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Gemma 1.1 2B It by google

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  94636   
  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
Model Card on HF 🤗: https://huggingface.co/google/gemma-1.1-2b-it 

Gemma 1.1 2B It Benchmarks

LMSys ELO: 1021 vs 1272 (so35)-19.7%
MMLU Pro: 5.37
GPQA: 2.57
MUSR: 2.02
BBH: 5.86
IFEval: 30.67 vs 88 (so35)-65.1%
MATH Lvl 5: 0.15
LLME Score: 0.385
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 2B It (google/gemma-1.1-2b-it)

Gemma 1.1 2B It Parameters and Internals

Model Type 
text generation, text-to-text, large language model, decoder-only LL
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:
Model's bias due to training data, Challenges with open-ended tasks
Considerations:
Continuous monitoring and detailed guidelines.
Additional Notes 
Models are state-of-the-art open large language models designed for responsible AI development.
Supported Languages 
English (Fluent)
Training Details 
Data Sources:
Web Documents, Code, Mathematics
Data Volume:
6 trillion tokens
Methodology:
Reinforcement Learning with Human Feedback (RLHF)
Context Length:
2048
Training Time:
Unknown
Hardware Used:
TPUv5e
Model Architecture:
Text-to-text, decoder-only
Safety Evaluation 
Methodologies:
Red-teaming, Automated evaluations, Human evaluation
Findings:
Acceptable thresholds for internal policies, Results on well-known safety benchmarks
Risk Categories:
Child safety, Content safety, Representational harms, Memorization risks, Large-scale harms
Ethical Considerations:
Various safety benchmarks and internal evaluation methods applied.
Responsible Ai Considerations 
Fairness:
Careful pre-processing and evaluation to mitigate biases
Transparency:
Open model with summarized details on architecture and capabilities
Accountability:
Development of mechanisms and guidelines for content safety
Mitigation Strategies:
Techniques for de-biasing and content flagging guidelines
Input Output 
Input Format:
Text string
Accepted Modalities:
text
Output Format:
Generated text
Performance Tips:
Addition of appropriate content context can improve performance.
Release Notes 
Version:
1.1
Date:
Unknown
Notes:
Substantial gains with improved RLHF method and resolved multi-turn conversation bug.
LLM NameGemma 1.1 2B It
Repository 🤗https://huggingface.co/google/gemma-1.1-2b-it 
Model Size2b
Required VRAM5.1 GB
Updated2024-12-21
Maintainergoogle
Model Typegemma
Model Files  5.0 GB: 1-of-2   0.1 GB: 2-of-2
Model ArchitectureGemmaForCausalLM
Licensegemma
Context Length8192
Model Max Length8192
Transformers Version4.38.1
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typebfloat16

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Gemma 1.1 2B It AWQ1223 GB

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Rank the Gemma 1.1 2B 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 v20241217
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