license: gemma
library_name: transformers
pipeline_tag: text-generation
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Summary description and brief definition of inputs and outputs.
Description
Gemma is a series of best-in-class open models and draws inspiration and
technological lineage from the Gemini family of models. They are text-to-text,
decoder-only large language models with open weights. Gemma models are
well-suited for a variety of text generation tasks, including question
answering, summarization, and reasoning.
Gemma-2-JPN is a Gemma 2 2B model fine-tuned on Japanese text. It supports the
Japanese language with the same level of performance of English only queries on
Gemma 2.
Usage
Below we share some code snippets on how to get quickly started with running the model. First, install the Transformers library with:
pip install -U transformers
Then, copy the snippet from the section that is relevant for your usecase.
Running with the pipeline API
python
1import torch
2from transformers import pipeline
34pipe = pipeline(5"text-generation",6 model="google/gemma-2-2b-jpn-it",7 model_kwargs={"torch_dtype": torch.bfloat16},8 device="cuda",# replace with "mps" to run on a Mac device9)1011messages =[12{"role":"user","content":"マシーンラーニングについての詩を書いてください。"},13]1415outputs = pipe(messages, return_full_text=False, max_new_tokens=256)16assistant_response = outputs[0]["generated_text"].strip()17print(assistant_response)
1translation_input_text =f"Translate the following poem from Japanese to English:\n\n{assistant_response}"2messages =[3{"role":"user","content": translation_input_text},4]56outputs = pipe(messages, return_full_text=False, max_new_tokens=1024)7translated_response = outputs[0]["generated_text"].strip()8print(translated_response)
Example output
## A Poem About Machine Learning
**1.**
A vast ocean of data, a deep expanse,
Complex patterns, hidden knowledge.
Machine learning, its strength so vast,
Predicting the future, opening the way.
**2.**
A neural network, with branches intricate,
A journey of learning, its process serene.
Learning from data, evolving in its form,
The precision of prediction, its strength.
**3.**
Supervised learning, guiding the correct answer,
Unsupervised learning, venturing into the unknown.
Machine learning, its evolution never ends,
Opening the doors to the future, a new era.
**4.**
Image recognition, speech recognition,
Complex tasks, finding the answer.
Machine learning, its strength so vast,
The possibilities of future technology, a story to be told.
**Explanation:**
The poem uses vivid imagery and metaphors to describe the power and potential of machine learning.
* **Data as an ocean:** Represents the vast amount of information available for learning.
* **Complex patterns:** Highlights the intricate nature of data and the challenges of extracting meaningful insights.
* **Future prediction:** Emphasizes the ability of machine learning to analyze data and make predictions about the future.
* **Neural network as a tree:** Represents the interconnectedness and complexity of the learning process.
* **Learning from data:** Focuses on the core principle of machine learning, where algorithms learn from data to improve their performance.
The poem concludes by highlighting the diverse applications of machine learning, such as image and speech recognition, and emphasizes its potential to shape the future of technology.
Running the model on a GPU using different precisions
The native weights of this model were exported in bfloat16 precision.
You can also use float32 if you skip the dtype, but no precision increase will occur (model weights will just be upcasted to float32). See examples below.
Input: Text string, such as a question, a prompt, or a document to
be summarized.
Output: Generated Japanese-language text in response to the input,
such as an answer to a question, or a summary of a document.
Model Data
Data used for model training and how the data was processed.
Training Dataset
These models were trained on a dataset of text data that includes a wide
variety of sources, totaling 8 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.
Instruction data set: large-scale and high-quality Japanese and
multilingual instruction data.
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.
Data Preprocessing
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, we used automated techniques to filter out certain
personal information and other sensitive data from training sets.
Additional methods: Filtering based on content quality and
safety in line with our policies.
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.
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models.
ML Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
foundation models, including
large language models like these ones.
Together, JAX and ML Pathways are used as described in the paper about the
Gemini family of models; "the 'single controller'
programming model of Jax and Pathways allows a single Python process to
orchestrate the entire training run, dramatically simplifying the development
workflow."
Evaluation
To assess the quality of this model, we collected a diverse set of Japanese
prompts and evaluated performance using an LLM-as-a-judge approach against
GPT-3.5. The rating system is based on a 7-scale assessments, which are
MuchBetterThan, BetterThan, SlightlyBetterThan, AboutTheSame, SlightlyWorse,
WorseThan, MuchWorseThan associated with the numerical scores 1.5, 1.0, 0.5, 0,
-0.5, -1.0, -1.5 respectively. We also tracked the ability of the model to
answer in the correct language: for a Japanese prompt, the model should
typically answer in Japanese rather than defaulting to English.
Benchmark
Gemma-2-IT
Gemma-2-IT-JPN
Preference vs GPT-3.5
-0.25 ± 0.05
0.03 ± 0.04
Language correctness
86.47%
98.24%
Ethics and Safety
Ethics and safety evaluation approach and results.
Evaluation Approach
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.
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.
Usage and Limitations
These models have certain limitations that users should be aware of.
Intended Usage
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.
Limitations
Training Data
The quality and diversity of the training data significantly
influence the model's capabilities. Biases or gaps in the training data
can lead to limitations in the model's responses.
The scope of the training dataset determines the subject areas
the model can handle effectively.
Context and Task Complexity
LLMs are better at tasks that can be framed with clear prompts
and instructions. Open-ended or highly complex tasks might be challenging.
A model's performance can be influenced by the amount of context
provided (longer context generally leads to better outputs, up to a
certain point).
Language Ambiguity and Nuance
Natural language is inherently complex. LLMs might struggle to
grasp subtle nuances, sarcasm, or figurative language.
Factual Accuracy
LLMs generate responses based on information they learned from
their training datasets, but they are not knowledge bases. They may
generate incorrect or outdated factual statements.
Common Sense
LLMs rely on statistical patterns in language. They might lack
the ability to apply common sense reasoning in certain situations.
Ethical Considerations and Risks
The development of large language models (LLMs) raises several ethical
concerns. In creating an open model, we have carefully considered the
following:
Bias and Fairness
LLMs trained on large-scale, real-world text data can reflect
socio-cultural biases embedded in the training material. These models
underwent careful scrutiny, input data pre-processing described and
posterior evaluations reported in this card.
Misinformation and Misuse
LLMs can be misused to generate text that is false, misleading,
or harmful.
This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
A responsibly developed open model offers the opportunity to
share innovation by making LLM technology accessible to developers and
researchers across the AI ecosystem.
Risks identified and mitigations:
Perpetuation of biases: It's encouraged to perform continuous
monitoring (using evaluation metrics, human review) and the exploration of
de-biasing techniques during model training, fine-tuning, and other use cases.
Generation of harmful content: Mechanisms and guidelines for content
safety are essential. Developers are encouraged to exercise caution and
implement appropriate content safety safeguards based on their specific
product policies and application use cases.
Misuse for malicious purposes: Technical limitations and developer and
end-user education can help mitigate against malicious applications of
LLMs. Educational resources and reporting mechanisms for users to flag
misuse are provided. Prohibited uses of Gemma models are outlined in the
Gemma Prohibited Use Policy.
Privacy violations: Models were trained on data filtered for removal of
PII (Personally Identifiable Information). Developers are encouraged to
adhere to privacy regulations with privacy-preserving techniques.
Benefits
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.
