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
File size: 1,529 Bytes
f8ba0eb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | 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。")
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