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LLAMA 2 COMMUNITY LICENSE AGREEMENT
Llama 2 Version Release Date: July 18, 2023
"Agreement" means the terms and conditions for use, reproduction, distribution and
modification of the Llama Materials set forth herein.
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libraries/llama-downloads/.
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entering in this Agreement on their behalf.
"Llama 2" means the foundational large language models and software and
algorithms, including machine-learning model code, trained model weights,
inference-enabling code, training-enabling code, fine-tuning enabling code and other
elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and-
libraries/llama-downloads/.
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Documentation (and any portion thereof) made available under this Agreement.
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---
license: llama2
language:
- ja
- en
---
## ELYZA-japanese-CodeLlama-7b
![ELYZA-Japanese-CodeLlama](./key_visual.png)
### Model Description
**ELYZA-japanese-CodeLlama-7b** は、 [Code Llama](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)をベースとして日本語能力を拡張するために追加事前学習を行ったモデルです。
詳細は [Blog記事](https://zenn.dev/elyza/articles/fcbf103e0a05b1) を参照してください。
### Usage
````python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
DEFAULT_SYSTEM_PROMPT = "あなたは誠実で優秀な日本人のアシスタントです。"
text = "エラトステネスの篩についてサンプルコードを示し、解説してください。"
model_name = "elyza/ELYZA-japanese-CodeLlama-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto")
if torch.cuda.is_available():
model = model.to("cuda")
prompt = "{bos_token}{b_inst} {system}{prompt} {e_inst} ".format(
bos_token=tokenizer.bos_token,
b_inst=B_INST,
system=f"{B_SYS}{DEFAULT_SYSTEM_PROMPT}{E_SYS}",
prompt=text,
e_inst=E_INST,
)
with torch.no_grad():
token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
output_ids = model.generate(
token_ids.to(model.device),
max_new_tokens=768,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
)
output = tokenizer.decode(output_ids.tolist()[0][token_ids.size(1) :], skip_special_tokens=True)
print(output)
"""
エラトステネスの篩は、素数を探すアルゴリズムの一つです。
以下にそのサンプルコードを示します。
```python
def eratosthenes_sieve(n):
sieve = [True] * (n + 1)
sieve[0] = sieve[1] = False
for i in range(2, int(n ** 0.5) + 1):
if sieve[i]:
for j in range(i * i, n + 1, i):
sieve[j] = False
return [i for i in range(n + 1) if sieve[i]]
```
このコードは、エラトステネスの篩を用いて、n以下のすべての素数を求める関数です。
エラトステネスの篩は、以下のようなアルゴリズムで動作します。
1. 2以外のすべての数を素数として扱う
2. 2以外の数のうち、2の倍数をすべて除外する
3. 3以外の数のうち、3の倍数をすべて除外する
4. 5以外の数のうち、5の倍数をすべて除外する
5. 7以外の数のうち、7の倍数をすべて除外する
6. …
このアルゴリズムでは、2の倍数、3の倍数、5の倍数、7の倍数…というように、素数の倍数を除外していきます。
このアルゴリズムは、素数の倍数は必ず素数の倍数の倍数となるという性質を利用しているため、非常に効率的です。
"""
````
### ELYZA-japanese-CodeLlama-7b Models
| Model Name | Vocab Size | #Params |
|:---------------------------------------------|:----------:|:-------:|
|[elyza/ELYZA-japanese-CodeLlama-7b](https://huggingface.co/elyza/ELYZA-japanese-CodeLlama-7b)| 32016 | 6.27B |
|[elyza/ELYZA-japanese-CodeLlama-7b-instruct](https://huggingface.co/elyza/ELYZA-japanese-CodeLlama-7b-instruct)| 32016 | 6.27B |
### Developers
以下アルファベット順
- [Akira Sasaki](https://huggingface.co/akirasasaki)
- [Masato Hirakawa](https://huggingface.co/m-hirakawa)
- [Shintaro Horie](https://huggingface.co/e-mon)
- [Tomoaki Nakamura](https://huggingface.co/tyoyo)
### Licence
Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
### How to Cite
```tex
@misc{elyzacodellama2023,
title={ELYZA-japanese-CodeLlama-7b},
url={https://huggingface.co/elyza/ELYZA-japanese-CodeLlama-7b},
author={Akira Sasaki and Masato Hirakawa and Shintaro Horie and Tomoaki Nakamura},
year={2023},
}
```
### Citations
```tex
@misc{rozière2023code,
title={Code Llama: Open Foundation Models for Code},
author={Baptiste Rozière and Jonas Gehring and Fabian Gloeckle and Sten Sootla and Itai Gat and Xiaoqing Ellen Tan and Yossi Adi and Jingyu Liu and Tal Remez and Jérémy Rapin and Artyom Kozhevnikov and Ivan Evtimov and Joanna Bitton and Manish Bhatt and Cristian Canton Ferrer and Aaron Grattafiori and Wenhan Xiong and Alexandre Défossez and Jade Copet and Faisal Azhar and Hugo Touvron and Louis Martin and Nicolas Usunier and Thomas Scialom and Gabriel Synnaeve},
year={2023},
eprint={2308.12950},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{touvron2023llama,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
year={2023},
eprint={2307.09288},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```

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