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+ META LLAMA 3 COMMUNITY LICENSE AGREEMENT
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+ Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.
README.md CHANGED
@@ -1,3 +1,153 @@
1
- ---
2
- license: llama3
3
- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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+ thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
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+ license: llama3
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+ datasets:
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+ - CohereForAI/aya_dataset
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+ - kunishou/databricks-dolly-15k-ja
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+ - kunishou/HelpSteer-35k-ja
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+ - kunishou/HelpSteer2-20k-ja
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+ - kunishou/hh-rlhf-49k-ja
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+ - kunishou/oasst1-chat-44k-ja
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+ - kunishou/oasst2-chat-68k-ja
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+ - meta-math/MetaMathQA
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+ - OpenAssistant/oasst1
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+ - OpenAssistant/oasst2
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+ - sahil2801/CodeAlpaca-20k
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+ language:
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+ - ja
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+ - en
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+ tags:
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+ - llama
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+ - llama-3
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+ - gptq
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+ inference: false
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+ ---
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+
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+ # `Llama 3 Youko 8B Instruct GPTQ (rinna/llama-3-youko-8b-instruct-gptq)`
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+
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+ ![rinna-icon](./rinna.png)
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+
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+ # Overview
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+
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+ rinna/llama-3-youko-8b-instruct-gptq is the quantized model for [rinna/llama-3-youko-8b-instruct](https://huggingface.co/rinna/llama-3-youko-8b-instruct) using [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ). The quantized version is 4x smaller than the original model and thus requires less memory and provides faster inference.
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+
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+ | Size | Continual Pre-Training | Instruction-Tuning |
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+ | :- | :- | :- |
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+ | 8B | Llama 3 Youko 8B [[HF]](https://huggingface.co/rinna/llama-3-youko-8b) [[GPTQ]](https://huggingface.co/rinna/llama-3-youko-8b-gptq) | Llama 3 Youko 8B Instruct [[HF]](https://huggingface.co/rinna/llama-3-youko-8b-instruct) [[GPTQ]](https://huggingface.co/rinna/llama-3-youko-8b-instruct-gptq) |
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+ | 70B | Llama 3 Youko 70B [[HF]](https://huggingface.co/rinna/llama-3-youko-70b) [[GPTQ]](https://huggingface.co/rinna/llama-3-youko-70b-gptq) | Llama 3 Youko 70B Instruct [[HF]](https://huggingface.co/rinna/llama-3-youko-70b-instruct) [[GPTQ]](https://huggingface.co/rinna/llama-3-youko-70b-instruct-gptq) |
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+
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+ * **Training: Built with Meta Llama 3**
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+
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+ See [rinna/llama-3-youko-8b-instruct](https://huggingface.co/rinna/llama-3-youko-8b-instruct) for details about model architecture and data.
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+
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+
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+ * **Contributors**
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+
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+ - [Toshiaki Wakatsuki](https://huggingface.co/t-w)
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+ - [Xinqi Chen](https://huggingface.co/Keely0419)
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+ - [Koh Mitsuda](https://huggingface.co/mitsu-koh)
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+ - [Kei Sawada](https://huggingface.co/keisawada)
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+
51
+ ---
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+
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+ # Benchmarking
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+
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+ Please refer to [rinna's LM benchmark page](https://rinnakk.github.io/research/benchmarks/lm/index.html).
56
+
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+ ---
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+
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+ # How to use the model
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+
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+ We found this instruction-tuned model tends to generate repeated text more often than its base counterpart, and thus we set repetition_penalty=1.1 for better generation performance. The same repetition penalty was applied to the instruction-tuned model in the aforementioned evaluation experiments.
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+
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+ ~~~~python
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+ import torch
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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+ model_id = "rinna/llama-3-youko-8b-instruct-gptq"
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model_id)
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+ model = AutoModelForCausalLM.from_pretrained(
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+ model_id,
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+ device_map="auto",
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+ )
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+
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+ messages = [
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+ {"role": "system", "content": "あなたは誠実で優秀なアシスタントです。どうか、簡潔かつ正直に答えてください。"},
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+ {"role": "user", "content": "西田幾多郎とはどんな人物ですか?"},
78
+ ]
79
+
80
+ input_ids = tokenizer.apply_chat_template(
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+ messages,
82
+ add_generation_prompt=True,
83
+ return_tensors="pt"
84
+ ).to(model.device)
85
+
86
+ terminators = [
87
+ tokenizer.convert_tokens_to_ids("<|end_of_text|>"),
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+ tokenizer.convert_tokens_to_ids("<|eot_id|>")
89
+ ]
90
+
91
+ outputs = model.generate(
92
+ input_ids,
93
+ max_new_tokens=512,
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+ eos_token_id=terminators,
95
+ do_sample=True,
96
+ temperature=0.6,
97
+ top_p=0.9,
98
+ repetition_penalty=1.1,
99
+ )
100
+
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+ response = outputs[0][input_ids.shape[-1]:]
102
+ response = tokenizer.decode(response, skip_special_tokens=True)
103
+ print(response)
104
+ ~~~~
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+
106
+ ---
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+
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+ # Tokenization
109
+ The model uses the original [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) tokenizer.
110
+
111
+ ---
112
+
113
+ # How to cite
114
+ ```bibtex
115
+ @misc{rinna-llama-3-youko-8b-instruct-gptq,
116
+ title = {rinna/llama-3-youko-8b-instruct-gptq},
117
+ author = {Wakatsuki, Toshiaki and Chen, Xinqi and Mitsuda, Koh and Sawada, Kei},
118
+ url = {https://huggingface.co/rinna/llama-3-youko-8b-instruct-gptq}
119
+ }
120
+
121
+ @inproceedings{sawada2024release,
122
+ title = {Release of Pre-Trained Models for the {J}apanese Language},
123
+ author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
124
+ booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
125
+ month = {5},
126
+ year = {2024},
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+ pages = {13898--13905},
128
+ url = {https://aclanthology.org/2024.lrec-main.1213},
129
+ note = {\url{https://arxiv.org/abs/2404.01657}}
130
+ }
131
+ ```
132
+ ---
133
+
134
+ # References
135
+ ```bibtex
136
+ @article{llama3modelcard,
137
+ title = {Llama 3 Model Card},
138
+ author = {AI@Meta},
139
+ year = {2024},
140
+ url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
141
+ }
142
+
143
+ @article{frantar2022gptq,
144
+ title = {{GPTQ}: Accurate Post-training Compression for Generative Pretrained Transformers},
145
+ author = {Frantar, Elias and Ashkboos, Saleh and Hoefler, Torsten and Alistarh, Dan},
146
+ year = {2022},
147
+ url = {https://arxiv.org/abs/2210.17323}
148
+ }
149
+ ```
150
+ ---
151
+
152
+ # License
153
+ [Meta Llama 3 Community License](https://llama.meta.com/llama3/license/)
USE_POLICY.md ADDED
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1
+ # Meta Llama 3 Acceptable Use Policy
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+
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+ Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you
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+ access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of
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+ this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)
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+
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+ ## Prohibited Uses
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+
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+ We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow
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+ others to use, Meta Llama 3 to:
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+
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+ 1. Violate the law or others’ rights, including to:
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+ 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
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+ 1. Violence or terrorism
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+ 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
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+ 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
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+
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+ 3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following:
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+ 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
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+ 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
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+ 3. Generating, promoting, or further distributing spam
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+ 4. Impersonating another individual without consent, authorization, or legal right
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+ 5. Representing that the use of Meta Llama 3 or outputs are human-generated
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+ 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
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+ 4. Fail to appropriately disclose to end users any known dangers of your AI system
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+
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+ Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation
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+ of this Policy through one of the following means:
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+
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+ ● Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)
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+ ● Reporting risky content generated by the model:
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+ developers.facebook.com/llama_output_feedback
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+ ● Reporting bugs and security concerns: facebook.com/whitehat/info
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+ ● Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3:
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+ }
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+ },
2052
+ "bos_token": "<|begin_of_text|>",
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+ "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}",
2054
+ "clean_up_tokenization_spaces": true,
2055
+ "eos_token": "<|eot_id|>",
2056
+ "model_input_names": [
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2058
+ "attention_mask"
2059
+ ],
2060
+ "model_max_length": 1000000000000000019884624838656,
2061
+ "tokenizer_class": "PreTrainedTokenizerFast"
2062
+ }