Create README.md

README.md

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+---
+language:
+- ko
+- en
+- zh
+- ja
+license: other
+library_name: transformers
+license_name: gemma-terms-of-use
+license_link: https://ai.google.dev/gemma/terms
+pipeline_tag: text-generation
+tags:
+- pytorch
+---
+
+# Gemma-Mling: Multilingual Gemma
+
+> Update @ 2024.04.15: First release of Gemma-Mling 7B model
+
+**Original Gemma Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
+
+This model card corresponds to the 7B base version of the **Gemma-Mling** model. 
+
+**Resources and Technical Documentation**:
+
+* [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_mling_7b,
+	author       = { {Junbum Lee, Taekyoon Choi} },
+	title        = { gemma-mling-7b },
+	year         = 2024,
+	url          = { https://huggingface.co/beomi/gemma-mling-7b },
+	publisher    = { Hugging Face }
+}
+```
+
+**Model Developers**: Junbum Lee (Beomi) & Taekyoon Choi (Taekyoon)
+
+## Model Information
+
+### 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-mling-7b")
+model = AutoModelForCausalLM.from_pretrained("beomi/gemma-mling-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-mling-7b")
+model = AutoModelForCausalLM.from_pretrained("beomi/gemma-mling-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]))
+```
+
+### Inputs and outputs
+
+*   **Input:** Text string, such as a question, a prompt, or a document to be
+    summarized.
+*   **Output:** Generated Multilingual-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.