Google has recently released Gemma and made its weight information public.
Gemma is an open-weight model. Open weight means that the model's weights (i.e., the parameters after model training) are open to the public, allowing users to directly use these weights to run the model for inference or fine-tuning. Compared with other open-source models, open source provides broader openness. Not only are the model weights open, but the model's code, architecture, and training process are also fully open source, enabling users to freely access and modify them.
Gemma can be deployed and trained on Google Cloud through Vertex AI or Google Kubernetes Engine (GKE), utilizing text generation inference and Transformers for implementation.
Google Colab can also be used, and here is the official code provided:
https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/lora_tuning.ipynb
Compared with other open models:
Gemma offers two scales: a 7B parameter version and a 2B parameter version.
Gemma 7B is a highly capable model designed for efficient deployment and development, suitable for consumer-grade GPUs and TPUs, and performs on par with the best models at the 7B parameter level, including Mistral 7B. Gemma 2B, while being an interesting model within its size range and suitable for CPU and edge-device applications, does not score as high on leaderboards as top-tier models like Phi 2.
If you'd like to try it directly, you can find it here: https://huggingface.co/chat?model=google/gemma-7b-it
Below are the answers about Gemma's performance compiled using the Gemma model:
According to Google's official announcement, Gemma is a new large language model capable of completing tasks such as text summarization, code translation, and question answering. Below is an overview of its performance:
Highlights:
High accuracy: Achieves advanced performance in various tasks, including text summarization, code translation, and question answering. Performs better than previous models like PaLM and Megatron-Turing NLG on T5 and QNLG evaluations. Multimodal capabilities: It can handle text, code, and images, making it applicable to a variety of tasks. It can translate code между Python and other programming languages. No specific training required: It can smoothly perform tasks without requiring extensive training. Can transfer knowledge between tasks more effectively.
Challenges:
Resource usage: Requires substantial computing resources, which may limit its accessibility. Google says it is developing technology to improve its efficiency. Bias: May inherit bias from the training data. Google is committed to reducing bias through various methods, such as using fairness-aware training techniques.
Overall:
Gemma is a powerful language model with impressive performance that can drive the advancement of many applications. However, its resource consumption and potential bias are challenges that need to be addressed.