Instructions to use Eclipse-Senpai/KeyLM-75M-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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 Settings
- 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
Add F16 GGUF and model card
Browse files- .gitattributes +1 -0
- KeyLM-75M-Instruct.F16.gguf +3 -0
- README.md +52 -0
.gitattributes
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KeyLM-75M-Instruct.F16.gguf
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---
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license: apache-2.0
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language:
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- en
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base_model: Eclipse-Senpai/KeyLM-75M-Instruct
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base_model_relation: quantized
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pipeline_tag: text-generation
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library_name: gguf
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tags:
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- keylm
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- gguf
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- llama.cpp
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- small-language-model
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- instruct
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---
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# KeyLM-75M-Instruct-GGUF
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GGUF builds of [**KeyLM-75M-Instruct**](https://huggingface.co/Eclipse-Senpai/KeyLM-75M-Instruct) for `llama.cpp`, LM Studio, Ollama, and other GGUF runtimes.
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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.
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## Files
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| File | Quant | Size | Notes |
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|---|---|---|---|
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| `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. |
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## Run with llama.cpp
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```bash
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# straight from the Hub
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llama-cli -hf Eclipse-Senpai/KeyLM-75M-Instruct-GGUF -cnv
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# or a local file
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llama-cli -m KeyLM-75M-Instruct.F16.gguf -cnv
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```
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The chat template (`User:` / `Assistant:`, assistant turns ending with `</s>`) is embedded in the GGUF, so conversation mode (`-cnv`) applies it automatically.
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## LM Studio / Ollama
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- **LM Studio:** load the `.gguf`; the embedded chat template is detected automatically.
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- **Ollama:** `ollama run hf.co/Eclipse-Senpai/KeyLM-75M-Instruct-GGUF`
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## Notes & limitations
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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).
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## License
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Apache 2.0.
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