Update README.md

README.md

@@ -1,42 +1,435 @@
 ---
+# license: gemma
+# widget :
+# - text: "What is Evening Star?"
+#   example_title: "What is Evening Star?"
+# - text : "கடுகு சிறுத்தாலும்"
+#   example_title: "கடுகு சிறுத்தாலும்"
+# - text : "யானைக்கும் அடி"
+#   example_title : "யானைக்கும் அடி"
+# ---
+# ---
+library_name: transformers
+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: gemma
-widget :
-- text: "What is Evening Star?"
-  example_title: "What is Evening Star?"
-- text : "கடுகு சிறுத்தாலும்"
-  example_title: "கடுகு சிறுத்தாலும்"
-- text : "யானைக்கும் அடி"
-  example_title : "யானைக்கும் அடி"
 ---
-# GPT2-Tamil
+# Gemma Model Card
 
-## Model description
-GPT2-Tamil is a GPT-2 transformer model fine Tuned on a  large corpus of Tamil data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences.
+**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
 
-More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token i only uses the inputs from 1 to i but not the future tokens.
+This model card corresponds to the 7B base version of the Gemma model. You can also visit the model card of the [2B base model](https://huggingface.co/google/gemma-2b), [7B instruct model](https://huggingface.co/google/gemma-7b-it), and [2B instruct model](https://huggingface.co/google/gemma-2b-it). 
 
-This way, the model learns an inner representation of the Tamil language that can then be used to extract features useful for downstream tasks.
-## Intended uses & limitations
+**Resources and Technical Documentation**:
+
+* [Gemma Technical Report](https://storage.googleapis.com/deepmind-media/gemma/gemma-report.pdf)
+* [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-7b-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.
+
+### Context Length
+Models are trained on a context length of 8192 tokens.
+
+### 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 examples
+
+You can find fine-tuning notebooks under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples). We provide:
+
+* A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using [QLoRA](https://huggingface.co/papers/2305.14314)
+* 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. You can also find the copy of the notebook [here](https://github.com/huggingface/notebooks/blob/main/peft/gemma_7b_english_quotes.ipynb).
+
+#### Running the model on a CPU
 
-You can use the raw model for text generation or fine-tune it to a downstream task. See the
-[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
 
-## Usage
-You can use this model for Tamil text generation:
 ```python
->>> from transformers import TFGPT2LMHeadModel, GPT2Tokenizer
->>> tokenizer = GPT2Tokenizer.from_pretrained("Lagstill/GPT-2-Tamil")
->>> model = TFGPT2LMHeadModel.from_pretrained("Lagstill/GPT-2-Tamil")
->>> text = "அகத்தின் அழகு"
->>> encoded_text = tokenizer.encode(text, return_tensors='tf')
->>> beam_output = model.generate(
-    encoded_text,
-    max_length=100,
-    num_beams=5,
-    temperature=0.7,
-    no_repeat_ngram_size=2,
-    num_return_sequences=5
-    )
->>> print(tokenizer.decode(beam_output[0], skip_special_tokens=True))
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b")
+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-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", 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-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", revision="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-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", 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-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", 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-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", 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).
+
+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        | 49.7        | 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        | 12.5       | 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**                    |               | **45.0**    | **56.9**  |
+
+
+## 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](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.