Update README.md

README.md

@@ -10,306 +10,30 @@ license_link: https://ai.google.dev/gemma/terms
 
 This model card corresponds to the 2B and 7B Instruct versions of the Gemma model's Guff.
 
-***The contents of this card have been copied from [Google's Gemma](https://huggingface.co/google/gemma-7b) Page***
-
-**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-7b-gg-hf)
-
 **Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
 
-**Authors**: Google
-
-## Model Information
-
-Summary description and a 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 cloud infrastructure, democratizing access to
-state-of-the-art AI models and helping foster innovation for everyone.
 
 #### Model Usage
-- Since this is a `guff`, it can be run locally using
-  - Ollama
-  - Llama.cpp
-  - LM Studio
-  - And Many More
-
-### 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 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, and symbolic representation, and 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        | 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.
+Since this is a `guff`, it can be run locally using
+- Ollama
+- Llama.cpp
+- LM Studio
+- And Many More
+- I have provided [GemmaModelFile](https://huggingface.co/c2p-cmd/google_gemma_guff/blob/main/GemmaModelFile) that can be used with ollama by:
+- Download the model:
+  ```python
+  pip install huggingface_hub
+  from huggingface_hub import hf_hub_download
+  
+  model_id="c2p-cmd/google_gemma_guff"
+  hf_hub_download(repo_id=model_id, local_dir="gemma_snapshot", local_dir_use_symlinks=False, filename="gemma_snapshot/gemma-2b-it.gguf")
+  ```
+- Load the model file to ollama
+  ```shell
+  ollama create gemma -f GemmaModelFile
+  ```
+- You change the model name based on needs