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Gemma 2 Instruct 9B Keras by google

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  21   
  Arxiv:1705.03551   Arxiv:1804.06876   Arxiv:1804.09301   Arxiv:1809.02789   Arxiv:1811.00937   Arxiv:1904.09728   Arxiv:1905.07830   Arxiv:1905.10044   Arxiv:1907.10641   Arxiv:1911.01547   Arxiv:1911.11641   Arxiv:2009.03300   Arxiv:2009.11462   Arxiv:2101.11718   Arxiv:2103.03874   Arxiv:2107.03374   Arxiv:2108.07732   Arxiv:2109.07958   Arxiv:2110.08193   Arxiv:2110.14168   Arxiv:2203.09509   Arxiv:2206.04615   Arxiv:2304.06364   Instruct   Keras-hub   Region:us
Model Card on HF 🤗: https://huggingface.co/google/gemma-2-instruct-9b-keras 

Gemma 2 Instruct 9B Keras Benchmarks

LLME Score: 0.1851
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma 2 Instruct 9B Keras (google/gemma-2-instruct-9b-keras)

Gemma 2 Instruct 9B Keras Parameters and Internals

Model Type 
text-to-text, decoder-only, large language model
Use Cases 
Areas:
Content Creation and Communication, Research and Education
Applications:
Text Generation, Chatbots and Conversational AI, Text Summarization, NLP Research, Language Learning Tools, Knowledge Exploration
Limitations:
Training Data, Context and Task Complexity, Language Ambiguity and Nuance, Factual Accuracy, Common Sense
Considerations:
Consider performing continuous monitoring and exploration of de-biasing techniques.
Supported Languages 
English (proficient)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
9B model trained with 8 trillion tokens
Methodology:
Text-to-text, decoder-only large language models
Hardware Used:
TPUv5p
Safety Evaluation 
Methodologies:
structured evaluations, internal red-teaming testing
Risk Categories:
Text-to-Text Content Safety, Text-to-Text Representational Harms, Memorization, Large-scale harm
Ethical Considerations:
Child safety, content safety, representational harms, memorization, large-scale harms
Responsible Ai Considerations 
Fairness:
Bias and Fairness evaluations against datasets such as WinoBias and BBQ Dataset
Transparency:
Model card provides details on architecture, capabilities, limitations, and evaluation processes.
Accountability:
Responsible AI Toolkit
Mitigation Strategies:
Continuous monitoring, content safety mechanisms, developer education
Input Output 
Input Format:
Text string
Accepted Modalities:
text
Output Format:
Generated English-language text
LLM NameGemma 2 Instruct 9B Keras
Repository 🤗https://huggingface.co/google/gemma-2-instruct-9b-keras 
Model Size9b
Updated2025-04-29
Maintainergoogle
Instruction-BasedYes

Rank the Gemma 2 Instruct 9B Keras Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227
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