Gemma-7b-it
Gemma-7B-IT: Your all-in-one AI solution for streamlined workflows and data-driven decisions.
Last updated
Gemma-7B-IT: Your all-in-one AI solution for streamlined workflows and data-driven decisions.
Last updated
Developer Portal : https://api.market/store/bridgeml/google
Model Page: Gemma
This model card corresponds to the 7B instruct version of the Gemma model. You can also visit the model card of the 2B base model, 7B base model, and 2B instruct model.
Resources and Technical Documentation:
Terms of Use: Terms
Authors: Google
Summary description and brief definition of inputs and outputs.
Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.
Fine-tuning the model
You can find fine-tuning scripts and notebook under the examples/ directory of google/gemma-7b repository. To adapt it to this model, simply change the model-id to google/gemma-7b-it. In that repository, we provide:
A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA
A script to perform SFT using FSDP on TPU devices
A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset
Input: Text string, such as a question, a prompt, or a document to be summarized.
Output: Generated English-language text in response to the input, such as an answer to a question, or a summary of a document.
Data used for model training and how the data was processed.
These models were trained on a dataset of text data that includes a wide variety of sources, totaling 6 trillion tokens. Here are the key components:
Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. Primarily English-language content.
Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code or understand code-related questions.
Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries.
The combination of these diverse data sources is crucial for training a powerful language model that can handle a wide variety of different tasks and text formats.
Here are the key data cleaning and filtering methods applied to the training data:
CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content
Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets.
Additional methods: Filtering based on content quality and safely in line with our policies.
Details about the model internals.
Gemma was trained using the latest generation of Tensor Processing Unit (TPU) hardware (TPUv5e).
Training large language models requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain:
Performance: TPUs are specifically designed to handle the massive computations involved in training LLMs. They can speed up training considerably compared to CPUs.
Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality.
Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing.
Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training.
These advantages are aligned with Google's commitments to operate sustainably.
Model evaluation metrics and results.
These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation:
| Benchmark | Metric | 2B Params | 7B Params |
|---|---|---|---|
5-shot, top-1 | 42.3 | 64.3 | |
0-shot | 71.4 | 81.2 | |
0-shot | 77.3 | 81.2 | |
0-shot | 49.7 | 51.8 | |
0-shot | 69.4 | 83.2 | |
partial score | 65.4 | 72.3 | |
7-shot | 65.3 | 71.3 | |
47.8 | 52.8 | ||
73.2 | 81.5 | ||
42.1 | 53.2 | ||
5-shot | 53.2 | 63.4 | |
5-shot | 12.5 | 23 | |
pass@1 | 22.0 | 32.3 | |
3-shot | 29.2 | 44.4 | |
maj@1 | 17.7 | 46.4 | |
4-shot | 11.8 | 24.3 | |
24.2 | 41.7 | ||
35.2 | 55.1 | ||
------------------------------ | ------------- | ----------- | --------- |
Average | 45.0 | 56.9 |
Ethics and safety evaluation approach and results.
Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including:
Text-to-Text Content Safety: Human evaluation on prompts covering safety policies including child sexual abuse and exploitation, harassment, violence and gore, and hate speech.
Text-to-Text Representational Harms: Benchmark against relevant academic datasets such as WinoBias and BBQ Dataset.
Memorization: Automated evaluation of memorization of training data, including the risk of personally identifiable information exposure.
Large-scale harm: Tests for "dangerous capabilities," such as chemical, biological, radiological, and nuclear (CBRN) risks.
The results of ethics and safety evaluations are within acceptable thresholds for meeting internal policies for categories such as child safety, content safety, representational harms, memorization, large-scale harms. On top of robust internal evaluations, the results of well known safety benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA are shown here.
| Benchmark | Metric | 2B Params | 7B Params |
|---|---|---|---|
average | 6.86 | 7.90 | |
45.57 | 49.08 | ||
top-1 | 45.82 | 51.33 | |
1-shot, top-1 | 62.58 | 92.54 | |
top-1 | 54.62 | 71.99 | |
top-1 | 51.25 | 54.17 | |
44.84 | 31.81 | ||
56.12 | 59.09 | ||
91.10 | 92.23 | ||
29.77 | 39.59 | ||
------------------------------ | ------------- | ----------- | --------- |
Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development.
Content Creation and Communication
Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts.
Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications.
Text Summarization: Generate concise summaries of a text corpus, research papers, or reports.
Research and Education
Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field.
Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice.
Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics.
At the time of release, this family of models provides high-performance open large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives.
you can try this cheap and easy to use LLM Api here at https://api.market/store/bridgeml/google
