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Google Gemma 2 9B It by SillyTilly

 »  All LLMs  »  SillyTilly  »  Google Gemma 2 9B It   URL Share it on

  4   
  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   Conversational   Endpoints compatible   Gemma2   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/SillyTilly/google-gemma-2-9b-it 

Google Gemma 2 9B It Benchmarks

LLME Score: 0.15989
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Google Gemma 2 9B It (SillyTilly/google-gemma-2-9b-it)
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Google Gemma 2 9B It Parameters and Internals

Model Type 
text-to-text, decoder-only, large language model
Use Cases 
Areas:
Research, Commercial applications
Applications:
Content Creation and Communication, Research and Education
Primary Use Cases:
Text Generation, Chatbots and Conversational AI, Text Summarization
Limitations:
Open-ended or highly complex tasks, Language ambiguity and nuance, Factual inaccuracy, Common sense reasoning
Additional Notes 
Trained using JAX and ML Pathways.
Supported Languages 
English (Full Support)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
8 trillion tokens
Hardware Used:
TPUv5p
Safety Evaluation 
Methodologies:
Red-teaming, Internal evaluations
Findings:
Within acceptable thresholds for internal policies
Risk Categories:
Text-to-Text Content Safety, Text-to-Text Representational Harms, Memorization, Large-scale harm
Responsible Ai Considerations 
Fairness:
Careful scrutiny and pre-processing of input data
Transparency:
Model card summarizes the model's details
Accountability:
Model creators accountable for evaluation processes
Mitigation Strategies:
Continuous monitoring and exploration of de-biasing techniques
Input Output 
Input Format:
Text string
Accepted Modalities:
text
Output Format:
Generated English-language text
Performance Tips:
Use bfloat16 or float16 for better performance on compatible hardware.
LLM NameGoogle Gemma 2 9B It
Repository 🤗https://huggingface.co/SillyTilly/google-gemma-2-9b-it 
Model Size9b
Required VRAM18.6 GB
Updated2025-10-16
MaintainerSillyTilly
Model Typegemma2
Model Files  4.9 GB: 1-of-4   5.0 GB: 2-of-4   5.0 GB: 3-of-4   3.7 GB: 4-of-4
Model ArchitectureGemma2ForCausalLM
Licensegemma
Context Length8192
Model Max Length8192
Transformers Version4.42.0.dev0
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typebfloat16

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

Rank the Google Gemma 2 9B It Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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