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+ ---
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+ license: gemma
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+ library_name: transformers
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+ extra_gated_heading: Access RecurrentGemma on Hugging Face
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+ extra_gated_prompt: To access RecurrentGemma on Hugging Face, you’re required to review
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+ and agree to Google’s usage license. To do this, please ensure you’re logged-in
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+ to Hugging Face and click below. Requests are processed immediately.
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+ extra_gated_button_content: Acknowledge license
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+ ---
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+
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+ # RecurrentGemma Model Card
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+
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+ **Model Page**: [RecurrentGemma]( https://ai.google.dev/gemma/docs/recurrentgemma/model_card)
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+
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+ This model card corresponds to the 9B base version of the RecurrentGemma model. You can also visit the model card of the [9B instruct model](https://huggingface.co/google/recurrentgemma-9b-it).
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+
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+ **Resources and technical documentation:**
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+
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+ * [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
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+ * [RecurrentGemma on Kaggle](https://www.kaggle.com/models/google/recurrentgemma)
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+
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+ **Terms of Use:** [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
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+
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+ **Authors:** Google
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+
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+ ## Usage
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+
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+ Below we share some code snippets on how to get quickly started with running the model.
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+
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+ First, make sure to `pip install transformers`, then copy the snippet from the section that is relevant for your usecase.
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+
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+ ### Running the model on a single / multi GPU
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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+ tokenizer = AutoTokenizer.from_pretrained("google/recurrentgemma-9b")
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+ model = AutoModelForCausalLM.from_pretrained("google/recurrentgemma-9b", device_map="auto")
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+
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+ input_text = "Write me a poem about Machine Learning."
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+ input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
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+
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+ outputs = model.generate(**input_ids)
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+ print(tokenizer.decode(outputs[0]))
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+ ```
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+
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+ ## Model information
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+
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+ ### Model summary
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+
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+ #### Description
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+
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+ RecurrentGemma is a family of open language models built on a [novel recurrent
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+ architecture](https://arxiv.org/abs/2402.19427) developed at Google. Both
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+ pre-trained and instruction-tuned versions are available in English.
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+
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+ Like Gemma, RecurrentGemma models are well-suited for a variety of text
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+ generation tasks, including question answering, summarization, and reasoning.
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+ Because of its novel architecture, RecurrentGemma requires less memory than
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+ Gemma and achieves faster inference when generating long sequences.
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+
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+ #### Inputs and outputs
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+
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+ * **Input:** Text string (e.g., a question, a prompt, or a document to be
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+ summarized).
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+ * **Output:** Generated English-language text in response to the input (e.g.,
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+ an answer to the question, a summary of the document).
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+
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+ #### Citation
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+
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+ ```none
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+ @article{recurrentgemma_2024,
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+ title={RecurrentGemma},
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+ url={},
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+ DOI={},
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+ publisher={Kaggle},
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+ 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.},
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+ year={2024}
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+ }
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+ ```
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+
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+ ### Model data
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+
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+ #### Training dataset and data processing
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+
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+ RecurrentGemma uses the same training data and data processing as used by the
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+ Gemma model family. A full description can be found on the [Gemma model
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+ card](https://ai.google.dev/gemma/docs/model_card#model_data).
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+
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+ ## Implementation information
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+
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+ ### Hardware and frameworks used during training
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+
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+ Like
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+ [Gemma](https://ai.google.dev/gemma/docs/model_card#implementation_information),
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+ RecurrentGemma was trained on
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+ [TPUv5e](https://cloud.google.com/tpu/docs/intro-to-tpu?_gl=1*18wi411*_ga*MzE3NDU5OTY1LjE2MzQwNDA4NDY.*_ga_WH2QY8WWF5*MTcxMTA0MjUxMy4xNy4wLjE3MTEwNDI1MTkuMC4wLjA.&_ga=2.239449409.-317459965.1634040846),
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+ using [JAX](https://github.com/google/jax) and [ML
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+ Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/).
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+
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+ ## Evaluation information
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+
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+ ### Benchmark results
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+
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+ #### Evaluation approach
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+
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+ These models were evaluated against a large collection of different datasets and
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+ metrics to cover different aspects of text generation:
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+
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+ #### Evaluation results
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+
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+ Benchmark | Metric | RecurrentGemma 9B
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+ ------------------- | ------------- | -----------------
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+ [MMLU] | 5-shot, top-1 | 60.5
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+ [HellaSwag] | 0-shot | 80.4
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+ [PIQA] | 0-shot | 81.3
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+ [SocialIQA] | 0-shot | 52.3
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+ [BoolQ] | 0-shot | 80.3
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+ [WinoGrande] | partial score | 73.6
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+ [CommonsenseQA] | 7-shot | 73.2
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+ [OpenBookQA] | | 51.8
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+ [ARC-e][ARC-c] | | 78.8
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+ [ARC-c] | | 52.0
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+ [TriviaQA] | 5-shot | 70.5
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+ [Natural Questions] | 5-shot | 21.7
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+ [HumanEval] | pass@1 | 31.1
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+ [MBPP] | 3-shot | 42.0
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+ [GSM8K] | maj@1 | 42.6
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+ [MATH] | 4-shot | 23.8
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+ [AGIEval] | | 39.3
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+ [BIG-Bench] | | 55.2
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+ **Average** | | 56.1
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+
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+ ### Inference speed results
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+
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+ 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.
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+
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+ #### Throughput
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+
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+ 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.
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+
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+ <img src="max_throughput.png" width="400" alt="Maximum Throughput comparison of RecurrentGemma-9B and Gemma-7B">
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+
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+ #### Latency
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+
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+ 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.
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+
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+ \# Tokens Sampled | Gemma-7B (sec) | RecurrentGemma-9B (sec) | Improvement (%)
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+ ----------------- | -------------- | ----------------------- | ---------------
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+ 128 | 3.1 | 2.8 | 9.2%
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+ 256 | 5.9 | 5.4 | 9.7%
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+ 512 | 11.6 | 10.5 | 10.7%
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+ 1024 | 23.5 | 20.6 | 14.2%
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+ 2048 | 48.2 | 40.9 | 17.7%
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+ 4096 | 101.9 | 81.5 | 25.0%
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+ 8192 | OOM | 162.8 | -
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+ 16384 | OOM | 325.2 | -
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+
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+
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+ ## Ethics and safety
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+
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+ ### Ethics and safety evaluations
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+
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+ #### Evaluations approach
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+
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+ Our evaluation methods include structured evaluations and internal red-teaming
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+ testing of relevant content policies. Red-teaming was conducted by a number of
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+ different teams, each with different goals and human evaluation metrics. These
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+ models were evaluated against a number of different categories relevant to
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+ ethics and safety, including:
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+
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+ * **Text-to-text content safety:** Human evaluation on prompts covering safety
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+ policies including child sexual abuse and exploitation, harassment, violence
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+ and gore, and hate speech.
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+ * **Text-to-text representational harms:** Benchmark against relevant academic
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+ datasets such as WinoBias and BBQ Dataset.
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+ * **Memorization:** Automated evaluation of memorization of training data,
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+ including the risk of personally identifiable information exposure.
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+ * **Large-scale harm:** Tests for “dangerous capabilities,” such as chemical,
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+ biological, radiological, and nuclear (CBRN) risks; as well as tests for
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+ persuasion and deception, cybersecurity, and autonomous replication.
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+
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+ #### Evaluation results
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+
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+ The results of ethics and safety evaluations are within acceptable thresholds
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+ for meeting [internal
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+ policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11)
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+ for categories such as child safety, content safety, representational harms,
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+ memorization, large-scale harms. On top of robust internal evaluations, the
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+ results of well known safety benchmarks like BBQ, Winogender, Winobias,
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+ RealToxicity, and TruthfulQA are shown here.
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+
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+ Benchmark | Metric | RecurrentGemma 9B | RecurrentGemma 9B IT
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+ ------------------------ | ------ | ----------------- | --------------------
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+ [RealToxicity] | avg | 10.3 | 8.8
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+ [BOLD] | | 39.8 | 47.9
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+ [CrowS-Pairs] | top-1 | 38.7 | 39.5
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+ [BBQ Ambig][BBQ] | top-1 | 95.9 | 67.1
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+ [BBQ Disambig][BBQ] | top-1 | 78.6 | 78.9
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+ [Winogender] | top-1 | 59.0 | 64.0
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+ [TruthfulQA] | | 38.6 | 47.7
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+ [Winobias 1_2][Winobias] | | 61.5 | 60.6
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+ [Winobias 2_2][Winobias] | | 90.2 | 90.3
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+ [Toxigen] | | 58.8 | 64.5
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+
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+ ## Model usage and limitations
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+
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+ ### Known limitations
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+
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+ These models have certain limitations that users should be aware of:
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+
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+ * **Training data**
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+ * The quality and diversity of the training data significantly influence
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+ the model's capabilities. Biases or gaps in the training data can lead
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+ to limitations in the model's responses.
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+ * The scope of the training dataset determines the subject areas the model
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+ can handle effectively.
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+ * **Context and task complexity**
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+ * LLMs are better at tasks that can be framed with clear prompts and
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+ instructions. Open-ended or highly complex tasks might be challenging.
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+ * A model's performance can be influenced by the amount of context
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+ provided (longer context generally leads to better outputs, up to a
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+ certain point).
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+ * **Language ambiguity and nuance**
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+ * Natural language is inherently complex. LLMs might struggle to grasp
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+ subtle nuances, sarcasm, or figurative language.
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+ * **Factual accuracy**
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+ * LLMs generate responses based on information they learned from their
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+ training datasets, but they are not knowledge bases. They may generate
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+ incorrect or outdated factual statements.
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+ * **Common sense**
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+ * LLMs rely on statistical patterns in language. They might lack the
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+ ability to apply common sense reasoning in certain situations.
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+
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+ ### Ethical considerations and risks
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+
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+ The development of large language models (LLMs) raises several ethical concerns.
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+ In creating an open model, we have carefully considered the following:
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+
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+ * **Bias and fairness**
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+ * LLMs trained on large-scale, real-world text data can reflect
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+ socio-cultural biases embedded in the training material. These models
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+ underwent careful scrutiny, input data pre-processing described and
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+ posterior evaluations reported in this card.
245
+ * **Misinformation and misuse**
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+ * LLMs can be misused to generate text that is false, misleading, or
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+ harmful.
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+ * Guidelines are provided for responsible use with the model, see the
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+ [Responsible Generative AI
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+ Toolkit](https://ai.google.dev/gemma/responsible).
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+ * **Transparency and accountability**
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+ * This model card summarizes details on the models' architecture,
253
+ capabilities, limitations, and evaluation processes.
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+ * A responsibly developed open model offers the opportunity to share
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+ innovation by making LLM technology accessible to developers and
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+ researchers across the AI ecosystem.
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+
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+ Risks Identified and Mitigations:
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+
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+ * **Perpetuation of biases:** It's encouraged to perform continuous monitoring
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+ (using evaluation metrics, human review) and the exploration of de-biasing
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+ techniques during model training, fine-tuning, and other use cases.
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+ * **Generation of harmful content:** Mechanisms and guidelines for content
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+ safety are essential. Developers are encouraged to exercise caution and
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+ implement appropriate content safety safeguards based on their specific
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+ product policies and application use cases.
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+ * **Misuse for malicious purposes:** Technical limitations and developer and
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+ end-user education can help mitigate against malicious applications of LLMs.
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+ Educational resources and reporting mechanisms for users to flag misuse are
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+ provided. Prohibited uses of Gemma models are outlined in our [terms of
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+ use](https://www.kaggle.com/models/google/gemma/license/consent).
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+ * **Privacy violations:** Models were trained on data filtered for removal of
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+ PII (Personally Identifiable Information). Developers are encouraged to
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+ adhere to privacy regulations with privacy-preserving techniques.
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+
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+ ## Intended usage
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+
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+ ### Application
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+
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+ Open Large Language Models (LLMs) have a wide range of applications across
281
+ various industries and domains. The following list of potential uses is not
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+ comprehensive. The purpose of this list is to provide contextual information
283
+ about the possible use-cases that the model creators considered as part of model
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+ training and development.
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+
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+ * **Content creation and communication**
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+ * **Text generation:** These models can be used to generate creative text
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+ formats like poems, scripts, code, marketing copy, email drafts, etc.
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+ * **Chatbots and conversational AI:** Power conversational interfaces for
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+ customer service, virtual assistants, or interactive applications.
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+ * **Text summarization:** Generate concise summaries of a text corpus,
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+ research papers, or reports.
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+ * **Research and education**
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+ * **Natural Language Processing (NLP) research:** These models can serve
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+ as a foundation for researchers to experiment with NLP techniques,
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+ develop algorithms, and contribute to the advancement of the field.
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+ * **Language Learning Tools:** Support interactive language learning
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+ experiences, aiding in grammar correction or providing writing practice.
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+ * **Knowledge Exploration:** Assist researchers in exploring large bodies
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+ of text by generating summaries or answering questions about specific
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+ topics.
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+
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+ ### Benefits
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+
305
+ At the time of release, this family of models provides high-performance open
306
+ large language model implementations designed from the ground up for Responsible
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+ AI development compared to similarly sized models.
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+
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+ Using the benchmark evaluation metrics described in this document, these models
310
+ have shown to provide superior performance to other, comparably-sized open model
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+ alternatives.
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+
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+ In particular, RecurrentGemma models achieve comparable performance to Gemma
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+ models but are faster during inference and require less memory, especially on
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+ long sequences.
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+
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+ [MMLU]: https://arxiv.org/abs/2009.03300
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+ [HellaSwag]: https://arxiv.org/abs/1905.07830
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+ [PIQA]: https://arxiv.org/abs/1911.11641
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+ [SocialIQA]: https://arxiv.org/abs/1904.09728
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+ [BoolQ]: https://arxiv.org/abs/1905.10044
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+ [winogrande]: https://arxiv.org/abs/1907.10641
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+ [CommonsenseQA]: https://arxiv.org/abs/1811.00937
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+ [OpenBookQA]: https://arxiv.org/abs/1809.02789
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+ [ARC-c]: https://arxiv.org/abs/1911.01547
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+ [TriviaQA]: https://arxiv.org/abs/1705.03551
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+ [Natural Questions]: https://github.com/google-research-datasets/natural-questions
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+ [HumanEval]: https://arxiv.org/abs/2107.03374
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+ [MBPP]: https://arxiv.org/abs/2108.07732
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+ [GSM8K]: https://arxiv.org/abs/2110.14168
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+ [MATH]: https://arxiv.org/abs/2103.03874
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+ [AGIEval]: https://arxiv.org/abs/2304.06364
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+ [BIG-Bench]: https://arxiv.org/abs/2206.04615
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+ [RealToxicity]: https://arxiv.org/abs/2009.11462
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+ [BOLD]: https://arxiv.org/abs/2101.11718
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+ [CrowS-Pairs]: https://aclanthology.org/2020.emnlp-main.154/
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+ [BBQ]: https://arxiv.org/abs/2110.08193v2
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+ [Winogender]: https://arxiv.org/abs/1804.09301
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+ [TruthfulQA]: https://arxiv.org/abs/2109.07958
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+ [winobias]: https://arxiv.org/abs/1804.06876
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+ [Toxigen]: https://arxiv.org/abs/2203.09509
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+ "architectures": [
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+ "RecurrentGemmaForCausalLM"
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+ "attention_bias": false,
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+ "intermediate_size": 24576,
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+ "logits_soft_cap": 30.0,
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+ "lru_width": 4096,
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+ "model_type": "recurrent_gemma",
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+ "num_attention_heads": 16,
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+ "pad_token_id": 0,
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+ "rms_norm_eps": 1e-06,
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+ "rope_theta": 10000.0,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.42.0.dev0",
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+ "use_cache": true,
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+ "vocab_size": 256000,
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+ "w_init_variance_scale": 0.01
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+ }
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1466
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1467
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1468
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1469
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1470
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1471
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1472
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1473
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1474
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1475
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1476
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1477
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1478
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1482
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1491
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1492
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1494
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1497
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1498
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1499
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1500
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1501
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1502
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1503
+ "<start_of_turn>",
1504
+ "<end_of_turn>"
1505
+ ],
1506
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1507
+ "clean_up_tokenization_spaces": false,
1508
+ "eos_token": "<eos>",
1509
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1510
+ "pad_token": "<pad>",
1511
+ "sp_model_kwargs": {},
1512
+ "spaces_between_special_tokens": false,
1513
+ "tokenizer_class": "GemmaTokenizer",
1514
+ "unk_token": "<unk>",
1515
+ "use_default_system_prompt": false
1516
+ }