Instructions to use agestau/f_cap_100recs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use agestau/f_cap_100recs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="agestau/f_cap_100recs")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("agestau/f_cap_100recs") model = AutoModelForMultimodalLM.from_pretrained("agestau/f_cap_100recs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use agestau/f_cap_100recs with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "agestau/f_cap_100recs" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "agestau/f_cap_100recs", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/agestau/f_cap_100recs
- SGLang
How to use agestau/f_cap_100recs 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 "agestau/f_cap_100recs" \ --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": "agestau/f_cap_100recs", "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 "agestau/f_cap_100recs" \ --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": "agestau/f_cap_100recs", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use agestau/f_cap_100recs with Docker Model Runner:
docker model run hf.co/agestau/f_cap_100recs
Download pytorch_model.bin from agestau/f_cap_100recs: direct link, hf CLI and curl.
- Browser
- Download file 990 MB
-
https://huggingface.co/agestau/f_cap_100recs/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://agestau/f_cap_100recs/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/agestau/f_cap_100recs/resolve/main/pytorch_model.bin
990 MB
- Xet hash:
- 2f23dbe6af4452bb26c32d7ce86cc4f043b3a69310a332cc6eddc34bfa90d90c
- Size of remote file:
- 990 MB
- SHA256:
- bafea4ed6f3324cd792013f180d59cb76a6643680a63d3df27ce062206ca2087
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.