Gemma-Mling: Multilingual Gemma

Update @ 2024.04.15: First release of Gemma-Mling 7B model

• Original Google's Gemma-7B
• Training Code @ Github: Gemma-EasyLM

Model Information

Usage

Running the model on a CPU

Running the model on a single / multi GPU

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

Software

Dataset

Training Progress

• Report Link: https://api.wandb.ai/links/tgchoi/6lt0ce3s

Evaluation

Evaluation Scripts

• For Knowledge / KoBest / XCOPA / XWinograd
  • EleutherAI/lm-evaluation-harness v0.4.2
    bash

    1!git clone https://github.com/EleutherAI/lm-evaluation-harness.git 2!cd lm-evaluation-harness && pip install -r requirements.txt && pip install -e . 3 4!lm_eval --model hf \ 5 --model_args pretrained=beomi/gemma-mling-7b,dtype="float16" \ 6 --tasks "haerae,kobest,kmmlu_direct,cmmlu,ceval-valid,mmlu,xwinograd,xcopa \ 7 --num_fewshot "0,5,5,5,5,5,0,5" \ 8 --device cuda
• For JP Eval Harness
  • Stability-AI/lm-evaluation-harness (jp-stable branch)
    bash

    1!git clone -b jp-stable https://github.com/Stability-AI/lm-evaluation-harness.git 2!cd lm-evaluation-harness && pip install -e ".[ja]" 3!pip install 'fugashi[unidic]' && python -m unidic download 4 5!cd lm-evaluation-harness && python main.py \ 6 --model hf-causal \ 7 --model_args pretrained=beomi/gemma-mling-7b,torch_dtype='auto'" 8 --tasks "jcommonsenseqa-1.1-0.3,jnli-1.3-0.3,marc_ja-1.1-0.3,jsquad-1.1-0.3,jaqket_v2-0.2-0.3,xlsum_ja,mgsm" 9 --num_fewshot "3,3,3,2,1,1,5"

Benchmark Results

Usage and Limitations

Intended Usage

• 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

• 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.
• 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.

• 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.
• 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

Open LLM Leaderboard Evaluation Results