Instructions to use abocide/matchcommentary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abocide/matchcommentary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="abocide/matchcommentary")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("abocide/matchcommentary", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use abocide/matchcommentary with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "abocide/matchcommentary" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abocide/matchcommentary", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/abocide/matchcommentary
- SGLang
How to use abocide/matchcommentary with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "abocide/matchcommentary" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abocide/matchcommentary", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "abocide/matchcommentary" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abocide/matchcommentary", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use abocide/matchcommentary with Docker Model Runner:
docker model run hf.co/abocide/matchcommentary
| { | |
| "architectures": ["MatchcommentaryModel"], | |
| "model_type": "Matchcommentary", | |
| "llm_ckpt": "meta-llama/Meta-Llama-3-8B-Instruct", | |
| "tokenizer_ckpt": "meta-llama/Meta-Llama-3-8B-Instruct", | |
| "max_frame_pos": 128, | |
| "window": 15, | |
| "num_query_tokens": 32, | |
| "num_video_query_token": 32, | |
| "num_features": 512, | |
| "fps": 0.5, | |
| "max_token_length": 128, | |
| "feature_subdir": "ResNET_PCA512", | |
| "torch_dtype": "float16", | |
| "transformers_version": "4.42.3", | |
| "description": "MatchcommentaryModel model for automatic soccer game commentary generation, trained on MatchTime dataset" | |
| } |