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--- |
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language: en |
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tags: |
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- multimodal |
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- text |
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- image |
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license: "apache-2.0" |
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datasets: |
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- obelisc |
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--- |
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# Model Card for m4-80b |
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<!-- Provide a quick summary of what the model is/does. [Optional] --> |
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Some cool model... |
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# Table of Contents |
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- [Model Card for m4-80b](#model-card-for--model_id-) |
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- [Table of Contents](#table-of-contents) |
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- [Model Details](#model-details) |
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- [Model Description](#model-description) |
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- [Uses](#uses) |
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- [Direct Use](#direct-use) |
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- [Downstream Use [Optional]](#downstream-use-optional) |
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- [Out-of-Scope Use](#out-of-scope-use) |
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- [Bias, Risks, and Limitations](#bias-risks-and-limitations) |
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- [Recommendations](#recommendations) |
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- [Training Details](#training-details) |
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- [Training Data](#training-data) |
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- [Training Procedure](#training-procedure) |
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- [Preprocessing](#preprocessing) |
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- [Speeds, Sizes, Times](#speeds-sizes-times) |
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- [Evaluation](#evaluation) |
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- [Testing Data, Factors & Metrics](#testing-data-factors--metrics) |
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- [Testing Data](#testing-data) |
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- [Factors](#factors) |
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- [Metrics](#metrics) |
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- [Results](#results) |
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- [Model Examination](#model-examination) |
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- [Environmental Impact](#environmental-impact) |
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- [Technical Specifications [optional]](#technical-specifications-optional) |
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- [Model Architecture and Objective](#model-architecture-and-objective) |
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- [Compute Infrastructure](#compute-infrastructure) |
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- [Hardware](#hardware) |
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- [Software](#software) |
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- [Citation](#citation) |
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- [Glossary [optional]](#glossary-optional) |
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- [More Information [optional]](#more-information-optional) |
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- [Model Card Authors [optional]](#model-card-authors-optional) |
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- [Model Card Contact](#model-card-contact) |
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- [How to Get Started with the Model](#how-to-get-started-with-the-model) |
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# Model Details |
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## Model Description |
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Some cool model... |
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- **Developed by:** HuggingFace |
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- **Model type:** Multi-modal model (text+image) |
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- **Language(s) (NLP):** en |
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- **License:** apache-2.0 |
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- **Parent Model:** [laion/CLIP-ViT-H-14-laion2B-s32B-b79K](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K) and [huggingface/llama-65b](https://huggingface.co/huggingface/llama-65b) |
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- **Resources for more information:** More information needed |
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- [GitHub Repo](https://github.com/huggingface/m4/) |
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- Associated Paper: [Flamingo: a Visual Language Model for Few-Shot Learning](https://arxiv.org/abs/2204.14198) |
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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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<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info 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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<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info 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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<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info 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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Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. |
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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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# Training Details |
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## Training Data |
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<!-- This should link to a Data 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 on training data 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 |
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More information needed |
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### Speeds, Sizes, Times |
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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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## Testing Data, Factors & Metrics |
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### Testing Data |
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<!-- This should link to a Data 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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More information needed |
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# Model Examination |
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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:** 64 nodes of 8x 80GB A100 gpus, EFA network |
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- **Hours used:** ~672 node hours |
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- **Cloud Provider:** AWS Sagemaker |
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- **Carbon Emitted:** unknown |
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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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More information needed |
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### Hardware |
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The training was performed on AWS SageMaker cluster with 64 nodes of 8x80GB A100 GPUs (512 GPUs total). The cluster uses the current EFA network which provides about 340GBps throughput. |
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As the network is quite slow for the needs of DeepSpeed ZeRO-3 we were only able to clock ~90 TFLOPs. |
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### Software |
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The training software is built on top of HuggingFace Transformers + Accelerate, and DeepSpeed ZeRO-3. Plus [WebDataset](https://github.com/webdataset/webdataset) for data loading. |
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# Citation |
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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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More information needed |
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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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<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. --> |
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V, i, c, t, o, r, ,, , S, t, a, s, ,, , X, X, X |
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# Model Card Contact |
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More information needed |
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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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<details> |
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<summary> Click to expand </summary> |
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More information needed |
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</details> |
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