JT Blog

  T he LLAMA 2 is a powerful language model that has demonstrated remarkable capabilities in understanding and generating human-like text. In this article, we will guide you through the process of deploying your LLAMA-2–13b-chat Language Model (LLM) as an API using Python’s FastAPI framework. This will allow you to interact with your LLAMA 2 model over HTTP requests and get a streaming response, enabling a wide range of applications such as chatbots, content generation, and more. Prerequisites Before we dive into the deployment process, ensure that you have the following components ready: LLAMA 2–13b-chat LLM Model: You should have the LLAMA2 Language Model pre-trained and saved in a suitable format for deployment. wget https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/resolve/main/llama-2-13b-chat.ggmlv3.q4_1.bin Python Environment: Set up a Python environment with the required packages. You can use virtual environments to manage dependencies cleanly pip install llama-cpp-pyth...

  Introduction Natural Language Processing (NLP) has seen tremendous advancements in recent years, thanks to powerful large language models like Falcon 7B. Falcon 7B, is a state-of-the-art LLM based on the Transformer architecture( https://huggingface.co/blog/falcon ). While Falcon 7B offers impressive out-of-the-box performance, instruction fine-tuning allows you to build your own LLM with context and knowledge about your data. In this article, we will explore how to fine-tune Falcon7B on custom Frequently Asked Questions (FAQ) data, allowing you to create a powerful and accurate FAQ-based chat bot tailored to your specific needs. Understanding Fine-Tuning Fine-tuning is a transfer learning technique that involves taking a pre-trained model, such as Falcon 7B, and adapting it to perform a specific task. The pre-trained model is already equipped with knowledge about language, grammar, and context from its broad training on a large corpus of text. Fine-tuning allows us to leverage t...