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Recurrentgemma 9B It by alpindale

 »  All LLMs  »  alpindale  »  Recurrentgemma 9B It   URL Share it on

  145   
  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   Conversational   Endpoints compatible   Recurrent gemma   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/alpindale/recurrentgemma-9b-it 

Recurrentgemma 9B It Benchmarks

LLME Score: 0.16027
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Recurrentgemma 9B It (alpindale/recurrentgemma-9b-it)
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Recurrentgemma 9B It Parameters and Internals

Model Type 
text generation
Use Cases 
Applications:
Content creation and communication, Research and education
Primary Use Cases:
Text generation, Chatbots and conversational AI, Text summarization
Limitations:
Influenced by quality and diversity of training data, Challenging with open-ended or highly complex tasks, May misinterpret language nuance, Potential factual inaccuracy
Considerations:
Consider context length and complexity of tasks.
Training Details 
Hardware Used:
TPUv5e
Model Architecture:
Recurrent architecture
Safety Evaluation 
Methodologies:
structured evaluations, red-teaming
Risk Categories:
Text-to-text content safety, Text-to-text representational harms, Memorization, Large-scale harm
Responsible Ai Considerations 
Fairness:
The model underwent input data pre-processing and posterior evaluations for bias and fairness.
Transparency:
This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes.
Mitigation Strategies:
Continuous monitoring, content safety guidelines, privacy-preserving techniques.
Input Output 
Input Format:
Text string (e.g., question, prompt, document to summarize)
Accepted Modalities:
text
Output Format:
Generated English-language text
Performance Tips:
Provide longer context to improve performance.
LLM NameRecurrentgemma 9B It
Repository 🤗https://huggingface.co/alpindale/recurrentgemma-9b-it 
Model Size9b
Required VRAM19.3 GB
Updated2025-12-08
Maintaineralpindale
Model Typerecurrent_gemma
Model Files  5.0 GB: 1-of-4   5.0 GB: 2-of-4   4.9 GB: 3-of-4   4.4 GB: 4-of-4
Model ArchitectureRecurrentGemmaForCausalLM
Licensegemma
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 alpindale/recurrentgemma-9b-it.

Rank the Recurrentgemma 9B 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 v20241124
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