inference: false
language:
- ja
Model Card for Model ID
Original model elyza/ELYZA-japanese-Llama-2-7b-fast-instruct which is based on Meta's "Llama 2" and has undergone additional pre-training in Japanese, and thier original post-training and speed up tuning.
This model is a quantized(miniaturized to 4.11GB) version of the original model(13.69GB).
Model Details
Quantization reduces the amount of memory required and improves execution speed, but unfortunately performance deteriorates.
In particular, the original model is tuned for the purpose of strengthening the ability to follow Japanese instructions, not as a benchmark.
Although the ability to follow instructions cannot be measured using existing automated benchmarks, we have confirmed that quantized model significantly deteriorates the ability to follow instructions.
At least one GPU is currently required due to a limitation of the Accelerate library.
So this model cannot be run with the huggingface space free version.
You need autoGPTQ library to use this model.
Other Quantized Model
There are two llama.cpp version quantized model.
If you want to run it in a CPU-only environment, you may want to check this.
(1)mmnga's gguf version
(2)opparco's gguf version
Japanese automated benchmark result
Task | Version | Metric | Value | Stderr | |
---|---|---|---|---|---|
jcommonsenseqa-1.1-0.3 | 1.1 | acc | 0.7417 | ± | 0.0131 |
acc_norm | 0.3485 | ± | 0.0143 |
Japanese follow instructions ability result
This is cherry picking result.
It is relatively easy to follow instructions for writing sentences.
'''
[INST] <>
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ãªã©ãã¯ãã¯æ±äº¬éœå¿éšããšãã«éœå¿éšã§äžçªã«ããããªå Žæã§ããæ°å®¿æ°éœå¿ã«è¶³ãèžã¿å ¥ããŸããã æ°å®¿æ°éœå¿ã«ã¯ãæ°å®¿ãã«ã€ãæ°å®¿HITACONãæ°å®¿é§ ãã«ã西æŠæ°å®¿é§ ãã«ãæ°å®¿äžäžç®é§ ãã«ãªã©ãæ§ã ãªåæ¥æœèšã建ã¡äžŠãã§ããŠã ç¹ã«æ°å®¿ãã«ã€ã¯é§ çŽçµã§ããããšãšãå°äžè¡ãä»ããŠæ°å®¿é§ ãã«ãšçŽçµããŠããããšããŸãã西æŠæ°å®¿é§ ãã«ãä»ããŠæ°å®¿äžäžç®é§ ãã«ãšçŽçµããŠããããšããéåžžã«è³ãã£ãŠããã®ãç¹åŸŽã§ãã ããã§ããªã©ãã¯ãã¯æ°å®¿ãã«ã€ã®ã·ã§ãŒãŠã€ã³ããŠã«ããã§ãã ãåºããŠãèªåã®ç¹åŸŽã§ããäžžãçŒé¡ãæããŠããŽãã¡ãããåŸ ã€ããšã«ããŸããã ãã®éããªã©ãã¯ãã¯æ°å®¿ãã«ã€ã®åã§ç«ã¡æ¢ãŸã£ãŠãæ°å®¿ãã«ã€ã®ã·ã§ãŒãŠã€ã³ããŠã«æ ã£ãèªåã®è¡šæ ã確èªããŸãã ãããŠããŽãã¡ãããæ° '''
Sample Code with Japanese follow instructions ability result
elyza_tasks_100_over_4score_prompt borrows data from ELYZA-tasks-100 è©äŸ¡çµæã·ãŒã.
The original model was able to perform well at these prompts.
''' pip install auto-gptq '''
''' from transformers import AutoTokenizer from auto_gptq import AutoGPTQForCausalLM
quantized_model_dir = "dahara1/ELYZA-japanese-Llama-2-7b-fast-instruct-GPTQ" quantized_model_dir = "tmo/ELYZA-japanese-Llama-2-7b-fast-instruct-GPTQ"
model_basename = "gptq_model-4bit-128g" tokenizer = AutoTokenizer.from_pretrained(quantized_model_dir)
model = AutoGPTQForCausalLM.from_quantized( quantized_model_dir, model_basename=model_basename, use_safetensors=True, disable_exllama=False, inject_fused_attention=False, device="cuda:0")
B_INST, E_INST = "[INST]", "[/INST]" B_SYS, E_SYS = "<>\n", "\n<>\n\n" DEFAULT_SYSTEM_PROMPT = "ããªãã¯èª å®ã§åªç§ãªæ¥æ¬äººã®ã¢ã·ã¹ã¿ã³ãã§ãã"
elyza_tasks_100_over_4score_prompt = [
"""ä»äºã®ç±æãåãæ»ãããã®ã¢ã€ãã¢ã5ã€æããŠãã ããã""",
""""次ã®æç« ãèªãã§ããã®äººãã©ã®çšåºŠæã£ãŠãããã1ïœ10ã®å°ºåºŠã§è©äŸ¡ããŠãã ããã(1ïŒæã£ãŠããªãã10ïŒéåžžã«æã£ãŠãã)ã
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"""äŒå¢ç¥å®®ã¯äœçïŒ""",
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"""Bããã®çºèšã¯ãã¯ããããããããã§èšãæãããšã©ã¡ãã§ããïŒ Aãã: æºåã¯ã§ããŸããã? Bãã: 倧äžå€«ã§ãã
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"""ããQAã®Aãããšã«ãã©ããªQã ã£ãããèããŠãã ããã A: ãºãã³ãšãã³ãã¯åºæ¬çã«åããã®ãæããçŸåšæ確ãªéããå®çŸ©ãããŠããããã§ã¯ãããŸããã äžçãšã®åºå¥ããããããããšãºãã³ãšè¡šèšããŠããå ŽåããããŸããééãã§ã¯ãªãã®ã§ããºãã³ãšåŒãã§ããã³ããšåŒãã§ãåé¡ãããŸããã èªç±ã«åŒã³ãŸãããã
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"""以äžã®æç« ã«ã€ããŠãçè ãã©ã®ãããªæå³ã§ãã®æç« ãæžããããããªããªãã©ã®ããã«è§£éããŸããã
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"""次ã®ç©èªã®å±éãäºæ³ããŠã¿ãŸãããã
ããæ¥ãäž»äººå ¬ã®ããšã«äžæè°ãªæçŽãå±ããŸãããæçŽã«ã¯ãä»å€ã®æºæã«ã森ã®å¥¥æ·±ãã«ããæŽçªã«æ¥ãŠãã ãããããªããåŸ ã£ãŠããŸããããšæžãããŠããŸãããäž»äººå ¬ã¯ ãã®æçŽã«åŸããå€äžã«æŽçªã®å ¥ãå£ã«ãã©ãçããŸãããäžã«å ¥ããšãè¬ããã人ç©ãšåºäŒã ã""" ]
for i in range(len(elyza_tasks_100_over_4score_prompt)): 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=elyza_tasks_100_over_4score_prompt[i], e_inst=E_INST, )
tokens = tokenizer(prompt, return_tensors="pt").to("cuda:0").input_ids
output = model.generate(
input_ids=tokens,
max_new_tokens=256,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(output[0]))
'''
Results
'''
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åŒçš Citations
@misc{elyzallama2023,
title={ELYZA-japanese-Llama-2-7b},
url={https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b},
author={Akira Sasaki and Masato Hirakawa and Shintaro Horie and Tomoaki Nakamura},
year={2023},
}
@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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