Instructions to use riytdxc43/vediolargemodels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use riytdxc43/vediolargemodels with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="riytdxc43/vediolargemodels")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("riytdxc43/vediolargemodels", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use riytdxc43/vediolargemodels with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "riytdxc43/vediolargemodels" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "riytdxc43/vediolargemodels", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/riytdxc43/vediolargemodels
- SGLang
How to use riytdxc43/vediolargemodels 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 "riytdxc43/vediolargemodels" \ --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": "riytdxc43/vediolargemodels", "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 "riytdxc43/vediolargemodels" \ --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": "riytdxc43/vediolargemodels", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use riytdxc43/vediolargemodels with Docker Model Runner:
docker model run hf.co/riytdxc43/vediolargemodels
Download faster-whisper-large-v3-turbo-ct2/preprocessor_config.json from riytdxc43/vediolargemodels: direct link, hf CLI and curl.
- Browser
- Download file 340 Bytes
-
https://huggingface.co/riytdxc43/vediolargemodels/resolve/main/faster-whisper-large-v3-turbo-ct2/preprocessor_config.json
- Command line
-
hf download hf://riytdxc43/vediolargemodels/faster-whisper-large-v3-turbo-ct2/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/riytdxc43/vediolargemodels/resolve/main/faster-whisper-large-v3-turbo-ct2/preprocessor_config.json
340 Bytes
| { | |
| "chunk_length": 30, | |
| "feature_extractor_type": "WhisperFeatureExtractor", | |
| "feature_size": 128, | |
| "hop_length": 160, | |
| "n_fft": 400, | |
| "n_samples": 480000, | |
| "nb_max_frames": 3000, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "WhisperProcessor", | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| } | |