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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use morikomorizz/GLM-Collection 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 morikomorizz/GLM-Collection:IQ2_M
# Run inference directly in the terminal:
llama cli -hf morikomorizz/GLM-Collection:IQ2_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf morikomorizz/GLM-Collection:IQ2_M
# Run inference directly in the terminal:
llama cli -hf morikomorizz/GLM-Collection:IQ2_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 morikomorizz/GLM-Collection:IQ2_M
# Run inference directly in the terminal:
./llama-cli -hf morikomorizz/GLM-Collection:IQ2_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 morikomorizz/GLM-Collection:IQ2_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf morikomorizz/GLM-Collection:IQ2_M

Use Docker

docker model run hf.co/morikomorizz/GLM-Collection:IQ2_M

LM Studio
Jan

vLLM

How to use morikomorizz/GLM-Collection with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "morikomorizz/GLM-Collection"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "morikomorizz/GLM-Collection",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/morikomorizz/GLM-Collection:IQ2_M

Ollama
How to use morikomorizz/GLM-Collection with Ollama:
```
ollama run hf.co/morikomorizz/GLM-Collection:IQ2_M
```

Unsloth Studio

How to use morikomorizz/GLM-Collection 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 morikomorizz/GLM-Collection 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 morikomorizz/GLM-Collection to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use morikomorizz/GLM-Collection with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf morikomorizz/GLM-Collection:IQ2_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": "morikomorizz/GLM-Collection:IQ2_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use morikomorizz/GLM-Collection with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf morikomorizz/GLM-Collection:IQ2_M

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 "morikomorizz/GLM-Collection:IQ2_M" \
  --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"

Docker Model Runner
How to use morikomorizz/GLM-Collection with Docker Model Runner:
```
docker model run hf.co/morikomorizz/GLM-Collection:IQ2_M
```

Lemonade

How to use morikomorizz/GLM-Collection with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull morikomorizz/GLM-Collection:IQ2_M

Run and chat with the model

lemonade run user.GLM-Collection-IQ2_M

List all available models

lemonade list

Hermes Agent

How to use morikomorizz/GLM-Collection with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf morikomorizz/GLM-Collection:IQ2_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 morikomorizz/GLM-Collection:IQ2_M

Run Hermes

hermes

Atomic Chat

morikomorizz commited on Jul 6

Commit

ea149f6

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@@ -27,7 +27,9 @@ The frontmost FFN layers in the first block are all set to Q8_0, while the remai
 ### The "Garbage In, Garbage Out" Hypothesis
 > Please note that the Quantization Override (Q8) applied to the very first layers in these custom models is an intentional experiment. The core hypothesis we are testing challenges the "Garbage In, Garbage Out" concept: if the frontmost layers (which capture and process the initial input context) are extensively compressed, will it inevitably trap the model with degraded inputs and result in poor final generations?
 > And conversely: if the frontmost layers are aggressively preserved, will it allow the model to capture clear and clean inputs, leading to superior final generation results?
 > **Call for Feedback:** If you are willing to test these models, please share your performance benchmarks, perplexity scores, or qualitative observations in the Community Discussions or Issues tab. Your field reports are crucial to help determine whether early-layer degradation is a fatal flaw or a manageable trade-off in MoE architectures.
 ---

 ### The "Garbage In, Garbage Out" Hypothesis
 > Please note that the Quantization Override (Q8) applied to the very first layers in these custom models is an intentional experiment. The core hypothesis we are testing challenges the "Garbage In, Garbage Out" concept: if the frontmost layers (which capture and process the initial input context) are extensively compressed, will it inevitably trap the model with degraded inputs and result in poor final generations?
+>
 > And conversely: if the frontmost layers are aggressively preserved, will it allow the model to capture clear and clean inputs, leading to superior final generation results?
+>
 > **Call for Feedback:** If you are willing to test these models, please share your performance benchmarks, perplexity scores, or qualitative observations in the Community Discussions or Issues tab. Your field reports are crucial to help determine whether early-layer degradation is a fatal flaw or a manageable trade-off in MoE architectures.
 ---