TWO
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sutra-mlt256-v2

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 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [Two Platforms]
-- **Model type:** [Tokenizer for SUTRA Models. SUTRA Models are dual transformer based Multilingual LLMs]
-- **Language(s) (NLP):** [50+ Languages including English, Hindi, Gujarati, Bengali, Tamil, Korean, Arabic, Japanese, French, German etc.]
-- **License:** [Proprietary]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
-<!-- 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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-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
-### Training Data
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-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
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-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
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-### Testing Data, Factors & Metrics
-#### Testing Data
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-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).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 **BibTeX:**
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-**APA:**
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-## Glossary [optional]
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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 ### Model Description
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **Developed by:** Two Platforms
+- **Model type:** Tokenizer for SUTRA Models. SUTRA Models are dual transformer based Multilingual LLMs
+- **Language(s) (NLP):** 50+ Languages including English, Hindi, Gujarati, Bengali, Tamil, Korean, Arabic, Japanese, French, German etc.
+- **License:** Proprietary
+### Model Sources
+- **Paper:** [SUTRA: Scalable Multilingual Language Model Architecture](https://huggingface.co/papers/2405.06694)
+- **Demo:** [SUTRA tokenizer comparison](https://huggingface.co/spaces/TWO/sutra-tokenizer-comparison)
+## Citation
 **BibTeX:**
+@misc{bendale2023sutra,
+  author       = {Abhijit Bendale and Michael Sapienza and Steven Ripplinger and Simon Gibbs and Jaewon Lee and Pranav Mistry},
+  title        = {SUTRA: Scalable Multilingual Language Model Architecture},
+  howpublished = {arXiv preprint arXiv:2405.06694},
+  year         = {2024}
+}