Instructions to use lmstudio-community/GLM-Z1-32B-0414-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lmstudio-community/GLM-Z1-32B-0414-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lmstudio-community/GLM-Z1-32B-0414-GGUF",
	filename="GLM-Z1-32B-0414-Q3_K_L.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 lmstudio-community/GLM-Z1-32B-0414-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 lmstudio-community/GLM-Z1-32B-0414-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lmstudio-community/GLM-Z1-32B-0414-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lmstudio-community/GLM-Z1-32B-0414-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lmstudio-community/GLM-Z1-32B-0414-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 lmstudio-community/GLM-Z1-32B-0414-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf lmstudio-community/GLM-Z1-32B-0414-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 lmstudio-community/GLM-Z1-32B-0414-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lmstudio-community/GLM-Z1-32B-0414-GGUF:Q4_K_M

Use Docker

docker model run hf.co/lmstudio-community/GLM-Z1-32B-0414-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use lmstudio-community/GLM-Z1-32B-0414-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lmstudio-community/GLM-Z1-32B-0414-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": "lmstudio-community/GLM-Z1-32B-0414-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lmstudio-community/GLM-Z1-32B-0414-GGUF:Q4_K_M

Ollama
How to use lmstudio-community/GLM-Z1-32B-0414-GGUF with Ollama:
```
ollama run hf.co/lmstudio-community/GLM-Z1-32B-0414-GGUF:Q4_K_M
```

Unsloth Studio

How to use lmstudio-community/GLM-Z1-32B-0414-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 lmstudio-community/GLM-Z1-32B-0414-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 lmstudio-community/GLM-Z1-32B-0414-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lmstudio-community/GLM-Z1-32B-0414-GGUF to start chatting

How to use lmstudio-community/GLM-Z1-32B-0414-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lmstudio-community/GLM-Z1-32B-0414-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": "lmstudio-community/GLM-Z1-32B-0414-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use lmstudio-community/GLM-Z1-32B-0414-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 lmstudio-community/GLM-Z1-32B-0414-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 lmstudio-community/GLM-Z1-32B-0414-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use lmstudio-community/GLM-Z1-32B-0414-GGUF with Docker Model Runner:
```
docker model run hf.co/lmstudio-community/GLM-Z1-32B-0414-GGUF:Q4_K_M
```

Lemonade

How to use lmstudio-community/GLM-Z1-32B-0414-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lmstudio-community/GLM-Z1-32B-0414-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GLM-Z1-32B-0414-GGUF-Q4_K_M

List all available models

lemonade list

The <think> tags

by sovetboga - opened Apr 25, 2025

Discussion

sovetboga

Apr 25, 2025

•

edited Apr 25, 2025

Hello,

The model isn’t picking up the think tags. Will this start working after a llama.cpp update? Currently, the reasoning mode doesn’t work by default. I got it working by following advice from https://huggingface.co/ilintar/THUDM_GLM-Z1-9B-0414_iGGUF — I copied the Jinja template from there.

EvgeniyIV

Apr 25, 2025

•

edited Apr 25, 2025

Hello,

The model isn’t picking up the think tags. Will this start working after a llama.cpp update? Currently, the reasoning mode doesn’t work by default. I got it working by following advice from https://huggingface.co/ilintar/THUDM_GLM-Z1-9B-0414_iGGUF—I copied the Jinja template from there.

I just copied prompt template from GLM-4-32B in LM Studio and after that tag "think" works correctly now

sovetboga

Apr 25, 2025

Hello,

The model isn’t picking up the think tags. Will this start working after a llama.cpp update? Currently, the reasoning mode doesn’t work by default. I got it working by following advice from https://huggingface.co/ilintar/THUDM_GLM-Z1-9B-0414_iGGUF—I copied the Jinja template from there.

I just copied prompt template from GLM-4-32B in LM Studio and after that tag "think" works correctly now

Thanks, that helped! By the way, with the temperature set at 0.6 (as recommended), the responses sometimes lack detail. At 0.7 or 0.8, the answers are much more complete and logical. This applies to chat interactions, not programming.

EvgeniyIV

Apr 25, 2025

Hello,

The model isn’t picking up the think tags. Will this start working after a llama.cpp update? Currently, the reasoning mode doesn’t work by default. I got it working by following advice from https://huggingface.co/ilintar/THUDM_GLM-Z1-9B-0414_iGGUF—I copied the Jinja template from there.

I just copied prompt template from GLM-4-32B in LM Studio and after that tag "think" works correctly now

Thanks, that helped! By the way, with the temperature set at 0.6 (as recommended), the responses sometimes lack detail. At 0.7 or 0.8, the answers are much more complete and logical. This applies to chat interactions, not programming.

I've tested GLM-4 and GLM-Z1 for programming, my opinion that GLM-4 gives much more better results any way... Really strange

sovetboga

Apr 25, 2025

Hello,

The model isn’t picking up the think tags. Will this start working after a llama.cpp update? Currently, the reasoning mode doesn’t work by default. I got it working by following advice from https://huggingface.co/ilintar/THUDM_GLM-Z1-9B-0414_iGGUF—I copied the Jinja template from there.

I just copied prompt template from GLM-4-32B in LM Studio and after that tag "think" works correctly now

Thanks, that helped! By the way, with the temperature set at 0.6 (as recommended), the responses sometimes lack detail. At 0.7 or 0.8, the answers are much more complete and logical. This applies to chat interactions, not programming.

I've tested GLM-4 and GLM-Z1 for programming, my opinion that GLM-4 gives much more better results any way... Really strange

They're suited for different tasks. For example, I had a list and asked to sort it—the standard GLM handled everything perfectly (didn’t miss a single line), which was great. Then, I gave the same task to Z1: it didn’t process all lines but instead described the sorting structure in detail, using at most 50 out of 300 lines, while thoroughly explaining the logic, steps, and even notes on why. Now, here’s the catch: if we take that same logic and ask the standard model to sort accordingly, it succeeds. But if we ask Z1 to apply the exact same logic, it still skips some lines.

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment