Llama 2 Huggingface

WEB Llama 2 is Here - Get it on Hugging Face

A Comprehensive Guide to Using WEB Llama 2 with Transformers and PEFT

WEB Llama 2: A State-of-the-Art Language Model

Meta has released WEB Llama 2, a cutting-edge family of large language models that are now available on Hugging Face. These models have already garnered significant attention and excitement within the AI community.

This blog post will provide a comprehensive guide on how to use WEB Llama 2 models with Transformers and PEFT. We will cover everything you need to know, from installation and setup to fine-tuning and deployment.

Key Features of WEB Llama 2

• State-of-the-art performance on a wide range of NLP tasks
• Open-access and free to use for both research and commercial purposes
• Trained on a massive dataset of text and code
• Available in a variety of sizes, from 7 billion to 70 billion parameters

Getting Started with WEB Llama 2

To get started with WEB Llama 2, you will need to install the Hugging Face Transformers library. You can do this using the following command:

``` pip install transformers ```

Once you have installed Transformers, you can load a WEB Llama 2 model using the following code:

``` from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("meta/web-llama-2-large-finetuned-sst-2") model = AutoModelForSequenceClassification.from_pretrained("meta/web-llama-2-large-finetuned-sst-2") ```

You can now use the tokenizer to tokenize your text and the model to make predictions.

Fine-tuning WEB Llama 2

You can fine-tune WEB Llama 2 models on your own dataset using the PEFT library. PEFT is a powerful tool for fine-tuning language models, and it makes it easy to get started with fine-tuning.

To fine-tune a WEB Llama 2 model using PEFT, you will need to first install PEFT using the following command:

``` pip install peft ```

Once you have installed PEFT, you can fine-tune a WEB Llama 2 model using the following code:

``` from peft import PEFT peft = PEFT(model, tokenizer) peft.train(train_dataset, validation_dataset, epochs=5) ```

Once you have fine-tuned your model, you can use it to make predictions on new data.

Conclusion

WEB Llama 2 is a powerful new language model that can be used for a wide range of NLP tasks. In this blog post, we have provided a comprehensive guide on how to use WEB Llama 2 models with Transformers and PEFT. We hope this guide has been helpful, and we encourage you to experiment with WEB Llama 2 to see what it can do.