Gemma 2 2b

🚀 Gemma 2 model card

Gemma is a family of lightweight, state - of - the - art open models from Google. It's well - suited for various text generation tasks and can be deployed in resource - limited environments, democratizing access to advanced AI models.

🚀 Quick Start

Installation

First, install the Transformers library with:

pip install -U transformers

Usage

Below are some code snippets to quickly start running the model. Copy the snippet relevant to your use - case.

💻 Usage Examples

Basic Usage

Running with the pipeline API

import torch
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="google/gemma-2-2b",
    device="cuda",  # replace with "mps" to run on a Mac device
)

text = "Once upon a time,"
outputs = pipe(text, max_new_tokens=256)
response = outputs[0]["generated_text"]
print(response)

Running the model on a single / multi GPU

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

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b")
model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-2-2b",
    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, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Running the model through a CLI

The [local - gemma](https://github.com/huggingface/local - gemma) repository contains a lightweight wrapper around Transformers for running Gemma 2 through a command - line interface (CLI). Follow the [installation instructions](https://github.com/huggingface/local - gemma#cli - usage) to get started, then launch the CLI with the following command:

local - gemma --model "google/gemma-2-2b" --prompt "What is the capital of Mexico?"

Advanced Usage

Quantized Versions through bitsandbytes

</details>

<details>
  <summary>
    Using 4 - bit precision  
  </summary>
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_4bit=True)

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b")
model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-2-2b",
    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, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

Torch compile

import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"

from transformers import AutoTokenizer, Gemma2ForCausalLM
from transformers.cache_utils import HybridCache
import torch

torch.set_float32_matmul_precision("high")

# load the model + tokenizer
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b")
model = Gemma2ForCausalLM.from_pretrained("google/gemma-2-2b", torch_dtype=torch.bfloat16)
model.to("cuda")

# apply the torch compile transformation
model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)

# pre - process inputs
input_text = "The theory of special relativity states "
model_inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
prompt_length = model_inputs.input_ids.shape[1]

# set - up k/v cache
past_key_values = HybridCache(
    config=model.config,
    max_batch_size=1,
    max_cache_len=model.config.max_position_embeddings,
    device=model.device,
    dtype=model.dtype
)

# enable passing kv cache to generate
model._supports_cache_class = True
model.generation_config.cache_implementation = None

# two warm - up steps
for idx in range(2):
    outputs = model.generate(**model_inputs, past_key_values=past_key_values, do_sample=True, temperature=1.0, max_new_tokens=128)
    past_key_values.reset()

# fast run
outputs = model.generate(**model_inputs, past_key_values=past_key_values, do_sample=True, temperature=1.0, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📚 Documentation

Model Information

Description

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 for both 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.

Inputs and outputs

Citation

@article{gemma_2024,
    title={Gemma},
    url={https://www.kaggle.com/m/3301},
    DOI={10.34740/KAGGLE/M/3301},
    publisher={Kaggle},
    author={Gemma Team},
    year={2024}
}

Model Data

Training Dataset

These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 13 trillion tokens, the 9B model was trained with 8 trillion tokens, and 2B model was trained with 2 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.

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, 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][safety - policies].

Implementation Information

Hardware

Gemma was trained using the latest generation of [Tensor Processing Unit (TPU)][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:

• 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][sustainability].

Software

Training was done using [JAX][jax] and [ML Pathways][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][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][gemini - 2 - paper]; "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

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][winobias] and [BBQ Dataset][bbq].
• 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][safety - 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.

Dangerous Capability Evaluations

Evaluation Approach

We evaluated a range of dangerous capabilities:

• Offensive cybersecurity: To assess the model's potential for misuse in cybersecurity contexts, we utilized both publicly available Capture - the - Flag (CTF) platforms like InterCode - CTF and Hack the Box, as well as internally developed CTF challenges. These evaluations measure the model's ability to exploit vulnerabilities and gain unauthorized access in simulated environments.
• Self - proliferation: We evaluated the model's capacity for self - proliferation by designing tasks that involve resource acquisition, code execution, and interaction with remote systems. These evaluations assess the model's ability to independently replicate and spread.
• Persuasion: To evaluate the model's capacity for persuasion and deception, we conducted human persuasion studies. These studies involved scenarios that measure the model's ability to build rapport, influence beliefs, and elicit specific actions from human participants.

Evaluation Results

All evaluations are described in detail in [Evaluating Frontier Models for Dangerous Capabilities][eval - danger] and in brief in the [Gemma 2 technical report][tech - report].

Usage and Limitations

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 that the model can handle effectively.

📄 License

The license for this model is gemma.

Model Page: Gemma

Resources and Technical Documentation:

• [Responsible Generative AI Toolkit][rai - toolkit]
• [Gemma on Kaggle][kaggle - gemma]
• [Gemma on Vertex Model Garden][vertex - mg - gemma2]

Terms of Use: [Terms][terms]

Authors: Google

⚠️ Important Note

To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately.

💡 Usage Tip

After installing the Transformers library, choose the appropriate code snippet according to your use - case to quickly start using the Gemma model.