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library_name: transformers
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# Model Card for Model ID
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## Model Details
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### Model Description
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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- **Repository:**
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- **Paper
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- **Demo [optional]:** [More Information Needed]
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## Uses
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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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[More Information Needed]
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## Training Details
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### Training Data
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[More Information Needed]
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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- **Training regime:**
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## Evaluation
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### Testing Data, Factors & Metrics
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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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[More Information Needed]
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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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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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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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[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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[More Information Needed]
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## Model Card Contact
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library_name: transformers
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license: mit
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datasets:
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- mlsquare/CLIENT_samantar_mixed_train_val
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language:
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- en
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pipeline_tag: text-generation
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# Model Card for Model ID
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Adapter for mlsquare/pico_seshu_test using LoRA on "model.layers.3.out_proj". Standard use of PEFT on Mamba-hf model
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## Model Details
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### Model Description
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- **Developed by:** MLsquare
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- **Model type:** Next Character Generation
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- **Language(s) (NLP):** All languages in ai4bharat/samanantar dataset
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- **License:** MIT
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## Model Details
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### Model Description
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- **Developed by:** MLsquare
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- **Model type:** Next Character Generation
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- **Language(s) (NLP):** All languages in ai4bharat/samanantar dataset
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- **License:** MIT
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### Model Sources [optional]
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- **Repository:** https://github.com/LegallyCoder/mamba-hf
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- **Paper:** https://arxiv.org/abs/2312.00752
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## Uses
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Refer to the github repository for more information
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### Direct Use
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Refer to the github repository for more information
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## How to Get Started with the Model
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Refer to the github repository: https://github.com/mlsquare/fedem
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## Training Details
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### Training Data
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Individual target and source sentences from the AI4Bharat Samanantar dataset. All 11 language sentences and their translations have been stacked and used for next character generation task.
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### Training Procedure
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Trained on the next character generation task using cross-entropy loss.
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#### Preprocessing [optional]
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converted to raw UTF8 characters before training by using ByT5-large tokenizer
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#### Training Hyperparameters
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- **Training regime:**
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output_dir="mamba",
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per_device_train_batch_size=1,
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per_device_eval_batch_size=1,
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num_train_epochs=4,
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weight_decay=0.1,
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lr_scheduler_type="cosine",
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learning_rate=5e-4,
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fp16=False,
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## Evaluation
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A simple cross-entropy loss has been used to test the pipeline and working of the model.
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## Model Card Contact
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MLsquare
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