Llama-2 in Langchain and Hugging Face

?What is Llama2?

#llama2, the next generation of our open source large language model.??

• LLama2 is a transformer-based language model developed by researchers at Meta AI.?
• The model is trained on a large corpus of text data and is designed to generate coherent and contextually relevant text.
• ?LLama2 uses a multi-layer transformer architecture with encoder and decoder components to generate text.?
• The model is trained on a variety of tasks, including language translation, text summarization, and text generation.
• ?LLama2 has achieved state-of-the-art results on several benchmark datasets.?
• The model's architecture and training procedures are made publicly available to encourage further research and development in the field of natural language processing.?
• LLama2 has many potential applications, including chatbots, language translation.

How to download #llama2 ?

• From meta git repository using download.sh
• From Hugging Face

1. From meta git repository using download.sh

• Go to the meta website https://ai.meta.com/llama/?
• Click on download. Provide the details in the form .?
• Accept term and condition and continue.
• Once you submit you will receive an email from meta to download the model from git repository. You can download the Llama2 in your local using download.sh script from git repository.?

2. Once you get acceptance email from meta ,login to Hugging Face .?

link : https://huggingface.co/meta-llama

Select any model and submit request to grant access from Hugging Face .

Note :?This is a form to enable access to Llama 2 on Hugging Face after you have been granted access from Meta. Please visit the Meta website and accept our license terms and acceptable use policy before submitting this form. Requests will be processed in 1-2 days.

Llama2?in Langchain and Hugging Face?in Google Colab

1. Install transformers and Langchain
2. Login to Hugging face cli using Access Token
3. Create the pipeline using transformers.pipeline
4. Create the llm model using HuggingFacePipeline from Langchain
5. Create the prompt template and LLMChain instance
6. Run the model using LLMChain.run method

For more detail description please visit below link.