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 vllm-deploy/test.py from Wangtwohappy/T4_code: direct link, hf CLI and curl.
- Browser
- Download file 1.09 kB
-
https://huggingface.co/Wangtwohappy/T4_code/resolve/main/vllm-deploy/test.py
- Command line
-
hf download hf://Wangtwohappy/T4_code/vllm-deploy/test.py
-
curl -L -o test.py https://huggingface.co/Wangtwohappy/T4_code/resolve/main/vllm-deploy/test.py
1.09 kB
| from openai import OpenAI | |
| import base64 | |
| # API configuration | |
| openai_api_key = "token-abc123" # API key must match the one set when launching the service | |
| openai_api_base = "http://localhost:8000/v1" | |
| client = OpenAI( | |
| api_key=openai_api_key, | |
| base_url=openai_api_base, | |
| ) | |
| # Read and encode local image | |
| with open('/mnt/data/xiuying/Code/xywang/demo.jpeg', 'rb') as file: | |
| image = "data:image/jpeg;base64," + base64.b64encode(file.read()).decode('utf-8') | |
| chat_response = client.chat.completions.create( | |
| model="MiniCPM-V-4-Q4_K_M.gguf", # Specify model path or HuggingFace ID | |
| messages=[{ | |
| "role": "user", | |
| "content": [ | |
| {"type": "text", "text": "Please describe this image"}, | |
| { | |
| "type": "image_url", | |
| "image_url": { | |
| "url": image, # Supports network image URLs | |
| }, | |
| }, | |
| ], | |
| }], | |
| extra_body={ | |
| "stop_token_ids": [1, 73440] | |
| } | |
| ) | |
| print("Chat response:", chat_response) | |
| print("Chat response content:", chat_response.choices[0].message.content) |