Update README.md

README.md

@@ -1,199 +1,222 @@
 ---
+language:
+- ko
+- en
+license: other
 library_name: transformers
-tags: []
+license_name: gemma-terms-of-use
+license_link: https://ai.google.dev/gemma/terms
+pipeline_tag: text-generation
+tags:
+- pytorch
 ---
 
-# Model Card for Model ID
+# Gemma-Ko
 
-<!-- Provide a quick summary of what the model is/does. -->
+**Quantuzed from:**: [Gemma Ko](https://huggingface.co/beomi/gemma-ko-7b)
+**Original Gemma Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
 
+This model card corresponds to the 7B base version of the **Gemma-Ko** model. 
 
+**Resources and Technical Documentation**:
 
-## Model Details
+* [Original Google's Gemma-7B](https://huggingface.co/google/gemma-7b)
+* [Training Code @ Github: Gemma-EasyLM](https://github.com/Beomi/Gemma-EasyLM)
 
-### Model Description
+**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
 
-<!-- Provide a longer summary of what this model is. -->
+**Citation**
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+```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 }
+}
+```
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+**Model Developers**: Junbum Lee (Beomi) & Taekyoon Choi (Taekyoon)
 
-### Model Sources [optional]
+## Model Information
 
-<!-- Provide the basic links for the model. -->
+Summary description and brief definition of inputs and outputs.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Description
 
-## Uses
+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.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+### Usage
 
-### Direct Use
+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.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+#### Running the model on a CPU
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-### Downstream Use [optional]
+tokenizer = AutoTokenizer.from_pretrained("beomi/gemma-ko-7b")
+model = AutoModelForCausalLM.from_pretrained("beomi/gemma-ko-7b")
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+input_text = "머신러닝과 딥러닝의 차이는"
+input_ids = tokenizer(input_text, return_tensors="pt")
 
-[More Information Needed]
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
 
-### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+#### Running the model on a single / multi GPU
 
-[More Information Needed]
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-## Bias, Risks, and Limitations
+tokenizer = AutoTokenizer.from_pretrained("beomi/gemma-ko-7b")
+model = AutoModelForCausalLM.from_pretrained("beomi/gemma-ko-7b", device_map="auto")
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+input_text = "머신러닝과 딥러닝의 차이는"
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
 
-[More Information Needed]
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
 
-### Recommendations
+#### Other optimizations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+* _Flash Attention 2_
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+First make sure to install `flash-attn` in your environment `pip install flash-attn`
 
-## How to Get Started with the Model
+```diff
+model = AutoModelForCausalLM.from_pretrained(
+    "beomi/gemma-ko-7b", 
+    torch_dtype=torch.float16, 
++   attn_implementation="flash_attention_2"
+).to(0)
+```
 
-Use the code below to get started with the model.
+### Inputs and outputs
 
-[More Information Needed]
+*   **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.
 
-## Training Details
+## Implementation Information
 
-### Training Data
+Details about the model internals.
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+### Software
 
-[More Information Needed]
+Training was done using [beomi/Gemma-EasyLM](https://github.com/Beomi/Gemma-EasyLM).
 
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+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.