Update README.md

Esse trabalho consiste em desenvolver um modelo de linguagem grande (LLM) com 2.3 milhões de parâmetros usando a arquitetura LLaMA 1, implementado em um notebook do Google Collaboratory. Os alunos irão construir um LLM desde o pré-processamento dos dados até a avaliação do desempenho do modelo. O projeto envolverá as etapas principais de preparação de dados, configuração do modelo, treinamento e avaliação.
Etapas do Projeto:
1 Preparação do Ambiente:
◦ Configuração do Google Colaboratory.
◦ Instalação das bibliotecas necessárias: torch, transformers, datasets, entre outras.
2 Coleta e Pré-processamento dos Dados:
◦ Utilização do conjunto de dados TinyShakespeare.
◦ Tokenização e limpeza dos dados.
3 Configuração da Arquitetura do Modelo:
◦ Implementação dos componentes principais da arquitetura LLaMA 1 [4]:
▪ RMSNorm
▪ SwiGLU
▪ Rotary Embeddings
◦ Definição da rede neural com PyTorch.
4 Treinamento do Modelo:
◦ Ajuste dos hiperparâmetros: taxa de aprendizado, batch size, número de épocas, etc.
◦ Utilização de técnicas de regularização e otimização.
5 Avaliação do Modelo:
◦ Métricas de avaliação: perplexidade, acurácia.
◦ Análise dos resultados e ajuste dos parâmetros conforme necessário.
◦ Pode-se usar como referência o modelo GPT-2 na coleção TinyShakespeare.
6 Documentação e Apresentação:
◦ Registro de todas as etapas e decisões no notebook.
◦ Criação de uma apresentação resumindo o processo e os resultados obtidos.
Entrega:
• Notebook do Google Colaboratory contendo todo o código e documentação do processo.
• Link de compartilhamento do modelo gerado no HuggingFace
• Relatório final com análise dos resultados e possíveis melhorias.
Recursos Adicionais:
• Documentação PyTorch
• Tutorial Transformers
• Conjunto de Dados TinyShakespeare
Referências Bibliográficas:
[1] Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805. [2] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is All You Need. Advances in neural information processing systems, 30. [3] Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving Language Understanding by Generative Pre-Training. OpenAI preprint. [4] Touvron, H., Bojanowski, P., Caron, M., Cord, M., El-Nouby, A., Grave, E., ... & Jégou, H. (2023). LLaMA: Open and Efficient Foundation Language Models. arXiv preprint arXiv:2302.13971. [5] Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., ... & Rush, A. M. (2020). Transformers: State-of-the-Art Natural Language Processing. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 38-45.

README.md

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+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## 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:** [More Information Needed]
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+### Model Sources [optional]
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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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+### Downstream Use [optional]
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+### Out-of-Scope Use
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+## Bias, Risks, and Limitations
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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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+## 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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+#### 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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+## Evaluation
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+### Testing Data, Factors & Metrics
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+#### Factors
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+#### Metrics
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+### Results
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+#### Summary
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+## Model Examination [optional]
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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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+## Technical Specifications [optional]
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+### Model Architecture and Objective
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+### Compute Infrastructure
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