Instructions to use HuanjinYao/DenseConnector-v1.5-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuanjinYao/DenseConnector-v1.5-7B with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="HuanjinYao/DenseConnector-v1.5-7B")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("HuanjinYao/DenseConnector-v1.5-7B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
DenseConnector-v1.5-7B Model Card
Model details
Model type: DenseConnector is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture.
Model info: DenseConnector-v1.5-7B was trained in 05/2024.
Paper or resources for more information: https://github.com/HJYao00/DenseConnector
Paper on Hugging Face: arxiv.org/abs/2405.13800
Training dataset: This model is trained on LLaVA-1.5 dataset.
Large Language Model: Vicuna-7B
License
Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
Where to send questions or comments about the model: https://github.com/HJYao00/DenseConnector/issues
Intended use
Primary intended uses: The primary use of DenseConnector 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.
- Downloads last month
- 3