The release of ChatGPT is a matter of celebration not just amongst tech enthusiasts but also among regular folks. Large Language models are unique and complex in their own ways and offer exceptional performance in a variety of tasks. Some LLMs have billions of parameters, which is far more than most other NLP models. This allows them to learn complex patterns in language and perform tasks that would be impossible for smaller models.
LLaMa-2 is a family of large language models (LLMs) developed by Meta AI. It is the successor to the original LLaMa model, which was released in 2022. LLaMa-2 is a family of models ranging in scale from 7B to 70B parameters, which makes it one of the largest LLMs publicly available. LLaMa-2 is unique in several ways. It is trained on a massive dataset of text and code, which allows it to learn complex patterns in language and perform a wide range of tasks, including text translation, text summarization, question answering, code generation, and creative writing.
Llama-2 outperformed state-of-the-art open-source models such as Falcon and MPT in various benchmarks, including MMLU, TriviaQA, Natural Question, HumanEval, and others.
LLaMa-2 is designed to be more efficient and accessible than other LLMs. It is trained using a new technique called ‘lazy training,’ which allows it to be trained on much larger datasets than previous LLMs. LLaMa-2 is optimized to run on a variety of hardware platforms, including GPUs, CPUs, and even mobile devices. It is open source, which means that anyone can use it for research or commercial purposes. This makes it a valuable resource for researchers and developers who are working on new applications for LLMs.
Key Benefits of Using LLaMa-2
- High Accuracy and Fluency: LLaMa-2 is trained on a massive dataset of text and code, which allows it to generate text that is both accurate and fluent.
- Efficiency and Accessibility: LLaMa-2 is designed to be more efficient and accessible than other LLMs. It can be trained on larger datasets and run on a variety of hardware platforms.
- Open Source: LLaMa-2 is open source, which means that anyone can use it for research or commercial purposes.
LLaMa-2 is a powerful new tool for natural language processing. It has the potential to be used in a wide range of applications, such as machine translation, text summarization, question answering, code generation, and creative writing.
Methods of Using LLaMa-2
There are two methods of using LLaMa-2:
- Download the model with the correct instructions and link provided in the email (the hard way, and works only if you have a decent GPU), or
- Use Hugging Face and a cloud GPU notebook. (E2E Networks gives you a robust option.)
In this article, we will go through the second method, that is, using the huggingface library in E2E TIR Notebook.
To use LLaMA-2 in a TIR Notebook, we first need to install the necessary packages:
Then we need to log in to Hugging Face:
Get the token number using your id; it is free to use, and now we can download the LLaMA-2 model.
Text summarization is a crucial task in natural language processing (NLP) that extracts the most important information from a text while retaining its core meaning. In recent years, various techniques and models have been developed to automate this process, making it easier to digest large volumes of text data.
This article proposes a solution for text summarization using LLaMA-2 locally, without using cloud services or exposing your documents to third-party applications or OpenAI's models. We will explore the capabilities of LLaMA-2 and demonstrate how it can streamline your multiple document summarization needs.
This HuggingFacePipeline will now allow us to use the LLaMA-2 model in our notebook.
Here is a brief explanation of each step:
- Installing the necessary packages: The pip install command installs the Python packages that we need to use LLaMA-2. These packages include transformers, einops, accelerate, langchain, and bitsandbytes.
- Logging in to Hugging Face: The huggingface-cli login command logs us in to Hugging Face. This is necessary because we need to download the LLaMA-2 model from the Hugging Face Hub.
- Downloading the LLaMA-2 model: The code in this section downloads the LLaMA-2 model from the Hugging Face Hub. We specify the model name (model = "meta-llama/Llama-2-7b-chat-hf") and the tokenizer (tokenizer = AutoTokenizer.from_pretrained(model)).
- Creating a HuggingFacePipeline out of the model: The code in this section creates a HuggingFacePipeline out of the LLaMA-2 model. This Pipeline will allow us to use the model in our notebook.
Once we have created the HuggingFacePipeline, we can start using the LLaMA-2 model.
You can pass your text inside the function and generate a summary of your own. For example, see below.
"""Arsenal take on Stoke City in Barclays Under 21 Premier League clash .
Jack Wilshere and club captain Mikel Arteta have been out since November .
Abou Diaby has been ravaged by injuries during nine-year spell at club .
Arteta, Wilshere and Diaby are all close to first-team returns .
Young winger Serge Gnabry also in the side on return from injury .
READ: Arsenal's Alex Oxlade-Chamberlain, Calum Chambers, Jack Wilshere and Danny Welbeck keep their agents close."""
Arsenal take on Stoke City in Barclays Under 21 Premier League clash.
• Jack Wilshere and club captain Mikel Arteta have been out since November.
• Abou Diaby has been ravaged by injuries during his nine-year spell at the club.
• Arteta, Wilshere, and Diaby are all close to first-team returns.
• Young winger Serge Gnabry is also in the side on his return from injury.
• Alex Oxlade-Chamberlain, Calum Chambers, Jack Wilshere, and Danny Welbeck keep their agents close.
Similarly, we can change the prompt as follows to generate an output for text translation.
template = """ Translate the following sentence from English to French:
prompt = PromptTemplate(template=template, input_variables=["text"])
llm_chain = LLMChain(prompt=prompt, llm=llm)
For example, see below.
The quick brown fox jumps over the lazy dog.
``` Le renard brun rapide saute sur le chien endormi.```
* "The quick brown fox" becomes "Le renard brun rapide" in French, where "renard" means "fox" and "brun" means "brown".
* "jumps" becomes "saute" in French, which means "jumps" in English.
* "over" becomes "sur" in French, which means "over" in English.
* "the lazy dog" becomes "le chien endormi" in French, where "chien" means "dog" and "endormi" means "lazy".
So the translated sentence is "Le renard brun rapide saute sur le chien endormi".
In this article, we have explored the capabilities of LLaMA-2 and demonstrated how it can be used for text summarization locally. We have also discussed the benefits of using LLaMA-2 for this task, such as its accuracy, speed, and ease of use.
LLaMA-2 is a powerful tool that can help individuals and businesses to streamline their multiple document summarization needs. It is ideal for tasks such as summarizing news articles, research papers, and other types of documents.
LLaMA-2 has the potential to revolutionize the way we interact with text data. By making it possible to summarize text quickly and accurately, LLaMA-2 can help us to make better decisions and be more productive.