M.gguf) = 9514ec1112b3e2a47cac52179d796c84
MD5 (glm4-9b-chat-Q4_K_S.gguf) = 38f48ddf4dc5f6845d070de5d1c3e4c6
MD5 (glm4-9b-chat-Q5_K_M.gguf) = 99717a90672ea7cf34f0ea23cff47c8a
MD5 (glm4-9b-chat-Q5_K_S.gguf) = b720b3cb4c5190bd36eac26f385e979b
MD5 (glm4-9b-chat-Q6_K.gguf) = b8a36cf46408ec558d471c38e55989c1
MD5 (glm4-9b-chat-Q8_0.gguf) = 1e2aea60e7c9453d560738f6bc06885e

Legal / License

"Built with glm-4"

I just copied the LICENSE file from https://huggingface.co/THUDM/glm-4-9b-chat as required for redistribution.

Downloads last month: 30

GGUF

Model size

9B params

Architecture

chatglm

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

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