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 xywang/test.py from Wangtwohappy/T4_code: direct link, hf CLI and curl.
- Browser
- Download file 1.53 kB
-
https://huggingface.co/Wangtwohappy/T4_code/resolve/main/xywang/test.py
- Command line
-
hf download hf://Wangtwohappy/T4_code/xywang/test.py
-
curl -L -o test.py https://huggingface.co/Wangtwohappy/T4_code/resolve/main/xywang/test.py
1.53 kB
| import os | |
| import sys | |
| import argparse | |
| VIDEO_FILE_DIR = "/home/xiuying/Code/xywang/test_videos" | |
| # API服务器的URL | |
| API_URL = "http://127.0.0.1:8010/video-inference/" | |
| PROMPT = "视频里发生了什么?" | |
| for file in os.listdir(VIDEO_FILE_DIR): | |
| if file.endswith(".mp4"): | |
| VIDEO_FILE_PATH = os.path.join(VIDEO_FILE_DIR, file) | |
| command = ( | |
| f"curl -v -X POST '{API_URL}' " | |
| f"-F 'prompt={PROMPT}' " | |
| f"-F 'video_file=@{VIDEO_FILE_PATH};type=video/mp4' " | |
| f"-F 'sampling_method=content_aware' " | |
| f"-F 'sampling_rate=28' " | |
| ) | |
| print("将要执行以下 cURL 命令:") | |
| print("---------------------------------") | |
| print(command) | |
| print("---------------------------------") | |
| print("\n正在执行...\n") | |
| os.system(command) | |
| for file in os.listdir(VIDEO_FILE_DIR): | |
| if file.endswith(".mp4"): | |
| VIDEO_FILE_PATH = os.path.join(VIDEO_FILE_DIR, file) | |
| command = ( | |
| f"curl -v -X POST '{API_URL}' " | |
| f"-F 'prompt={PROMPT}' " | |
| f"-F 'video_file=@{VIDEO_FILE_PATH};type=video/mp4' " | |
| f"-F 'sampling_method=uniform' " | |
| f"-F 'sampling_rate=28' " | |
| ) | |
| print("将要执行以下 cURL 命令:") | |
| print("---------------------------------") | |
| print(command) | |
| print("---------------------------------") | |
| print("\n正在执行...\n") | |
| os.system(command) | |
| print("\n\n✅ 测试脚本执行完毕。") | |