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Gemma 2 Baku 2B by rinna

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Gemma 2 Baku 2B is an open-source language model by rinna. Features: 2b LLM, VRAM: 10.5GB, Context: 8K, License: gemma, LLM Explorer Score: 0.16.

  Arxiv:2404.01657 Base model:finetune:google/gem...   Base model:google/gemma-2-2b   Dataset:cc100   Dataset:eleutherai/pile   Dataset:mc4 Dataset:oscar-corpus/colossal-...   Dataset:wikipedia   En   Gemma2   Ja   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/rinna/gemma-2-baku-2b 

Gemma 2 Baku 2B Benchmarks

LLME Score: 0.15695
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gemma 2 Baku 2B (rinna/gemma-2-baku-2b)
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Gemma 2 Baku 2B Parameters and Internals

Model Type 
text-generation
Supported Languages 
ja (advanced), en (intermediate)
Training Details 
Data Sources:
Japanese CC-100, Japanese C4, Japanese OSCAR, The Pile, Wikipedia, rinna curated Japanese dataset
Data Volume:
80B tokens
Model Architecture:
A 26-layer, 2304-hidden-size transformer-based language model
Input Output 
Accepted Modalities:
text
Output Format:
generated text
Performance Tips:
Recommended to use eager attention when conducting batch inference under bfloat16 precision. Gemma 2 yields NaN values with padding sequences when default attention mechanism is employed with bfloat16.
LLM NameGemma 2 Baku 2B
Repository 🤗https://huggingface.co/rinna/gemma-2-baku-2b 
Base Model(s)  Gemma 2 2B   google/gemma-2-2b
Model Size2b
Required VRAM10.5 GB
Updated2026-04-26
Maintainerrinna
Model Typegemma2
Model Files  5.0 GB: 1-of-3   5.0 GB: 2-of-3   0.5 GB: 3-of-3
Supported Languagesja en
Model ArchitectureAutoModelForCausalLM
Licensegemma
Context Length8192
Model Max Length8192
Transformers Version4.42.4
Tokenizer ClassGemmaTokenizer
Padding Token<pad>
Vocabulary Size256000
Torch Data Typefloat32

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Note: green Score (e.g. "73.2") means that the model is better than rinna/gemma-2-baku-2b.

Rank the Gemma 2 Baku 2B Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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