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Terms of Use: Terms
Authors: Google
Summary description and brief definition of inputs and outputs.
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.
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.
pipeline API
import torch
from transformers import pipeline
pipe = pipeline(
"text-generation",
model="google/gemma-2-2b-jpn-it",
model_kwargs={"torch_dtype": torch.bfloat16},
device="cuda", # replace with "mps" to run on a Mac device
)
messages = [
{"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。"},
]
outputs = pipe(messages, return_full_text=False, max_new_tokens=256)
assistant_response = outputs[0]["generated_text"].strip()
print(assistant_response)
## マシーンラーニングの詩
**1.**
データの海、深淵の広がり、
複雑なパターン、隠された知識。
機械学習、その力強さ、
未来を予測、その道を開く。
**2.**
ニューラルネットワーク、複雑な枝、
学習の旅、その過程は静か。
データから学び、進化する姿、
予測の精度、その力強さ。
**3.**
教師あり学習、正解を導く、
教師なし学習、未知の世界へ。
機械学習、その進化は止まらない、
未来の扉を開く、新たな時代へ。
**4.**
画像認識、音声認識、
複雑なタスク、その答えを見つける。
機械学習、その力強さ、
未来の技術、その可能性を語る。
It can also be used for translation, as follows:
translation_input_text = f"Translate the following poem from Japanese to English:\n\n{assistant_response}"
messages = [
{"role": "user", "content": translation_input_text},
]
outputs = pipe(messages, return_full_text=False, max_new_tokens=1024)
translated_response = outputs[0]["generated_text"].strip()
print(translated_response)
## 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.
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b-jpn-it")
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2-2b-jpn-it",
device_map="auto",
torch_dtype=torch.bfloat16,
)
messages = [
{"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。"},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True, return_dict=True).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
generated_text = tokenizer.batch_decode(outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True)[0]
print(generated_text.strip())
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.
torch.float32# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b-jpn-it")
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2-2b-jpn-it",
device_map="auto",
)
messages = [
{"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。"},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True, return_dict=True).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
generated_text = tokenizer.batch_decode(outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True)[0]
print(generated_text.strip())
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 8 trillion tokens. Here are the key components:
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:
Details about the model internals.
Gemma was trained using the latest generation of Tensor Processing Unit (TPU) hardware (TPUv5p).
Training large language models requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain:
These advantages are aligned with Google's commitments to operate sustainably.
Training was done using JAX and ML Pathways.
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."
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 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:
These models have certain limitations that users should be aware of.
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.
The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following:
Risks identified and mitigations:
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.
Base model
google/gemma-2-2b