Instructions to use leok7v/KeyLM-75M-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use leok7v/KeyLM-75M-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="leok7v/KeyLM-75M-Instruct-GGUF",
	filename="KeyLM-75M-Instruct.Q4_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use leok7v/KeyLM-75M-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf leok7v/KeyLM-75M-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf leok7v/KeyLM-75M-Instruct-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf leok7v/KeyLM-75M-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf leok7v/KeyLM-75M-Instruct-GGUF:Q4_0

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 leok7v/KeyLM-75M-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf leok7v/KeyLM-75M-Instruct-GGUF:Q4_0

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 leok7v/KeyLM-75M-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf leok7v/KeyLM-75M-Instruct-GGUF:Q4_0

Use Docker

docker model run hf.co/leok7v/KeyLM-75M-Instruct-GGUF:Q4_0

LM Studio
Jan

vLLM

How to use leok7v/KeyLM-75M-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "leok7v/KeyLM-75M-Instruct-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": "leok7v/KeyLM-75M-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/leok7v/KeyLM-75M-Instruct-GGUF:Q4_0

Ollama
How to use leok7v/KeyLM-75M-Instruct-GGUF with Ollama:
```
ollama run hf.co/leok7v/KeyLM-75M-Instruct-GGUF:Q4_0
```

Unsloth Studio new

How to use leok7v/KeyLM-75M-Instruct-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 leok7v/KeyLM-75M-Instruct-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 leok7v/KeyLM-75M-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for leok7v/KeyLM-75M-Instruct-GGUF to start chatting

Docker Model Runner
How to use leok7v/KeyLM-75M-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/leok7v/KeyLM-75M-Instruct-GGUF:Q4_0
```

Lemonade

How to use leok7v/KeyLM-75M-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull leok7v/KeyLM-75M-Instruct-GGUF:Q4_0

Run and chat with the model

lemonade run user.KeyLM-75M-Instruct-GGUF-Q4_0

List all available models

lemonade list

KeyLM-75M-Instruct-GGUF

GGUF builds of KeyLM-75M-Instruct for llama.cpp, LM Studio, Ollama, and other GGUF runtimes.

KeyLM is a 75M-parameter instruction-tuned language model trained from scratch on approximately 18 billion tokens. See the main model card for benchmarks, training details, limitations, and the transformers (safetensors) version.

Files

File	Quant	Size	Notes
`KeyLM-75M-Instruct.F16.gguf`	F16	~144 MB	Full precision and recommended. The model is already tiny, so there is little reason to quantize further.

Run with llama.cpp

# straight from the Hub
llama-cli -hf Eclipse-Senpai/KeyLM-75M-Instruct-GGUF -cnv

# or a local file
llama-cli -m KeyLM-75M-Instruct.F16.gguf -cnv

The chat template (User: / Assistant:, assistant turns ending with </s>) is embedded in the GGUF, so conversation mode (-cnv) applies it automatically.

LM Studio / Ollama

LM Studio: load the .gguf; the embedded chat template is detected automatically.
Ollama: ollama run hf.co/Eclipse-Senpai/KeyLM-75M-Instruct-GGUF

Notes & limitations

KeyLM is a tiny model: good at simple instruction following and short chat, near random chance on knowledge/reasoning benchmarks. It is not a factual assistant. Full numbers and caveats are on the main model card.

License

Apache 2.0.

Downloads last month: -

GGUF

Model size

75.3M params

Architecture

qwen3

Hardware compatibility

4-bit

8-bit

Model tree for leok7v/KeyLM-75M-Instruct-GGUF

Base model

Eclipse-Senpai/KeyLM-75M

Finetuned

Eclipse-Senpai/KeyLM-75M-Instruct

Quantized

(2)

this model