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README.md

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----
-license: gemma
-library_name: transformers
-pipeline_tag: text-generation
-extra_gated_heading: Access Gemma on Hugging Face
-extra_gated_prompt: >-
-  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.
-extra_gated_button_content: Acknowledge license
-tags:
-- conversational
----
-
-
-# Gemma 2 model card
-
-**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
-
-**Resources and Technical Documentation**:
-
-* [Responsible Generative AI Toolkit][rai-toolkit]
-* [Gemma on Kaggle][kaggle-gemma]
-* [Gemma on Vertex Model Garden][vertex-mg-gemma]
-
-**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent/verify/huggingface?returnModelRepoId=google/gemma-2-9b-it)
-
-**Authors**: Google
-
-## Model Information
-
-Summary description and brief definition of inputs and outputs.
-
-### 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.
-
-### Usage
-
-Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
-
-
-#### Running the model on a single / multi GPU
-
-
-```python
-# pip install accelerate
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-
-tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
-model = AutoModelForCausalLM.from_pretrained(
-    "google/gemma-2-9b-it",
-    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]))
-```
-
-<a name="precisions"></a>
-#### Running the model on a GPU using different precisions
-
-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.
-
-* _Upcasting to `torch.float32`_
-
-```python
-# pip install accelerate
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
-model = AutoModelForCausalLM.from_pretrained(
-    "google/gemma-2-9b-it",
-    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]))
-```
-
-#### Quantized Versions through `bitsandbytes`
-
-* _Using 8-bit precision (int8)_
-
-```python
-# pip install bitsandbytes accelerate
-from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
-
-quantization_config = BitsAndBytesConfig(load_in_8bit=True)
-
-tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
-model = AutoModelForCausalLM.from_pretrained(
-    "google/gemma-2-9b-it",
-    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_
-
-```python
-# pip install bitsandbytes accelerate
-from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
-
-quantization_config = BitsAndBytesConfig(load_in_4bit=True)
-
-tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
-model = AutoModelForCausalLM.from_pretrained(
-    "google/gemma-2-9b-it",
-    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`
-
-```diff
-model = AutoModelForCausalLM.from_pretrained(
-    model_id, 
-    torch_dtype=torch.float16, 
-+   attn_implementation="flash_attention_2"
-).to(0)
-```
-
-### Chat Template
-
-The instruction-tuned models use a chat template that must be adhered to for conversational use.
-The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.
-
-Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:
-
-```py
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import transformers
-import torch
-
-model_id = "google/gemma-2-9b-it"
-dtype = torch.bfloat16
-
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(
-    model_id,
-    device_map="cuda",
-    torch_dtype=dtype,)
-
-chat = [
-    { "role": "user", "content": "Write a hello world program" },
-]
-prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
-```
-
-At this point, the prompt contains the following text:
-
-```
-<bos><start_of_turn>user
-Write a hello world program<end_of_turn>
-<start_of_turn>model
-```
-
-As you can see, each turn is preceded by a `<start_of_turn>` delimiter and then the role of the entity
-(either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with
-the `<end_of_turn>` token.
-
-You can follow this format to build the prompt manually, if you need to do it without the tokenizer's
-chat template.
-
-After the prompt is ready, generation can be performed like this:
-
-```py
-inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
-outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)
-print(tokenizer.decode(outputs[0]))
-```
-
-### Inputs and outputs
-
-*   **Input:** Text string, such as a question, a prompt, or a document to be
-    summarized.
-*   **Output:** Generated English-language text in response to the input, such
-    as an answer to a question, or a summary of a document.
-
-### Citation
-
-```none
-@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
-
-Data used for model training and how the data was processed.
-
-### 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 and the 9B model was trained with 8 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
-
-Details about the model internals.
-
-### 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
-
-Model evaluation metrics and results.
-
-### Benchmark Results
-
-These models were evaluated against a large collection of different datasets and
-metrics to cover different aspects of text generation:
-
-| Benchmark                      | Metric        | Gemma PT 9B | Gemma PT 27B |
-| ------------------------------ | ------------- | ----------- | ------------ |
-| [MMLU][mmlu]                   | 5-shot, top-1 | 71.3        | 75.2         |
-| [HellaSwag][hellaswag]         | 10-shot       | 81.9        | 86.4         |
-| [PIQA][piqa]                   | 0-shot        | 81.7        | 83.2         |
-| [SocialIQA][socialiqa]         | 0-shot        | 53.4        | 53.7         |
-| [BoolQ][boolq]                 | 0-shot        | 84.2        | 84.8         |
-| [WinoGrande][winogrande]       | partial score | 80.6        | 83.7         |
-| [ARC-e][arc]                   | 0-shot        | 88.0        | 88.6         |
-| [ARC-c][arc]                   | 25-shot       | 68.4        | 71.4         |
-| [TriviaQA][triviaqa]           | 5-shot        | 76.6        | 83.7         |
-| [Natural Questions][naturalq]  | 5-shot        | 29.2        | 34.5         |
-| [HumanEval][humaneval]         | pass@1        | 40.2        | 51.8         |
-| [MBPP][mbpp]                   | 3-shot        | 52.4        | 62.6         |
-| [GSM8K][gsm8k]                 | 5-shot, maj@1 | 68.6        | 74.0         |
-| [MATH][math]                   | 4-shot        | 36.6        | 42.3         |
-| [AGIEval][agieval]             | 3-5-shot      | 52.8        | 55.1         |
-| [BIG-Bench][big-bench]         | 3-shot, CoT   | 68.2        | 74.9         |
-| ------------------------------ | ------------- | ----------- | ------------ |
-
-## Ethics and Safety
-
-Ethics and safety evaluation approach and results.
-
-### 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.
-
-#### Gemma 2.0
-
-| Benchmark                | Metric        | Gemma 2 IT 9B | Gemma 2 IT 27B |
-| ------------------------ | ------------- | --------------- | ---------------- |
-| [RealToxicity][realtox]  | average       |  8.25           |  8.84            |
-| [CrowS-Pairs][crows]     | top-1         | 37.47           | 36.67            |
-| [BBQ Ambig][bbq]         | 1-shot, top-1 | 88.58           | 85.99            |
-| [BBQ Disambig][bbq]      | top-1         | 82.67           | 86.94            |
-| [Winogender][winogender] | top-1         | 79.17           | 77.22            |
-| [TruthfulQA][truthfulqa] |               | 50.27           | 51.60            |
-| [Winobias 1_2][winobias] |               | 78.09           | 81.94            |
-| [Winobias 2_2][winobias] |               | 95.32           | 97.22            |
-| [Toxigen][toxigen]       |               | 39.30           | 38.42            |
-| ------------------------ | ------------- | --------------- | ---------------- |
-
-## Usage and Limitations
-
-These models have certain limitations that users should be aware of.
-
-### 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 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.
-  * Guidelines are provided for responsible use with the model, see the
-    [Responsible Generative AI Toolkit][rai-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
-  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 the
-  [Gemma Prohibited Use Policy][prohibited-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.
-
-### 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.
-
-[rai-toolkit]: https://ai.google.dev/responsible
-[kaggle-gemma]: https://www.kaggle.com/models/google/gemma-2
-[terms]: https://ai.google.dev/gemma/terms
-[vertex-mg-gemma]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335
-[sensitive-info]: https://cloud.google.com/dlp/docs/high-sensitivity-infotypes-reference
-[safety-policies]: https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11
-[prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy
-[tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu
-[sustainability]: https://sustainability.google/operating-sustainably/
-[jax]: https://github.com/google/jax
-[ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/
-[sustainability]: https://sustainability.google/operating-sustainably/
-[foundation-models]: https://ai.google/discover/foundation-models/
-[gemini-2-paper]: https://goo.gle/gemma2report
-[mmlu]: https://arxiv.org/abs/2009.03300
-[hellaswag]: https://arxiv.org/abs/1905.07830
-[piqa]: https://arxiv.org/abs/1911.11641
-[socialiqa]: https://arxiv.org/abs/1904.09728
-[boolq]: https://arxiv.org/abs/1905.10044
-[winogrande]: https://arxiv.org/abs/1907.10641
-[commonsenseqa]: https://arxiv.org/abs/1811.00937
-[openbookqa]: https://arxiv.org/abs/1809.02789
-[arc]: https://arxiv.org/abs/1911.01547
-[triviaqa]: https://arxiv.org/abs/1705.03551
-[naturalq]: https://github.com/google-research-datasets/natural-questions
-[humaneval]: https://arxiv.org/abs/2107.03374
-[mbpp]: https://arxiv.org/abs/2108.07732
-[gsm8k]: https://arxiv.org/abs/2110.14168
-[realtox]: https://arxiv.org/abs/2009.11462
-[bold]: https://arxiv.org/abs/2101.11718
-[crows]: https://aclanthology.org/2020.emnlp-main.154/
-[bbq]: https://arxiv.org/abs/2110.08193v2
-[winogender]: https://arxiv.org/abs/1804.09301
-[truthfulqa]: https://arxiv.org/abs/2109.07958
-[winobias]: https://arxiv.org/abs/1804.06876
-[math]: https://arxiv.org/abs/2103.03874
-[agieval]: https://arxiv.org/abs/2304.06364
-[big-bench]: https://arxiv.org/abs/2206.04615
-[toxigen]: https://arxiv.org/abs/2203.09509