Instructions to use Wangtwohappy/T4_code 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 Wangtwohappy/T4_code 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 Wangtwohappy/T4_code:Q4_K_M # Run inference directly in the terminal: llama cli -hf Wangtwohappy/T4_code:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Wangtwohappy/T4_code:Q4_K_M # Run inference directly in the terminal: llama cli -hf Wangtwohappy/T4_code: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 Wangtwohappy/T4_code:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Wangtwohappy/T4_code: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 Wangtwohappy/T4_code:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Wangtwohappy/T4_code:Q4_K_M
Use Docker
docker model run hf.co/Wangtwohappy/T4_code:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Wangtwohappy/T4_code with Ollama:
ollama run hf.co/Wangtwohappy/T4_code:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use Wangtwohappy/T4_code with Docker Model Runner:
docker model run hf.co/Wangtwohappy/T4_code:Q4_K_M
- Lemonade
How to use Wangtwohappy/T4_code with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Wangtwohappy/T4_code:Q4_K_M
Run and chat with the model
lemonade run user.T4_code-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download API_Transformers/delete.py from Wangtwohappy/T4_code: direct link, hf CLI and curl.
- Browser
- Download file 400 Bytes
-
https://huggingface.co/Wangtwohappy/T4_code/resolve/main/API_Transformers/delete.py
- Command line
-
hf download hf://Wangtwohappy/T4_code/API_Transformers/delete.py
-
curl -L -o delete.py https://huggingface.co/Wangtwohappy/T4_code/resolve/main/API_Transformers/delete.py
400 Bytes
| import os | |
| files = ["/mnt/data/xiuying/Code/local_deploy/video/new/Clips_60s/video_part_017.mp4", | |
| "/mnt/data/xiuying/Code/local_deploy/video/new/Clips_60s/video_part_018.mp4", | |
| "/mnt/data/xiuying/Code/local_deploy/video/new/Clips_60s/video_part_019.mp4", | |
| "/mnt/data/xiuying/Code/local_deploy/video/new/Clips_60s/video_part_020.mp4"] | |
| for file in files: | |
| os.remove(file) | |
| print(f"Deleted {file}") |