Instructions to use ysn-rfd/beecoder-220M-python-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use ysn-rfd/beecoder-220M-python-Q8_0-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ysn-rfd/beecoder-220M-python-Q8_0-GGUF", filename="beecoder-220m-python-q8_0.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use ysn-rfd/beecoder-220M-python-Q8_0-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ysn-rfd/beecoder-220M-python-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama-cli -hf ysn-rfd/beecoder-220M-python-Q8_0-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ysn-rfd/beecoder-220M-python-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama-cli -hf ysn-rfd/beecoder-220M-python-Q8_0-GGUF:Q8_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 ysn-rfd/beecoder-220M-python-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf ysn-rfd/beecoder-220M-python-Q8_0-GGUF:Q8_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 ysn-rfd/beecoder-220M-python-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ysn-rfd/beecoder-220M-python-Q8_0-GGUF:Q8_0
Use Docker
docker model run hf.co/ysn-rfd/beecoder-220M-python-Q8_0-GGUF:Q8_0
- LM Studio
- Jan
- vLLM
How to use ysn-rfd/beecoder-220M-python-Q8_0-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ysn-rfd/beecoder-220M-python-Q8_0-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ysn-rfd/beecoder-220M-python-Q8_0-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ysn-rfd/beecoder-220M-python-Q8_0-GGUF:Q8_0
- Ollama
How to use ysn-rfd/beecoder-220M-python-Q8_0-GGUF with Ollama:
ollama run hf.co/ysn-rfd/beecoder-220M-python-Q8_0-GGUF:Q8_0
- Unsloth Studio new
How to use ysn-rfd/beecoder-220M-python-Q8_0-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 ysn-rfd/beecoder-220M-python-Q8_0-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 ysn-rfd/beecoder-220M-python-Q8_0-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ysn-rfd/beecoder-220M-python-Q8_0-GGUF to start chatting
- Docker Model Runner
How to use ysn-rfd/beecoder-220M-python-Q8_0-GGUF with Docker Model Runner:
docker model run hf.co/ysn-rfd/beecoder-220M-python-Q8_0-GGUF:Q8_0
- Lemonade
How to use ysn-rfd/beecoder-220M-python-Q8_0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ysn-rfd/beecoder-220M-python-Q8_0-GGUF:Q8_0
Run and chat with the model
lemonade run user.beecoder-220M-python-Q8_0-GGUF-Q8_0
List all available models
lemonade list
ysn-rfd/beecoder-220M-python-Q8_0-GGUF
This model was converted to GGUF format from BEE-spoke-data/beecoder-220M-python using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo ysn-rfd/beecoder-220M-python-Q8_0-GGUF --hf-file beecoder-220m-python-q8_0.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo ysn-rfd/beecoder-220M-python-Q8_0-GGUF --hf-file beecoder-220m-python-q8_0.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo ysn-rfd/beecoder-220M-python-Q8_0-GGUF --hf-file beecoder-220m-python-q8_0.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo ysn-rfd/beecoder-220M-python-Q8_0-GGUF --hf-file beecoder-220m-python-q8_0.gguf -c 2048
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Model tree for ysn-rfd/beecoder-220M-python-Q8_0-GGUF
Base model
BEE-spoke-data/smol_llama-220M-GQA