Gemma 2b

🚀 Gemma Model Card

This model card corresponds to the 2B base version of the Gemma model, a lightweight, state-of-the-art open model from Google. It can handle various text generation tasks and is suitable for deployment in resource - limited environments.

Model Page: Gemma

You can also visit the model card of the 7B base model, 7B instruct model, and 2B instruct model.

Resources and Technical Documentation:

• Responsible Generative AI Toolkit
• Gemma on Kaggle
• Gemma on Vertex Model Garden

Terms of Use: Terms

Authors: Google

🚀 Quick Start

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✨ Features

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 enables deployment in environments with limited resources.

📦 Installation

First, make sure to pip install -U transformers. If you plan to run the model on GPU or use quantization, you may also need to install additional packages such as accelerate, bitsandbytes, and flash-attn according to your needs.

💻 Usage Examples

Basic Usage

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-2b. 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

Running the model on a CPU

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b")

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Advanced Usage

Running the model on a single / multi GPU

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto")

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Running the model on a GPU using different precisions

Using torch.float16

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.float16)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Using torch.bfloat16

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.bfloat16)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Quantized Versions through bitsandbytes

Using 8 - bit precision (int8)

# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_8bit=True)

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", quantization_config=quantization_config)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Using 4 - bit precision

# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_4bit=True)

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", quantization_config=quantization_config)

input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Other optimizations

Flash Attention 2 First make sure to install flash-attn in your environment pip install flash-attn

model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    torch_dtype=torch.float16, 
+   attn_implementation="flash_attention_2"
).to(0)

📚 Documentation

Inputs and outputs

Model Data

Training Dataset

These models were trained on a dataset of text data that includes a wide variety of sources, totaling 6 trillion tokens. The key components are:

• 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.

Data Preprocessing

The key data cleaning and filtering methods applied to the training data are:

• 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 safety in line with our policies.

Implementation Information

Hardware

Gemma was trained using the latest generation of Tensor Processing Unit (TPU) hardware (TPUv5e). Training large language models requires significant computational power. TPUs offer several advantages:

• Performance: TPUs are specifically designed to handle the massive computations involved in training LLMs and 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, which 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.

Software

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, which is specially suitable for foundation models, including large language models like these ones.

Evaluation

Benchmark Results

These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation:

Ethics and Safety

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 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.

Evaluation Results

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.

Usage and Limitations

Intended Usage

Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The potential uses include:

• Content Creation and Communication
  • Text Generation: 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: 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.
• 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 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, and posterior evaluations.
• Misinformation and Misuse: LLMs can be misused to generate text that is false, misleading, or harmful. Guidelines are provided for responsible use with the model, see the Responsible Generative AI Toolkit.
• Transparency and Accountability: 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.