Instructions to use yasserrmd/glm5.1-distill-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use yasserrmd/glm5.1-distill-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 yasserrmd/glm5.1-distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf yasserrmd/glm5.1-distill-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 yasserrmd/glm5.1-distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf yasserrmd/glm5.1-distill-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 yasserrmd/glm5.1-distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf yasserrmd/glm5.1-distill-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 yasserrmd/glm5.1-distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf yasserrmd/glm5.1-distill-GGUF:Q4_K_M

Use Docker

docker model run hf.co/yasserrmd/glm5.1-distill-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use yasserrmd/glm5.1-distill-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yasserrmd/glm5.1-distill-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": "yasserrmd/glm5.1-distill-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yasserrmd/glm5.1-distill-GGUF:Q4_K_M

Ollama
How to use yasserrmd/glm5.1-distill-GGUF with Ollama:
```
ollama run hf.co/yasserrmd/glm5.1-distill-GGUF:Q4_K_M
```

Unsloth Studio

How to use yasserrmd/glm5.1-distill-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 yasserrmd/glm5.1-distill-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 yasserrmd/glm5.1-distill-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for yasserrmd/glm5.1-distill-GGUF to start chatting

How to use yasserrmd/glm5.1-distill-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf yasserrmd/glm5.1-distill-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": "yasserrmd/glm5.1-distill-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use yasserrmd/glm5.1-distill-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf yasserrmd/glm5.1-distill-GGUF:Q4_K_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 "yasserrmd/glm5.1-distill-GGUF:Q4_K_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 yasserrmd/glm5.1-distill-GGUF with Docker Model Runner:
```
docker model run hf.co/yasserrmd/glm5.1-distill-GGUF:Q4_K_M
```

Lemonade

How to use yasserrmd/glm5.1-distill-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull yasserrmd/glm5.1-distill-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.glm5.1-distill-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use yasserrmd/glm5.1-distill-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 yasserrmd/glm5.1-distill-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 yasserrmd/glm5.1-distill-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

glm5.1-distill-GGUF

GGUF quantizations of yasserrmd/glm5.1-distill, produced with convert_hf_to_gguf.py and llama-quantize from ggml-org/llama.cpp.

The quant ladder mirrors Liquid AI's own LFM2.5-1.2B GGUF releases (e.g. LiquidAI/LFM2.5-1.2B-Base-GGUF).

Files

File	Quantization	Size
`glm5.1-distill-BF16.gguf`	BF16	2.18 GB
`glm5.1-distill-Q4_0.gguf`	Q4_0	664 MB
`glm5.1-distill-Q4_K_M.gguf`	Q4_K_M	697 MB
`glm5.1-distill-Q5_K_M.gguf`	Q5_K_M	804 MB
`glm5.1-distill-Q6_K.gguf`	Q6_K	918 MB
`glm5.1-distill-Q8_0.gguf`	Q8_0	1.16 GB

Quickstart with `llama.cpp`

Run any quant directly from the Hub:

llama-cli -hf yasserrmd/glm5.1-distill-GGUF:Q4_K_M     --jinja --ctx-size 32768     --temp 0.1 --top-k 50 --top-p 0.1 --repeat-penalty 1.05

Or download manually and serve via llama-server:

huggingface-cli download yasserrmd/glm5.1-distill-GGUF     --include "*Q4_K_M*" --local-dir ./glm5.1-distill-GGUF

llama-server     --model ./glm5.1-distill-GGUF/glm5.1-distill-Q4_K_M.gguf     --alias "yasserrmd/glm5.1-distill"     --threads -1 --n-gpu-layers 99     --ctx-size 32768 --port 8001     --temp 0.1 --top-k 50 --top-p 0.1 --repeat-penalty 1.05     --jinja

The recommended sampling parameters above are the official ones published by Liquid AI for the LFM2.5 family.

Quickstart with Ollama

ollama run hf.co/yasserrmd/glm5.1-distill-GGUF:Q4_K_M

Choosing a quant

Use case	Recommended
Maximum quality, plenty of RAM	`Q8_0` or `Q6_K`
Balanced default	`Q4_K_M` (matches Liquid AI's recommendation)
Smallest footprint, mobile / IoT	`Q4_0`
Lossless reference	`BF16` (only if you need it for further re-quantization)

Note: imatrix-based quantization is currently not supported for the LFM2 architecture in upstream llama.cpp (issue #14979). These files are plain k-quants, the same scheme used in Liquid AI's official GGUF releases.

Source model

For training data, hyperparameters, evaluation, and limitations see the source repo: yasserrmd/glm5.1-distill.

Downloads last month: 80

GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for yasserrmd/glm5.1-distill-GGUF

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

yasserrmd/glm5.1-distill

Quantized

(2)

this model

glm5.1-distill-GGUF

Files

Quickstart with llama.cpp

Quickstart with Ollama

Choosing a quant

Source model

Model tree for yasserrmd/glm5.1-distill-GGUF

Quickstart with `llama.cpp`