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Recurrentgemma 2B It 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   Arxiv:2402.19427   Autotrain compatible   Conversational   Endpoints compatible   Recurrent gemma   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/google/recurrentgemma-2b-it 

Recurrentgemma 2B It Benchmarks

MMLU Pro: 4.47
GPQA: 0.45
MUSR: 3.62
BBH: 7.98
IFEval: 29.49 vs 88 (so35)-66.5%
ARC: 30.97 vs 96.7 (so35)-68%
HellaSwag: 56.26 vs 95.3 (gpt4)-41%
MMLU: 40.87 vs 88.3 (so35)-53.7%
TruthfulQA: 42.81 vs 59 (gpt4)-27.4%
WinoGrande: 64.17 vs 87.5 (gpt4)-26.7%
GSM8K: 10.08 vs 96.4 (so35)-89.5%
MATH Lvl 5: 1.66
LLME Score: 0.29346
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Recurrentgemma 2B It (google/recurrentgemma-2b-it)

Recurrentgemma 2B It Parameters and Internals

Model Type 
open language model, text generation, instruction-tuned
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:
Creative text generation, Conversational interfaces, Text summarization
Limitations:
Bias due to training data, Complex task difficulty, Ambiguities in language, Factual inaccuracies, Lacking common sense
Considerations:
Biases, task complexity, factual accuracy, and responsible use were considered.
Supported Languages 
English (Proficient)
Training Details 
Methodology:
Recurrent architecture
Hardware Used:
TPUv5e
Model Architecture:
Recurrent architecture
Safety Evaluation 
Methodologies:
Red-teaming, Human evaluation
Findings:
Safe within acceptable thresholds
Risk Categories:
Text-to-text content safety, Text-to-text representational harms, Memorization, Large-scale harm
Ethical Considerations:
Bias, misinformation, misuse, transparency, and accountability were considered.
Responsible Ai Considerations 
Fairness:
The model underwent careful scrutiny and is reported in the model card.
Transparency:
Details on architectures, capabilities, and limitations are shared.
Accountability:
Developers should execute responsibilities with internal policies and guidelines.
Mitigation Strategies:
De-biasing techniques, user guidelines, and privacy-preserving techniques were explored.
Input Output 
Input Format:
Text string
Accepted Modalities:
Text
Output Format:
Generated text
LLM NameRecurrentgemma 2B It
Repository 🤗https://huggingface.co/google/recurrentgemma-2b-it 
Model Size2b
Required VRAM5.4 GB
Updated2025-01-23
Maintainergoogle
Model Typerecurrent_gemma
Model Files  5.0 GB: 1-of-2   0.4 GB: 2-of-2
Model ArchitectureRecurrentGemmaForCausalLM
Licensegemma
Transformers Version4.40.0.dev0
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typebfloat16

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