elsebaiymohamed / falcon7b_langchain

Falcon7b_Langchain

Text Generation with Falcon-7b and Langchain

This repository contains code for setting up a text generation pipeline using the Transformers library from Hugging Face and the Langchain library. The pipeline allows you to generate text using pre-trained language models and customize the generation process based on your requirements.

Installation

To run the code in this repository, you need to install the required libraries. Make sure you have the necessary dependencies installed in your Python environment.

Usage

1. Load the Falcon7b model:

   • The code loads the Falcon7b model from the Hugging Face Model Hub using the tiiuae/falcon-7b-instruct identifier. It initializes the tokenizer and determines the device (CPU or GPU) for model computations.

2. Set up the text generation pipeline:

   • The code uses the Transformers library to set up a text generation pipeline with various parameters such as the model, tokenizer, data type, maximum length, sampling options, etc.

3. Import the HuggingFacePipeline class:

   • The code imports the HuggingFacePipeline class from the Langchain library, which allows you to create a local pipeline from a Hugging Face model.

4. Instantiate the HuggingFacePipeline object:

   • The code creates an instance of the HuggingFacePipeline class using the pipeline set up in step 2. This object can be used to interact with the local pipeline for text generation.

5. Generate text using the pipeline:

   • You can generate text by passing a question or instruction to the pipeline object. The generated text will be returned as the result.

6. Conversation handling:

   • The code provides an example of conversation handling using the Langchain library. It sets up a conversation chain with a conversation memory, allowing you to initiate and continue conversations with the text generation model.

Please refer to the code comments and documentation in the provided Jupyter