RakutenAI 7B Chat by Rakuten

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  Arxiv:2403.15484   Autotrain compatible   Conversational   Endpoints compatible   Mistral   Pytorch   Region:us   Safetensors   Sharded   Tensorflow

RakutenAI 7B Chat Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
RakutenAI 7B Chat (Rakuten/RakutenAI-7B-chat)

RakutenAI 7B Chat Parameters and Internals

Model Type 
text generation, chatbot
Use Cases 
Limitations:
can produce biased, inaccurate, or unsafe outputs
Additional Notes 
Extended vocabulary from 32k to 48k for enhanced Japanese tokenization.
Supported Languages 
Japanese (high), English (competitive)
Training Details 
Data Sources:
JSNLI, RTE, KUCI, BELEBELE, JCS, JNLI, Dolly-15K, OpenAssistant1
Methodology:
fine-tuning for instruction-tuning and chat-tuning
Context Length:
4096
Model Architecture:
Mistral
Input Output 
Input Format:
custom chat template or plain text format
Accepted Modalities:
text
Output Format:
text
LLM NameRakutenAI 7B Chat
Repository ๐Ÿค—https://huggingface.co/Rakuten/RakutenAI-7B-chat 
Model Size7b
Required VRAM14.8 GB
Updated2025-02-04
MaintainerRakuten
Model Typemistral
Model Files  10.0 GB: 1-of-2   4.8 GB: 2-of-2   10.0 GB: 1-of-2   4.8 GB: 2-of-2
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.34.0
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size48000
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than Rakuten/RakutenAI-7B-chat.

Rank the RakutenAI 7B Chat 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