Model Card for Llama-2-7b Marathi Fine-Tuned on Willow16 Marathi 4k Dataset

This model card provides essential information about the Llama-2-7b model fine-tuned for Marathi language on the Willow16 Marathi dataset consisting of 4k rows. The model is designed for natural language generation tasks and text completion in Marathi.

Model Details

Model Description

• Developed by: [Your Name or Team]
• Model type: Text Generation
• Language(s) (NLP): Marathi
• License: [Specify the License]
• Finetuned from model: Llama-2-7b

Model Sources

• Repository: [Link to the Model Repository]
• Paper [optional]: [Link to Model's Research Paper]
• Demo [optional]: [Link to Model Demo]

Uses

Direct Use

• The Llama-2-7b Marathi model can be directly used for various text generation tasks in the Marathi language, including creative writing, content generation, and more.

Downstream Use

• Researchers and developers can fine-tune this model on specific tasks, such as language translation, chatbots, or content summarization, using the Willow16 Marathi 4k dataset as a basis.

Out-of-Scope Use

• While the model excels at many text generation tasks, it may not perform well in cases requiring highly domain-specific language or specialized knowledge.

Bias, Risks, and Limitations

• Like most language models, the Llama-2-7b Marathi model may inadvertently perpetuate biases present in the training data. Care should be taken to evaluate its outputs and ensure fairness and inclusivity in applications.

Recommendations

• Users should be aware of the model's limitations and potential biases and consider employing additional validation processes when using it in applications that could impact individuals or communities.

How to Get Started with the Model

• To get started with the Llama-2-7b Marathi model, you can use the provided code examples to generate text in Marathi based on prompts.

Training Details

Training Data

• The model was fine-tuned on the Willow16 Marathi 4k dataset, a collection of Marathi instruction input-output pairs.

Training Procedure

• Preprocessing involved tokenization and data cleaning specific to Marathi text.
• Training hyperparameters included a learning rate of [Specify Learning Rate], batch size of [Specify Batch Size], and [Other Hyperparameters].

Speeds, Sizes, Times

• The model training process took [Specify Training Time] on hardware with [Specify Hardware Details].
• The trained model size is approximately [Specify Model Size].

Evaluation

Testing Data, Factors & Metrics

Testing Data

• The model was evaluated on a test dataset containing [Specify Number] rows of Marathi instruction input-output pairs.

Factors

• Evaluation considered factors such as prompt diversity and task complexity.

Metrics

• Evaluation metrics included BLEU score, ROUGE score, and fluency scores.

Results

• The model achieved competitive performance on the evaluation tasks, demonstrating its ability to generate coherent and contextually relevant Marathi text.

Model Examination

• Model inspection involved analyzing attention mechanisms and interpretability features to understand how the model generates text.

Environmental Impact

• Carbon emissions were estimated using the Machine Learning Impact calculator. The model training process emitted [Specify Emissions] of CO2 equivalent.

Technical Specifications

Model Architecture and Objective

• Llama-2-7b is a transformer-based architecture designed for text generation and completion tasks.

Compute Infrastructure

Hardware

• Model training was performed on [Specify Hardware].

Software

• The training process used [Specify Software Stack].

Citation

BibTeX:

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APA:

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Glossary

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More Information

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Model Card Authors

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Model Card Contact

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