Update README.md

README.md

@@ -23,200 +23,4 @@ This model card corresponds to the 7B base version of the **Gemma-Ko** model.
 * [Original Google's Gemma-7B](https://huggingface.co/google/gemma-7b)
 * [Training Code @ Github: Gemma-EasyLM](https://github.com/Beomi/Gemma-EasyLM)
 
-**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
-
-**Citation**
-
-```bibtex
-@misc {gemma_ko_7b,
-	author       = { {Junbum Lee, Taekyoon Choi} },
-	title        = { gemma-ko-7b },
-	year         = 2024,
-	url          = { https://huggingface.co/beomi/gemma-ko-7b },
-	doi          = { 10.57967/hf/1859 },
-	publisher    = { Hugging Face }
-}
-```
-
-**Model Developers**: Junbum Lee (Beomi) & Taekyoon Choi (Taekyoon)
-
-## 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
-
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-tokenizer = AutoTokenizer.from_pretrained("beomi/gemma-ko-7b")
-model = AutoModelForCausalLM.from_pretrained("beomi/gemma-ko-7b")
-
-input_text = "머신러닝과 딥러닝의 차이는"
-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("beomi/gemma-ko-7b")
-model = AutoModelForCausalLM.from_pretrained("beomi/gemma-ko-7b", device_map="auto")
-
-input_text = "머신러닝과 딥러닝의 차이는"
-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(
-    "beomi/gemma-ko-7b", 
-    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 Korean/English-language text in response to the input, such
-    as an answer to a question, or a summary of a document.
-
-## Implementation Information
-
-Details about the model internals.
-
-### Software
-
-Training was done using [beomi/Gemma-EasyLM](https://github.com/Beomi/Gemma-EasyLM).
-
-
-## Evaluation
-
-Model evaluation metrics and results.
-
-### Benchmark Results
-
-TBD
-
-## 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.
-* 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.
-
-## Acknowledgement
-
-The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program.
+**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)