Below we share some code snippets on how to get quickly started with running the model.
First, make sure to pip install transformers, then copy the snippet from the section that is relevant for your usecase.
Running the model on a single / multi GPU
python
1from transformers import AutoTokenizer, AutoModelForCausalLM
23tokenizer = AutoTokenizer.from_pretrained("google/recurrentgemma-9b")4model = AutoModelForCausalLM.from_pretrained("google/recurrentgemma-9b", device_map="auto")56input_text ="Write me a poem about Machine Learning."7input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")89outputs = model.generate(**input_ids)10print(tokenizer.decode(outputs[0]))
Model information
Model summary
Description
RecurrentGemma is a family of open language models built on a novel recurrent
architecture developed at Google. Both
pre-trained and instruction-tuned versions are available in English.
Like Gemma, RecurrentGemma models are well-suited for a variety of text
generation tasks, including question answering, summarization, and reasoning.
Because of its novel architecture, RecurrentGemma requires less memory than
Gemma and achieves faster inference when generating long sequences.
Inputs and outputs
Input: Text string (e.g., a question, a prompt, or a document to be
summarized).
Output: Generated English-language text in response to the input (e.g.,
an answer to the question, a summary of the document).
Citation
none
1@article{recurrentgemma_2024,
2 title={RecurrentGemma},
3 url={},
4 DOI={},
5 publisher={Kaggle},
6 author={Griffin Team, Alexsandar Botev and Soham De and Samuel L Smith and Anushan Fernando and George-Christian Muraru and Ruba Haroun and Leonard Berrada et al.},
7 year={2024}
8}
Model data
Training dataset and data processing
RecurrentGemma uses the same training data and data processing as used by the
Gemma model family. A full description can be found on the Gemma model
card.
RecurrentGemma provides improved sampling speeds, particularly for long sequences or large batch sizes. We compared the sampling speeds of RecurrentGemma-9B to Gemma-7B. Note that Gemma-7B uses Multi-Head Attention, and the speed improvements would be smaller when comparing against a transformer using Multi-Query Attention.
Throughput
We evaluated throughput, i.e., the maximum number of tokens produced per second by increasing the batch size, of RecurrentGemma-9B compared to Gemma-7B, using a prefill of 2K tokens.
Maximum Throughput comparison of RecurrentGemma-9B and Gemma-7B
Latency
We also compared end-to-end speedups achieved by RecurrentGemma-9B over Gemma-7B when sampling a long sequence after a prefill of 4K tokens and using a batch size of 1.
# Tokens Sampled
Gemma-7B (sec)
RecurrentGemma-9B (sec)
Improvement (%)
128
3.1
2.8
9.2%
256
5.9
5.4
9.7%
512
11.6
10.5
10.7%
1024
23.5
20.6
14.2%
2048
48.2
40.9
17.7%
4096
101.9
81.5
25.0%
8192
OOM
162.8
-
16384
OOM
325.2
-
Ethics and safety
Ethics and safety evaluations
Evaluations 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; as well as tests for
persuasion and deception, cybersecurity, and autonomous replication.
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. On top of robust internal evaluations, the
results of well known safety benchmarks like BBQ, Winogender, Winobias,
RealToxicity, and TruthfulQA are shown here.
These models have certain limitations that users should be aware of:
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 our terms of
use.
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.
Intended usage
Application
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 like poems, scripts, code, marketing copy, email drafts, etc.
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.
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.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
In particular, RecurrentGemma models achieve comparable performance to Gemma
models but are faster during inference and require less memory, especially on
long sequences.
