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Gemma 2 9B by google

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  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   Autotrain compatible   Endpoints compatible   Gemma2   Region:us   Safetensors   Sharded   Tensorflow

Gemma 2 9B Benchmarks

MMLU Pro: 1.56
GPQA: 1.34
MUSR: 0.62
BBH: 3.21
IFEval: 20.59 vs 88 (so35)-76.6%
LLME Score: 0.39352
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

Gemma 2 9B Parameters and Internals

Model Type 
text generation, 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
Primary Use Cases:
Text generation tasks such as question answering, summarization, reasoning
Limitations:
Biases or gaps in data, open-ended tasks may be challenging, accuracy on factual information
Considerations:
Developers should be mindful of content safety and privacy issues.
Additional Notes 
Models require adequate safety safeguards based on application use cases.
Supported Languages 
English (proficient)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
9B model with 8 trillion tokens
Hardware Used:
TPUv5p
Model Architecture:
text-to-text, decoder-only
Safety Evaluation 
Methodologies:
internal red-teaming, structured evaluations
Findings:
Meets internal policies for child safety, content safety, representational harms, memorization, large-scale harms
Risk Categories:
child sexual abuse and exploitation, harassment, violence and gore, hate speech
Responsible Ai Considerations 
Fairness:
Models undergo scrutiny for socio-cultural biases.
Transparency:
Model details are shared in the model card.
Accountability:
Guidelines for responsible use are provided.
Mitigation Strategies:
Continuous monitoring and de-biasing encouraged.
Input Output 
Input Format:
Text string
Accepted Modalities:
text
Output Format:
Generated English-language text
LLM NameGemma 2 9B
Repository 🤗https://huggingface.co/google/gemma-2-9b 
Model Size9b
Required VRAM37.1 GB
Updated2024-11-21
Maintainergoogle
Model Typegemma2
Model Files  4.8 GB: 1-of-8   5.0 GB: 2-of-8   5.0 GB: 3-of-8   4.9 GB: 4-of-8   5.0 GB: 5-of-8   5.0 GB: 6-of-8   5.0 GB: 7-of-8   2.4 GB: 8-of-8
Model ArchitectureGemma2ForCausalLM
Licensegemma
Context Length8192
Model Max Length8192
Transformers Version4.42.0.dev0
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typefloat32
Gemma 2 9B (google/gemma-2-9b)

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Note: green Score (e.g. "73.2") means that the model is better than google/gemma-2-9b.

Rank the Gemma 2 9B 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 v20241110
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