Japanese GPT Neox 3.6B By rinna: Benchmarks, Features and Detailed Analysis. Insights on Japanese GPT Neox 3.6B.

Arxiv:2404.01657 Autotrain compatible Dataset:cc100 Dataset:mc4 Dataset:wikipedia Gpt neox Ja Lm Pytorch Region:us Safetensors

Japanese GPT Neox 3.6B Benchmarks

ARC: 25 vs 96.7 (so35)^-74.1%

HellaSwag: 25.46 vs 95.3 (gpt4)^-73.3%

MMLU: 23.12 vs 88.3 (so35)^-73.8%

TruthfulQA: 51.45 vs 59 (gpt4)^-12.8%

WinoGrande: 50.67 vs 87.5 (gpt4)^-42.1%

LLME Score: 0.21071

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

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Japanese GPT Neox 3.6B Parameters and Internals

LLM Name	Japanese GPT Neox 3.6B
Repository 🤗	https://huggingface.co/rinna/japanese-gpt-neox-3.6b
Model Size	3.6b
Required VRAM	7.4 GB
Updated	2024-09-07
Maintainer	rinna
Model Type	gpt_neox
Model Files	7.4 GB 7.4 GB
Supported Languages	ja
Model Architecture	GPTNeoXForCausalLM
License	mit
Context Length	2048
Model Max Length	2048
Tokenizer Class	T5Tokenizer
Padding Token	[PAD]
Vocabulary Size	32000
Torch Data Type	float16

Japanese GPT Neox 3.6B (rinna/japanese-gpt-neox-3.6b)

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Rank the Japanese GPT Neox 3.6B 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 v2024072803

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Japanese GPT Neox 3.6B by rinna

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Japanese GPT Neox 3.6B Benchmarks

Japanese GPT Neox 3.6B Parameters and Internals

Best Alternatives to Japanese GPT Neox 3.6B

Rank the Japanese GPT Neox 3.6B Capabilities

What open-source LLMs or SLMs are you in search of? 35693 in total.