Instructions to use bartowski/zai-org_GLM-4.7-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bartowski/zai-org_GLM-4.7-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bartowski/zai-org_GLM-4.7-GGUF",
	filename="zai-org_GLM-4.7-IQ1_M/zai-org_GLM-4.7-IQ1_M-00001-of-00003.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 bartowski/zai-org_GLM-4.7-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bartowski/zai-org_GLM-4.7-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bartowski/zai-org_GLM-4.7-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 bartowski/zai-org_GLM-4.7-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bartowski/zai-org_GLM-4.7-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 bartowski/zai-org_GLM-4.7-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bartowski/zai-org_GLM-4.7-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 bartowski/zai-org_GLM-4.7-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/zai-org_GLM-4.7-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bartowski/zai-org_GLM-4.7-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bartowski/zai-org_GLM-4.7-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/zai-org_GLM-4.7-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": "bartowski/zai-org_GLM-4.7-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bartowski/zai-org_GLM-4.7-GGUF:Q4_K_M

Ollama
How to use bartowski/zai-org_GLM-4.7-GGUF with Ollama:
```
ollama run hf.co/bartowski/zai-org_GLM-4.7-GGUF:Q4_K_M
```

Unsloth Studio

How to use bartowski/zai-org_GLM-4.7-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 bartowski/zai-org_GLM-4.7-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 bartowski/zai-org_GLM-4.7-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bartowski/zai-org_GLM-4.7-GGUF to start chatting

How to use bartowski/zai-org_GLM-4.7-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf bartowski/zai-org_GLM-4.7-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": "bartowski/zai-org_GLM-4.7-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use bartowski/zai-org_GLM-4.7-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 bartowski/zai-org_GLM-4.7-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 bartowski/zai-org_GLM-4.7-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use bartowski/zai-org_GLM-4.7-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/zai-org_GLM-4.7-GGUF:Q4_K_M
```

Lemonade

How to use bartowski/zai-org_GLM-4.7-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/zai-org_GLM-4.7-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.zai-org_GLM-4.7-GGUF-Q4_K_M

List all available models

lemonade list

Different sizes of same quants?

by inputout - opened Jan 1

Discussion

inputout

Jan 1

•

edited Jan 1

I was wondering why same quants yield different sizes of gguf. Just out of interest, why is that? (differences up to 1,3 GB !!)
255,3 GB Q5_K_M @bartowski
254,2 GB Q5_K_M unsloth
254,0 GB Q5_K_M mradermacher

bartowski

Owner Jan 1

My quants use this fork for the quantization resulting in slightly different layouts of quantization by layer for MoE models:

https://github.com/ggml-org/llama.cpp/pull/12727

Unsloth uses something else, not sure if it's public

mradermacher uses mainline afaik

inputout

Jan 2

Interesting, I had assumed it was related to different imatrixes, but then there are even more fundamental reasons.
Do you think that under these circumstances, the ppl of the quants are still comparable as a metric?
I found it exciting that in a test, your PPLs were better than the others on my own “uncontaminated” text, but the others were better on Wikitest, as if the imatrix were “benchmaxxed” on Wikitest (if that's even technically possible). It could also be a coincidence.

ilintar

Jan 2

@inputout if the imatrix is calibrated on WikiText, it will get better PPL results on WikiText, which is why it's generally a good idea to do PPL on a different dataset than the one used to calibrate the imatrix.

bartowski

Owner Jan 2

yeah imatrix itself won't change the actual size of the result, it only affects the scales and offsets chosen while quantizing

but as ilintar said, using the same dataset for imatrix and PPL can result in minor advantages to the result, which is why your use of an "uncontaminated" text is most likely to be the most accurate results

do you mind sharing your results? I'm highly curious

inputout

Jan 11

•

edited Jan 11

I also included mradermacher non-imatrix, and due to my modest hardware, it takes a long time. I limited the text input (~80-150KB of text and shortened the end of wiki.test).
At first, I had the impression that the results of wiki.test and non-wiki.test were swapped, but it's not quite that simple.
I also tested mradermacher non-imatrix, and that was very surprising: non-imatrix was clearly the best in wiki.test, so I can't really talk about benchmaxxing with imatrix quants if imatrix actually worsens the perplexity.
In non-wiki.test, @bartowski quants tend to be the best. It's interesting that -ctk q8_0 and -ctk q8_0 -ctv q8_0 reshuffle the cards a bit anyway.

Actually, only the following can be said with certainty:

Every other text delivers different results. Non-wiki.test texts in other languages tend little to show a kind of swap in the results.
Surprisingly, and for me most illogically, mradermacher's non-imatrix is best for wiki.test, refuting any benchmaxxing assumptions. I would have expected imatrix to be better on average than non-imatrix, even on wiki.
ctk q8_0 and -ctk q8_0 -ctv q8_0 reshuffle the cards.
On non-wiki.test texts with non-English, on average your quants are the better ones. (Perhaps also because of the slightly larger file size = better ppl)

Its not totally logical, so real benchmarks of the GGUFs would probably be better, but no one can do this work when new models and quants come out every week.
Regardless of how meaningful the tests actually are, many questions remain, such as: How meaningful is the ppl anyway? Does a better ppl also correlate with a genuinely better answer quality (real benchmarks)? What influence does the file size/amount of information have due to different quantification methods? What influence does imatrix have on non-English texts? etc...

Here are the results ("*" shows the ranking)

wiki.test.raw (shortened)
GLM-4.7-Q5_K_M(bar): 4.6279 +/- 0.08825 *3
GLM-4.7-Q5_K_M(uns): 4.6038 +/- 0.08732 *2
GLM-4.7.i1-Q5_K_M(mra): 4.6921 +/- 0.09047 *4 ("worst" overall)
GLM-4.7.Q5_K_M(mra): 4.5374 +/- 0.08610 *1
-ctk q8_0
GLM-4.7-Q5_K_M(bar): 4.6659 +/- 0.08935 *3
GLM-4.7-Q5_K_M(uns): 4.6126 +/- 0.08770 *2
GLM-4.7.i1-Q5_K_M(mra): 4.6432 +/- 0.08892 *3
GLM-4.7.Q5_K_M(mra): 4.5335 +/- 0.08591 *1 ("best" overall)
-ctk q8_0 -ctv q8_0
GLM-4.7-Q5_K_M(bar): 4.6760 +/- 0.08973 *4
GLM-4.7-Q5_K_M(uns): 4.6319 +/- 0.08834 *2
GLM-4.7.i1-Q5_K_M(mra): 4.6724 +/- 0.08953 *3
GLM-4.7.Q5_K_M(mra): 4.5640 +/- 0.08682 *1

non-english input:
GLM-4.7-Q5_K_M(bar): 4.6485 +/- 0.13850 *2
GLM-4.7-Q5_K_M(uns): 4.6544 +/- 0.13872 *3
GLM-4.7.i1-Q5_K_M(mra): 4.6417 +/- 0.13801 *1
GLM-4.7.Q5_K_M(mra): 4.6747 +/- 0.13983 *4
-ctk q8_0
GLM-4.7-Q5_K_M(bar): 4.6203 +/- 0.13709 *1 ("best" overall)
GLM-4.7-Q5_K_M(uns): 4.6515 +/- 0.13840 *2
GLM-4.7.i1-Q5_K_M(mra): 4.7015 +/- 0.14110 *4 ("worst" overall)
GLM-4.7.Q5_K_M(mra): 4.6877 +/- 0.14012 *3
-ctk q8_0 -ctv q8_0
GLM-4.7-Q5_K_M(bar): 4.6647 +/- 0.13913 *3
GLM-4.7-Q5_K_M(uns): 4.6356 +/- 0.13766 *1
GLM-4.7.i1-Q5_K_M(mra): 4.6430 +/- 0.13789 *2
GLM-4.7.Q5_K_M(mra): 4.6649 +/- 0.13931 *4

english/non-english mixed input:
GLM-4.7-Q5_K_M(bar): 2.3418 +/- 0.04566 *1 ("best" overall)
GLM-4.7-Q5_K_M(uns): 2.3587 +/- 0.04635 *4
GLM-4.7.i1-Q5_K_M(mra): 2.3562 +/- 0.04619 *3
GLM-4.7.Q5_K_M(mra): 2.3521 +/- 0.04621 *2
-ctk q8_0
GLM-4.7-Q5_K_M(bar): 2.3465 +/- 0.04576 *1
GLM-4.7-Q5_K_M(uns): 2.3560 +/- 0.04620 *2
GLM-4.7.i1-Q5_K_M(mra): 2.3751 +/- 0.04703 *4 ("worst" overall)
GLM-4.7.Q5_K_M(mra): 2.3599 +/- 0.04633 *3
-ctk q8_0 -ctv q8_0
GLM-4.7-Q5_K_M(bar): 2.3441 +/- 0.04566 *1
GLM-4.7-Q5_K_M(uns): 2.3660 +/- 0.04647 *3
GLM-4.7.i1-Q5_K_M(mra): 2.3544 +/- 0.04609 *2
GLM-4.7.Q5_K_M(mra): 2.3687 +/- 0.04688 *4

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