Instructions to use AnhLee0/Lab22 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use AnhLee0/Lab22 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 AnhLee0/Lab22:Q4_K_M # Run inference directly in the terminal: llama cli -hf AnhLee0/Lab22:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf AnhLee0/Lab22:Q4_K_M # Run inference directly in the terminal: llama cli -hf AnhLee0/Lab22: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 AnhLee0/Lab22:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf AnhLee0/Lab22: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 AnhLee0/Lab22:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf AnhLee0/Lab22:Q4_K_M
Use Docker
docker model run hf.co/AnhLee0/Lab22:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use AnhLee0/Lab22 with Ollama:
ollama run hf.co/AnhLee0/Lab22:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use AnhLee0/Lab22 with Docker Model Runner:
docker model run hf.co/AnhLee0/Lab22:Q4_K_M
- Lemonade
How to use AnhLee0/Lab22 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AnhLee0/Lab22:Q4_K_M
Run and chat with the model
lemonade run user.Lab22-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download data/pref/eval.parquet from AnhLee0/Lab22: direct link, hf CLI and curl.
- Browser
- Download file 121 kB
-
https://huggingface.co/AnhLee0/Lab22/resolve/main/data/pref/eval.parquet
- Command line
-
hf download hf://AnhLee0/Lab22/data/pref/eval.parquet
-
curl -L -o eval.parquet https://huggingface.co/AnhLee0/Lab22/resolve/main/data/pref/eval.parquet
121 kB
- Xet hash:
- 112a96f055c4c0c5f177d231f8e5661049bc96ee7a8a51bdb146a3b23e2c9f90
- Size of remote file:
- 121 kB
- SHA256:
- c3fa4f6b709c31ed40fa8147bb353add204f12170c5051967831bddbc13f1cba
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.