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library_name: transformers
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# Model Card for
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources
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- **Repository:** [
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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###
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Hardware
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[More Information Needed]
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
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language:
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- en
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license: mit
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library_name: transformers
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datasets:
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- liuhaotian/LLaVA-Instruct-150K
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- liuhaotian/LLaVA-Pretrain
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# Model Card for LLaVa-Phi-2-3B
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [LAION](https://laion.ai/), [SkunkworksAI](https://huggingface.co/SkunkworksAI) & [Ontocord](https://www.ontocord.ai/)
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- **Model type:** LLaVA is an open-source chatbot trained by fine-tuning Phi-2 on GPT-generated multimodal instruction-following data.
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It is an auto-regressive language model, based on the transformer architecture
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- **Finetuned from model:** [Phi-2](https://huggingface.co/microsoft/phi-2)
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- **License:** MIT
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### Model Sources
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- **Repository:** [BakLLaVa](https://github.com/SkunkworksAI/BakLLaVA)
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## Evaluation
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### Benchmarks
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| Model | Parameters |SQA | GQA | TextVQA | POPE |
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| --- | --- | --- | --- | --- | --- |
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| [LLaVA-1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) | 7.3B | 68.0| **62.0** | **58.3** | 85.3 |
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| [MC-LLaVA-3B](https://huggingface.co/visheratin/MC-LLaVA-3b) | 3B | - | 49.6 | 38.59 | - |
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| [LLaVA-Phi](https://arxiv.org/pdf/2401.02330.pdf) | 3B | 68.4 | - | 48.6 | 85.0 |
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| [moondream1](https://huggingface.co/vikhyatk/moondream1) | 1.6B | - | 56.3 | 39.8 | - |
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| **llava-phi-2-3b** | 2.7B | 69.0| 51.2 | 47.0 | 86.0 |
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| **llava-phi-2-3b-siglip** | 2.7B | **70.15%** | 52.56% | 47.99%| **87.00%** |
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