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/outputs/qwen_30s.json from Wangtwohappy/T4_code: direct link, hf CLI and curl.
- Browser
- Download file 775 Bytes
-
https://huggingface.co/Wangtwohappy/T4_code/resolve/main/API_Transformers/outputs/qwen_30s.json
- Command line
-
hf download hf://Wangtwohappy/T4_code/API_Transformers/outputs/qwen_30s.json
-
curl -L -o qwen_30s.json https://huggingface.co/Wangtwohappy/T4_code/resolve/main/API_Transformers/outputs/qwen_30s.json
775 Bytes
| { | |
| "/mnt/data/xiuying/Code/local_deploy/messi/Clips_30s/messi_part_001.mp4": { | |
| "response": "The video shows a soccer match between FC Barcelona and Malaga. The match took place on January 23, 2016, and was part of La Liga. The video appears to be a freeze frame from a soccer game, with the scoreboard visible in the background. The scoreboard shows that FC Barcelona is currently leading with a score of 1-0. The video is presented by WOUVA.", | |
| "tokens_per_second": 4.974079275567591, | |
| "peak_gpu_memory_mb": 9376.375, | |
| "num_generated_tokens": 84, | |
| "inference_time": 21.924885272979736, | |
| "cpu_usage": 60.2, | |
| "cpu_core_utilization": [ | |
| 60.4, | |
| 56.6, | |
| 56.5, | |
| 67.3 | |
| ] | |
| } | |
| } |