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Recurrentgemma 9B It Bnb 4bit by theo77186

 »  All LLMs  »  theo77186  »  Recurrentgemma 9B It Bnb 4bit   URL Share it on

  10   
  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   4-bit   4bit   Autotrain compatible Base model:google/recurrentgem... Base model:quantized:google/re...   Bitsandbytes   Conversational   Endpoints compatible   Quantized   Recurrent gemma   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/theo77186/recurrentgemma-9b-it-bnb-4bit 

Recurrentgemma 9B It Bnb 4bit Benchmarks

LLME Score: 0.19664
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 Bnb 4bit (theo77186/recurrentgemma-9b-it-bnb-4bit)

Recurrentgemma 9B It Bnb 4bit Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
Research, Commercial applications
Applications:
Text generation, Chatbots, Text summarization
Limitations:
Bias in responses, Complex task handling limitations, Nuance handling
Considerations:
Use case-specific risk assessment guides
Additional Notes 
Recurrent architecture provides benefits over prior models like Gemma in both speed and memory efficiency.
Training Details 
Hardware Used:
TPUv5e
Model Architecture:
Recurrent Architecture
Safety Evaluation 
Methodologies:
Red-teaming, Human evaluation, Automated evaluation
Findings:
Acceptable for internal policies
Risk Categories:
Bias, Memorization, Large-scale harm
Responsible Ai Considerations 
Fairness:
Pre-processing evaluation and reporting
Transparency:
Detailed model card
Accountability:
Google
Mitigation Strategies:
Monitoring and debiasing techniques
Input Output 
Input Format:
Text
Accepted Modalities:
Text
Output Format:
Generated Text
Performance Tips:
Use in batches and provide sufficient context for best performance
LLM NameRecurrentgemma 9B It Bnb 4bit
Repository 🤗https://huggingface.co/theo77186/recurrentgemma-9b-it-bnb-4bit 
Base Model(s)  Recurrentgemma 9B It   google/recurrentgemma-9b-it
Model Size9b
Required VRAM6.4 GB
Updated2025-02-05
Maintainertheo77186
Model Typerecurrent_gemma
Model Files  5.0 GB: 1-of-2   1.4 GB: 2-of-2
Quantization Type4bit
Model ArchitectureRecurrentGemmaForCausalLM
Licensegemma
Transformers Version4.41.2
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than theo77186/recurrentgemma-9b-it-bnb-4bit.

Rank the Recurrentgemma 9B It Bnb 4bit 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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