Instructions to use ermu2001/pllava-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ermu2001/pllava-13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ermu2001/pllava-13b")# Load model directly from transformers import AutoProcessor, AutoModelForSeq2SeqLM processor = AutoProcessor.from_pretrained("ermu2001/pllava-13b") model = AutoModelForSeq2SeqLM.from_pretrained("ermu2001/pllava-13b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ermu2001/pllava-13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ermu2001/pllava-13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ermu2001/pllava-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ermu2001/pllava-13b
- SGLang
How to use ermu2001/pllava-13b 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 "ermu2001/pllava-13b" \ --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": "ermu2001/pllava-13b", "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 "ermu2001/pllava-13b" \ --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": "ermu2001/pllava-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ermu2001/pllava-13b with Docker Model Runner:
docker model run hf.co/ermu2001/pllava-13b
PLLaVA Model Card
Model details
Model type: PLLaVA-13B is an open-source video-language chatbot trained by fine-tuning Image-LLM on video instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: llava-hf/llava-v1.6-vicuna-13b-hf
Model date: PLLaVA-13B was trained in April 2024.
Paper or resources for more information:
- github repo: https://github.com/magic-research/PLLaVA
- project page: https://pllava.github.io/
- paper link: https://arxiv.org/abs/2404.16994
License
llava-hf/llava-v1.6-vicuna-13b-hf license.
Where to send questions or comments about the model: https://github.com/magic-research/PLLaVA/issues
Intended use
Primary intended uses: The primary use of PLLaVA is research on large multimodal models and chatbots.
Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
Training dataset
Video-Instruct-Tuning data of OpenGVLab/VideoChat2-IT
Evaluation dataset
A collection of 6 benchmarks, including 5 VQA benchmarks and 1 recent benchmarks specifically proposed for Video-LMMs.
- Downloads last month
- 5