Upload README.md

README.md

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+---
+library_name: transformers
+tags: []
+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"
+license: other
+license_name: gemma-terms-of-use
+license_link: https://ai.google.dev/gemma/terms
+---
+
+AWQ quantized version of gemma-2b model.
+
+---
+
+# Gemma Model Card
+
+**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
+
+This model card corresponds to the 2B base version of the Gemma model. You can also visit the model card of the [7B base model](https://huggingface.co/google/gemma-7b), [7B instruct model](https://huggingface.co/google/gemma-7b-it), and [2B instruct model](https://huggingface.co/google/gemma-2b-it). 
+
+**Resources and Technical Documentation**:
+
+* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
+* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
+* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335?version=gemma-2b-gg-hf)
+
+**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
+
+**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, 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.
+
+
+#### Fine-tuning the model
+
+You can find fine-tuning scripts and notebook under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples) of [`google/gemma-7b`](https://huggingface.co/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
+
+
+```python
+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]))
+```
+
+
+#### Running the model on a single / multi GPU
+
+
+```python
+# 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`_
+
+```python
+# 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", 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`_
+
+```python
+# 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", 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)_
+
+```python
+# 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_
+
+```python
+# 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`
+
+```diff
+model = AutoModelForCausalLM.from_pretrained(
+ model_id, 
+ torch_dtype=torch.float16, 
++ attn_implementation="flash_attention_2"
+).to(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.
+
+## 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, totaling 6 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 safely in line with
+ [our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11).
+
+## Implementation Information
+
+Details about the model internals.
+
+### Hardware
+
+Gemma was trained using the latest generation of
+[Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e).
+
+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](https://sustainability.google/operating-sustainably/).
+
+### Software
+
+Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/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](https://ai.google/discover/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](https://arxiv.org/abs/2312.11805); "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 | 2B Params | 7B Params |
+| ------------------------------ | ------------- | ----------- | --------- |
+| [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot, top-1 | 42.3 | 64.3 |
+| [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot |71.4 | 81.2 |
+| [PIQA](https://arxiv.org/abs/1911.11641) | 0-shot | 77.3 | 81.2 |
+| [SocialIQA](https://arxiv.org/abs/1904.09728) | 0-shot | 59.7 | 51.8 |
+| [BooIQ](https://arxiv.org/abs/1905.10044) | 0-shot | 69.4 | 83.2 |
+| [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | 65.4 | 72.3 |
+| [CommonsenseQA](https://arxiv.org/abs/1811.00937) | 7-shot | 65.3 | 71.3 |
+| [OpenBookQA](https://arxiv.org/abs/1809.02789) | | 47.8 | 52.8 |
+| [ARC-e](https://arxiv.org/abs/1911.01547) | | 73.2 | 81.5 |
+| [ARC-c](https://arxiv.org/abs/1911.01547) | | 42.1 | 53.2 |
+| [TriviaQA](https://arxiv.org/abs/1705.03551) | 5-shot | 53.2 | 63.4 |
+| [Natural Questions](https://github.com/google-research-datasets/natural-questions) | 5-shot | - | 23 |
+| [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 22.0 | 32.3 |
+| [MBPP](https://arxiv.org/abs/2108.07732) | 3-shot | 29.2 | 44.4 |
+| [GSM8K](https://arxiv.org/abs/2110.14168) | maj@1 | 17.7 | 46.4 |
+| [MATH](https://arxiv.org/abs/2108.07732) | 4-shot | 11.8 | 24.3 |
+| [AGIEval](https://arxiv.org/abs/2304.06364) | | 24.2 | 41.7 |
+| [BIG-Bench](https://arxiv.org/abs/2206.04615) | | 35.2 | 55.1 |
+| ------------------------------ | ------------- | ----------- | --------- |
+| **Average** | | **54.0** | **56.4** |
+
+## 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](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2).
+* 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](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) 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.
+
+| Benchmark | Metric | 2B Params | 7B Params |
+| ------------------------------ | ------------- | ----------- | --------- |
+| [RealToxicity](https://arxiv.org/abs/2009.11462) | average | 6.86 | 7.90 |
+| [BOLD](https://arxiv.org/abs/2101.11718) | | 45.57 | 49.08 |
+| [CrowS-Pairs](https://aclanthology.org/2020.emnlp-main.154/) | top-1 | 45.82 | 51.33 |
+| [BBQ Ambig](https://arxiv.org/abs/2110.08193v2) | 1-shot, top-1 | 62.58 | 92.54 |
+| [BBQ Disambig](https://arxiv.org/abs/2110.08193v2) | top-1 | 54.62 | 71.99 |
+| [Winogender](https://arxiv.org/abs/1804.09301) | top-1 | 51.25 | 54.17 |
+| [TruthfulQA](https://arxiv.org/abs/2109.07958) | | 44.84 | 31.81 |
+| [Winobias 1_2](https://arxiv.org/abs/1804.06876) | | 56.12 | 59.09 |
+| [Winobias 2_2](https://arxiv.org/abs/1804.06876) | | 91.10 | 92.23 |
+| [Toxigen](https://arxiv.org/abs/2203.09509) | | 29.77 | 39.59 |
+| ------------------------------ | ------------- | ----------- | --------- |
+
+
+## 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](http://ai.google.dev/gemma/responsible).
+* 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](https://ai.google.dev/gemma/prohibited_use_policy).
+* 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.
+