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llava-v1.5-7b-lora

Image-Text-to-Text

Transformers

llava

text-generation

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liuhaotian commited on Oct 26, 2023

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-library_name: peft
-base_model: ./checkpoints/vicuna-7b-v1-5
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-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-## Uses
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
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-#### Training Hyperparameters
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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-## Training procedure
-### Framework versions
-- PEFT 0.6.0.dev0

 ---
+inference: false
 ---
+<br>
+<br>
+# LLaVA Model Card
+## Model details
+**Model type:**
+LLaVA 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 date:**
+LLaVA-v1.5-7B-LoRA was trained in October 2023.
+**Paper or resources for more information:**
+https://llava-vl.github.io/
+## 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/haotian-liu/LLaVA/issues
+## Intended use
+**Primary intended uses:**
+The primary use of LLaVA 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
+- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
+- 158K GPT-generated multimodal instruction-following data.
+- 450K academic-task-oriented VQA data mixture.
+- 40K ShareGPT data.
+## Evaluation dataset
+A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.