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Genji-JP 6B

Please check our blog post for more details, samples, evaluations and more: Blogpost

Model Description

Genji-JP 6B is a model finetuned on our Japanese storytelling dataset based on EleutherAI's GPT-J 6B model. This particular model is trained on Japanese web novels.

Hyperparameter Value
n_parameters 6,053,381,344
n_layers 28*
d_model 4,096
d_ff 16,384
n_heads 16
d_head 256
n_ctx 2,048
n_vocab 50,400 (same tokenizer as GPT-2/3)
position encoding Rotary position encodings (RoPE)
RoPE dimensions 64

* each layer consists of one feedforward block and one self attention block

The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model dimension is split into 16 heads, each with a dimension of 256. Rotary position encodings (RoPE) was applied to 64 dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as GPT-2/GPT-3.

Training data

GPT-J 6B was pretrained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model. After the pre-training, it's finetuned on our Japanese storytelling dataset. Check our blog post for more details.

How to use

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
model = AutoModelForCausalLM.from_pretrained("NovelAI/genji-jp", torch_dtype=torch.float16, low_cpu_mem_usage=True).eval().cuda()
text = '''ใ‚ใ‚‰ใ™ใ˜๏ผšใ‚ใชใŸใฏ็•ฐไธ–็•Œใซ่ปข็”Ÿใ—ใฆใ—ใพใ„ใพใ—ใŸใ€‚ๅ‹‡่€…ใจใชใฃใฆใ€ไปฒ้–“ใ‚’ไฝœใ‚Šใ€็•ฐไธ–็•Œใ‚’ๅ†’้™บใ—ใ‚ˆใ†๏ผ
***
่ปข็”Ÿใ™ใ‚‹ใจใ€ใ‚ใ‚‹่ƒฝๅŠ›ใ‚’ๆ‰‹ใซๅ…ฅใ‚Œใฆใ„ใŸใ€‚ใใ‚Œใฏใ€'''

tokens = tokenizer(text, return_tensors="pt").input_ids
generated_tokens = model.generate(tokens.long().cuda(), use_cache=True, do_sample=True, temperature=1, top_p=0.9, repetition_penalty=1.125, min_length=1, max_length=len(tokens[0]) + 400, pad_token_id=tokenizer.eos_token_id)
last_tokens = generated_tokens[0]
generated_text = tokenizer.decode(last_tokens).replace("๏ฟฝ", "")
print("Generation:\n" + generated_text)

When run, produces output like this:

Generation:
ใ‚ใ‚‰ใ™ใ˜๏ผšใ‚ใชใŸใฏ็•ฐไธ–็•Œใซ่ปข็”Ÿใ—ใฆใ—ใพใ„ใพใ—ใŸใ€‚ๅ‹‡่€…ใจใชใฃใฆใ€ไปฒ้–“ใ‚’ไฝœใ‚Šใ€็•ฐไธ–็•Œใ‚’ๅ†’้™บใ—ใ‚ˆใ†๏ผ
***
่ปข็”Ÿใ™ใ‚‹ใจใ€ใ‚ใ‚‹่ƒฝๅŠ›ใ‚’ๆ‰‹ใซๅ…ฅใ‚Œใฆใ„ใŸใ€‚ใใ‚Œใฏใ€ใ€Žไบˆ็Ÿฅใ€ใ ใ€‚้ŽๅŽปใ‹ใ‚‰ๆœชๆฅใฎใ“ใจใ‚’ใ€่ชฐใ‚‚็Ÿฅใ‚‰ใชใ„ๅ‡บๆฅไบ‹ใ‚‚ๅซใ‚ใฆ่ฆ‹้€šใ™ใ“ใจใŒๅ‡บๆฅใ‚‹ใ€‚
ๆ‚ช้ญ”ใฎๆฌ ็‰‡ใจๅ‘ผใฐใ‚Œใ‚‹ๅฐใ•ใช็ตๆ™ถใ‚’ๅ–ใ‚Š่พผใ‚“ใงใ€ไฝฟๅฝนใ™ใ‚‹ใ“ใจใŒๅ‡บๆฅใ‚‹ใ€‚ไบบใ‚’ๆƒนใใคใ‘ใ€ๅ •่ฝใ•ใ›ใ‚‹ใ€‚ไฝ•ใ‚ˆใ‚Šใ€ไฟบใฏ็”ทใชใ‚“ใฆๅฑ…ใชใ‹ใฃใŸใ—ใ€ๅฅณใซ่ˆˆๅ‘ณใ‚‚ใชใ„ใ€‚โ€ฆโ€ฆใใ‚“ใชใ‚ฏใ‚บใฎ็‰‡ๆฃ’ใ‚’ๆ‹…ใŽไธŠใ’ใ‚‹ๅฅดใŒๅคšใใชใ‚‹ใจๆ€ใ†ใจใ€ใกใ‚‡ใฃใจ่‹ฆใ—ใ„ใ€‚
ใ ใŒใ€ไธ€้ƒจใฎไบบ้–“ใซใฏๅ”ๅŠ›่€…ใ‚’ๅพ—ใ‚‹ใ“ใจใŒๅ‡บๆฅใ‚‹ใ€‚็›ฎ็ซ‹ใŸใชใ„่ก—ใซใ‚ใ‚‹ๅฏบใฎไธญใงใ€ๅธธใซๅฎถใซๅผ•ใใ“ใ‚‚ใฃใฆใ„ใ‚‹่€ไบบใ€‚ใใ‚“ใชใƒคใƒ„ใฎ้ญ‚ใ‚’ใ‚ณใƒณใƒˆใƒญใƒผใƒซใ™ใ‚‹ใ“ใจใŒๅ‡บๆฅใ‚‹ใฎใ ใ€‚ไพฟๅˆฉใช่ƒฝๅŠ›ใ ใ€‚ใ—ใ‹ใ—ใ€่ฃๅˆ‡ใ‚Š่€…ใฏๅคงๅ‹ขใ„ใ‚‹ใ€‚ๆฐ—ใ‚’ๆŠœใ‘ใฐใ€็‹‚ใ†ใ€‚ใ ใ‹ใ‚‰ๆณจๆ„ใŒๅฟ…่ฆใ ใ€‚
โ€•โ€•ใ€Œใ‚„ใฃใฆใ‚„ใ‚‹ใ‚ˆใ€
ใ€€ใ‚ขใƒผใƒญใƒณใฏไธๆ•ตใซ็ฌ‘ใฃใŸใ€‚ใ“ใฎ

Acknowledgements

This project was possible because of the compute provided by the TPU Research Cloud

Thanks EleutherAI for pretraining the GPT-J 6B model.

Thanks to everyone who contributed to this project!

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