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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Eclipse-Senpai/KeyLM-75M-Instruct-GGUF",
	filename="KeyLM-75M-Instruct.F16.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 Eclipse-Senpai/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 Eclipse-Senpai/KeyLM-75M-Instruct-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf Eclipse-Senpai/KeyLM-75M-Instruct-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Eclipse-Senpai/KeyLM-75M-Instruct-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf Eclipse-Senpai/KeyLM-75M-Instruct-GGUF:F16

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 Eclipse-Senpai/KeyLM-75M-Instruct-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf Eclipse-Senpai/KeyLM-75M-Instruct-GGUF:F16

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 Eclipse-Senpai/KeyLM-75M-Instruct-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Eclipse-Senpai/KeyLM-75M-Instruct-GGUF:F16

Use Docker

docker model run hf.co/Eclipse-Senpai/KeyLM-75M-Instruct-GGUF:F16

LM Studio
Jan

vLLM

How to use Eclipse-Senpai/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 "Eclipse-Senpai/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": "Eclipse-Senpai/KeyLM-75M-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Eclipse-Senpai/KeyLM-75M-Instruct-GGUF:F16

Ollama
How to use Eclipse-Senpai/KeyLM-75M-Instruct-GGUF with Ollama:
```
ollama run hf.co/Eclipse-Senpai/KeyLM-75M-Instruct-GGUF:F16
```

Unsloth Studio

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

Docker Model Runner
How to use Eclipse-Senpai/KeyLM-75M-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/Eclipse-Senpai/KeyLM-75M-Instruct-GGUF:F16
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Eclipse-Senpai/KeyLM-75M-Instruct-GGUF:F16

Run and chat with the model

lemonade run user.KeyLM-75M-Instruct-GGUF-F16

List all available models

lemonade list

KeyLM-75M-Instruct-GGUF / README.md

Eclipse-Senpai

Add F16 GGUF and model card

c6596b9 verified 3 days ago

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	---
	license: apache-2.0
	language:
	- en
	base_model: Eclipse-Senpai/KeyLM-75M-Instruct
	base_model_relation: quantized
	pipeline_tag: text-generation
	library_name: gguf
	tags:
	- keylm
	- gguf
	- llama.cpp
	- small-language-model
	- instruct
	---

	# KeyLM-75M-Instruct-GGUF

	GGUF builds of [KeyLM-75M-Instruct](https://huggingface.co/Eclipse-Senpai/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](https://huggingface.co/Eclipse-Senpai/KeyLM-75M-Instruct) 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

	```bash
	# 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](https://huggingface.co/Eclipse-Senpai/KeyLM-75M-Instruct).

	## License

	Apache 2.0.