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README.md

@@ -0,0 +1,531 @@
+---
+library_name: transformers
+license: gemma
+tags:
+- autoquant
+- gguf
+widget:
+- messages:
+  - role: user
+    content: How does the brain work?
+inference:
+  parameters:
+    max_new_tokens: 200
+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
+---
+
+# Gemma Model Card
+
+**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
+
+This model card corresponds to the 2B instruct version of the Gemma model. You can also visit the model card of the [2B base model](https://huggingface.co/google/gemma-2b), [7B base model](https://huggingface.co/google/gemma-7b), and [7B instruct model](https://huggingface.co/google/gemma-7b-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-it-gg-hf)
+
+**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent/verify/huggingface?returnModelRepoId=google/gemma-2b-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, 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 CPU
+
+As explained below, we recommend `torch.bfloat16` as the default dtype. You can use [a different precision](#precisions) if necessary.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
+model = AutoModelForCausalLM.from_pretrained(
+    "google/gemma-2b-it",
+    torch_dtype=torch.bfloat16
+)
+
+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
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
+model = AutoModelForCausalLM.from_pretrained(
+    "google/gemma-2b-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 use `float16`, which may be faster on certain hardware, indicating the `torch_dtype` when loading the model. For convenience, the `float16` revision of the repo contains a copy of the weights already converted to that 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.
+
+* _Using `torch.float16`_
+
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
+model = AutoModelForCausalLM.from_pretrained(
+    "google/gemma-2b-it",
+    device_map="auto",
+    torch_dtype=torch.float16,
+    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]))
+```
+
+* _Upcasting to `torch.float32`_
+
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
+model = AutoModelForCausalLM.from_pretrained(
+    "google/gemma-2b-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-2b-it")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-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-2b-it")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-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 = "gg-hf/gemma-2b-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)
+```
+
+### Fine-tuning
+
+You can find some fine-tuning scripts 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 them to this model, simply change the model-id to `google/gemma-2b-it`.
+
+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 the English quotes dataset
+
+### 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        | 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.

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