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Gemma 1.1 7B It by google

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  16633   
  Arxiv:1705.03551   Arxiv:1804.06876   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:2107.03374   Arxiv:2108.07732   Arxiv:2110.08193   Arxiv:2110.14168   Arxiv:2206.04615   Arxiv:2304.06364   Arxiv:2312.11805   Autotrain compatible   Conversational   Endpoints compatible   Gemma   Region:us   Safetensors   Sharded   Tensorflow

Gemma 1.1 7B It Benchmarks

LMSys ELO: 1084 vs 1272 (so35)-14.8%
MMLU Pro: 17.6
GPQA: 5.82
MUSR: 11.51
BBH: 15.93
IFEval: 50.39 vs 88 (so35)-42.7%
ARC: 60.07 vs 96.7 (so35)-37.9%
HellaSwag: 76.14 vs 95.3 (gpt4)-20.1%
MMLU: 60.92 vs 88.3 (so35)-31%
TruthfulQA: 50.74 vs 59 (gpt4)-14%
WinoGrande: 69.69 vs 87.5 (gpt4)-20.4%
GSM8K: 42.99 vs 96.4 (so35)-55.4%
MATH Lvl 5: 3.63
LLME Score: 0.44907
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

Gemma 1.1 7B It 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
Primary Use Cases:
NLP Research, Language Learning Tools, Knowledge Exploration
Limitations:
Factual Accuracy, Common Sense
Considerations:
Developers are encouraged to exercise caution and implement appropriate content safety safeguards.
Additional Notes 
Training hardware and Tensor Processing Units (TPUs) highlighted along with sustainability focus.
Supported Languages 
English (primary language)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
6 trillion tokens
Methodology:
novel RLHF method
Hardware Used:
TPUs
Safety Evaluation 
Methodologies:
structured evaluations, internal red-teaming
Risk Categories:
Text-to-Text Content Safety, Text-to-Text Representational Harms, Memorization, Large-scale harm
Ethical Considerations:
Bias and Fairness, Misinformation and Misuse, Transparency and Accountability
Responsible Ai Considerations 
Fairness:
Models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported.
Transparency:
Model card provides architectural, capabilities, limitations, and evaluation details.
Accountability:
Google is leading the model development, dissemination, and documenting processes.
Mitigation Strategies:
Continuous monitoring, content safety safeguards, developer and end-user education.
Input Output 
Input Format:
Text string input like a question or document for summarization
Accepted Modalities:
text
Output Format:
Generated English-language text
Performance Tips:
Longer context generally enhances model performance.
Release Notes 
Version:
Gemma 1.1 7B (IT)
Notes:
Improved model interaction, upgraded conversational capabilities, bug fixes.
LLM NameGemma 1.1 7B It
Repository 🤗https://huggingface.co/google/gemma-1.1-7b-it 
Model Size7b
Required VRAM17.1 GB
Updated2024-11-21
Maintainergoogle
Model Typegemma
Model Files  5.0 GB: 1-of-4   5.0 GB: 2-of-4   5.0 GB: 3-of-4   2.1 GB: 4-of-4
Model ArchitectureGemmaForCausalLM
Licensegemma
Context Length8192
Model Max Length8192
Transformers Version4.38.1
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typebfloat16
Gemma 1.1 7B It (google/gemma-1.1-7b-it)

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Rank the Gemma 1.1 7B It 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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Release v20241110
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