Amazon SageMaker JumpStart is a hub for machine learning (ML) that offers algorithms, models, and ML solutions. It provides ML practitioners with a range of best performing and publicly available foundation models (FMs) such as BLOOM, Llama 2, Falcon-40B, Stable Diffusion, OpenLLaMA, Flan-T5/UL2, and FMs from Cohere and LightOn. This post and accompanying notebook demonstrate how to deploy the BloomZ 176B foundation model using the SageMaker Python simplified SDK in Amazon SageMaker JumpStart as an endpoint for various natural language processing (NLP) tasks. The foundation models can also be accessed through Amazon SageMaker Studio.
The BloomZ 176B model is one of the largest publicly available models and is capable of performing various in-context few-shot learning and zero-shot learning NLP tasks. It utilizes instruction tuning, a technique that involves fine-tuning a language model on a collection of NLP tasks using instructions. This allows the model to generate responses to tasks it hasn’t been specifically trained for. Zero-shot learning in NLP enables the model to generate responses by providing an input text and a prompt that describes the expected output in natural language. This technique is applicable to tasks such as multilingual text and sentiment classification, question answering, code generation, summarization, common sense reasoning, natural language inference, and more.
Few-shot learning involves training a model to perform new tasks with only a few examples, which is useful when limited labeled data is available for training. It can be applied to tasks like text summarization, code generation, name entity recognition, question answering, grammar and spelling correction, product description and generalization, sentence and sentiment classification, chatbot and conversational AI, tweet generation, machine translation, and intent classification.
The BloomZ 176B model, part of the BigScience Large Open-science Open-access Multilingual (BLOOM) language model, is a transformer-based large language model (LLM) trained on vast amounts of text data using industrial-scale computational resources. It has 176 billion parameters and can generate text in 46 natural languages and 13 programming languages. Researchers can download and study BLOOM to explore its performance and behavior.
In this post, the state-of-the-art instruction-tuned BloomZ 176B model from Hugging Face is used for text generation without the need for fine-tuning. The model has been trained with a large amount of data, making it applicable to many general-purpose tasks. The code for the demo is available in the accompanying notebook.
Instruction tuning is a technique used to fine-tune LLMs on a collection of NLP tasks using textual instructions. This allows the model to generalize to new tasks without the need for prompt-specific fine-tuning. It improves the accuracy and effectiveness of models, especially in situations where specific task datasets are not available.
Prompt engineering plays a crucial role in zero-shot and few-shot learning tasks on BLOOM models. Creating high-quality prompts that provide necessary information to guide the model towards desired responses is important. Well-designed prompts can make the model more creative, generalized, and adaptable to new tasks. Prompt engineering involves careful consideration of the task and a deep understanding of the model’s strengths and limitations.
The table in the post demonstrates the use of the BloomZ 176B model for various zero-shot and few-shot NLP tasks, providing prompts and showcasing the model’s responses.
Overall, Amazon SageMaker JumpStart, along with the BloomZ 176B model, provides ML practitioners with a powerful tool for NLP tasks, enabling them to leverage pre-trained models and apply them to various applications without the need for extensive training or fine-tuning.