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+ Notices
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+
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+ Qwen/Qwen2.5-32B
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+ Source: https://huggingface.co/Qwen/Qwen2.5-32B
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+ License
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+ ```
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+ Apache License
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+ Version 2.0, January 2004
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+ http://www.apache.org/licenses/
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+
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+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+
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+ 1. Definitions.
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+
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+ "License" shall mean the terms and conditions for use, reproduction,
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+ and distribution as defined by Sections 1 through 9 of this document.
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+ "Licensor" shall mean the copyright owner or entity authorized by
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+ "control" means (i) the power, direct or indirect, to cause the
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+ direction or management of such entity, whether by contract or
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+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
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+ outstanding shares, or (iii) beneficial ownership of such entity.
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+
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+ "You" (or "Your") shall mean an individual or Legal Entity
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+ exercising permissions granted by this License.
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+
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+ "Source" form shall mean the preferred form for making modifications,
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+ source, and configuration files.
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+
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+ "Object" form shall mean any form resulting from mechanical
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+ transformation or translation of a Source form, including but
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+ not limited to compiled object code, generated documentation,
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+ and conversions to other media types.
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+
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+ of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
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+ PARTICULAR PURPOSE. You are solely responsible for determining the
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+ appropriateness of using or redistributing the Work and assume any
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+ risks associated with Your exercise of permissions under this License.
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+
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+ 8. Limitation of Liability. In no event and under no legal theory,
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+ whether in tort (including negligence), contract, or otherwise,
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+ unless required by applicable law (such as deliberate and grossly
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+ negligent acts) or agreed to in writing, shall any Contributor be
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+ liable to You for damages, including any direct, indirect, special,
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+ result of this License or out of the use or inability to use the
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+ Work (including but not limited to damages for loss of goodwill,
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+ work stoppage, computer failure or malfunction, or any and all
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+ other commercial damages or losses), even if such Contributor
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+
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+ 9. Accepting Warranty or Additional Liability. While redistributing
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+ on Your own behalf and on Your sole responsibility, not on behalf
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+ defend, and hold each Contributor harmless for any liability
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+ incurred by, or claims asserted against, such Contributor by reason
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+ END OF TERMS AND CONDITIONS
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+ APPENDIX: How to apply the Apache License to your work.
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+ ```
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+ Qwen/Qwen2.5-32B-Instruct
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+ Source: https://huggingface.co/Qwen/Qwen2.5-32B-Instruct
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+ License
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+ ```
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+ Apache License
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+ Version 2.0, January 2004
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+ http://www.apache.org/licenses/
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+
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+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+
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+ 1. Definitions.
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+
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+ "License" shall mean the terms and conditions for use, reproduction,
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+ and distribution as defined by Sections 1 through 9 of this document.
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+
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+ "Licensor" shall mean the copyright owner or entity authorized by
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+ the copyright owner that is granting the License.
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+
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+ "Legal Entity" shall mean the union of the acting entity and all
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+ control with that entity. For the purposes of this definition,
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+ "control" means (i) the power, direct or indirect, to cause the
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+ direction or management of such entity, whether by contract or
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+ otherwise, or (ii) ownership of fifty percent (50%) or more of the
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+ outstanding shares, or (iii) beneficial ownership of such entity.
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+
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+ "You" (or "Your") shall mean an individual or Legal Entity
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+ exercising permissions granted by this License.
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+ "Source" form shall mean the preferred form for making modifications,
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+ including but not limited to software source code, documentation
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+ source, and configuration files.
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+
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+ "Object" form shall mean any form resulting from mechanical
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+ transformation or translation of a Source form, including but
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+ not limited to compiled object code, generated documentation,
246
+ and conversions to other media types.
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+
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+ "Work" shall mean the work of authorship, whether in Source or
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README.md CHANGED
@@ -1,3 +1,159 @@
1
- ---
2
- license: apache-2.0
3
- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
3
+ license: apache-2.0
4
+ language:
5
+ - ja
6
+ - en
7
+ tags:
8
+ - qwen2
9
+ - conversational
10
+ - awq
11
+ base_model: rinna/qwen2.5-bakeneko-32b-instruct
12
+ base_model_relation: quantized
13
+ pipeline_tag: text-generation
14
+ library_name: transformers
15
+ ---
16
+
17
+ # `Qwen2.5 Bakeneko 32B Instruct AWQ (rinna/qwen2.5-bakeneko-32b-instruct-awq)`
18
+
19
+ ![rinna-icon](./rinna.png)
20
+
21
+ # Overview
22
+
23
+ This model is a 4-bit quantized model for [rinna/qwen2.5-bakeneko-32b-instruct](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct) using [AutoAWQ](https://github.com/casper-hansen/AutoAWQ). The quantized version is 4x smaller than the original model and thus requires less memory and provides faster inference.
24
+
25
+ | Size | Continual Pre-Training | Instruction Tuning | DeepSeek-R1 Distillation
26
+ | :- | :- | :- | :-
27
+ | 32B | Qwen2.5 Bakeneko 32B [[HF]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b) | Qwen2.5 Bakeneko 32B Instruct [[HF]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct)[[AWQ]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-awq)[[GGUF]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-gguf)[[GPTQ int8]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-gptq-int8)[[GPTQ int4]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-gptq-int4)| DeepSeek R1 Distill Qwen2.5 Bakeneko 32B [[HF]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b)[[AWQ]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b-awq)[[GGUF]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b-gguf)[[GPTQ int8]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b-gptq-int8)[[GPTQ int4]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b-gptq-int4)
28
+
29
+ See [rinna/qwen2.5-bakeneko-32b-instruct](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct) for details about model architecture and data.
30
+
31
+ * **Contributors**
32
+ - [Toshiaki Wakatsuki](https://huggingface.co/t-w)
33
+ - [Xinqi Chen](https://huggingface.co/Keely0419)
34
+ - [Kei Sawada](https://huggingface.co/keisawada)
35
+
36
+ ---
37
+
38
+ # Benchmarking
39
+
40
+ Please refer to [rinna's LM benchmark page](https://rinnakk.github.io/research/benchmarks/lm/index.html).
41
+
42
+ ---
43
+
44
+ # How to use the model
45
+
46
+ ~~~python
47
+ import torch
48
+ from transformers import AutoTokenizer, AutoModelForCausalLM
49
+
50
+ model_id = "rinna/qwen2.5-bakeneko-32b-instruct-awq"
51
+
52
+ tokenizer = AutoTokenizer.from_pretrained(model_id)
53
+ model = AutoModelForCausalLM.from_pretrained(
54
+ model_id,
55
+ device_map="auto",
56
+ torch_dtype=torch.bfloat16,
57
+ )
58
+
59
+ messages = [
60
+ {"role": "system", "content": "あなたは誠実で優秀な日本人のアシスタントです。"},
61
+ {"role": "user", "content": "ゲーム・小説・アニメに登場するアイテムボックスの特徴と、その原理を詳細に推測してください。"},
62
+ ]
63
+ prompt = tokenizer.apply_chat_template(
64
+ messages,
65
+ tokenize=False,
66
+ add_generation_prompt=True
67
+ )
68
+ input_ids = tokenizer.encode(
69
+ prompt,
70
+ add_special_tokens=False,
71
+ return_tensors="pt"
72
+ ).to(model.device)
73
+
74
+ outputs = model.generate(
75
+ input_ids,
76
+ max_new_tokens=512,
77
+ do_sample=True,
78
+ temperature=0.7,
79
+ top_k=20,
80
+ top_p=0.8,
81
+ repetition_penalty=1.05,
82
+ )
83
+
84
+ response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
85
+ print(response)
86
+ ~~~
87
+
88
+ ---
89
+
90
+ # Tokenization
91
+ The model uses the original [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) tokenizer.
92
+
93
+ ---
94
+
95
+ # How to cite
96
+ ```bibtex
97
+ @misc{rinna-qwen2.5-bakeneko-32b-instruct-awq,
98
+ title = {rinna/qwen2.5-bakeneko-32b-instruct-awq},
99
+ author = {Wakatsuki, Toshiaki and Chen, Xinqi and Sawada, Kei},
100
+ url = {https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-awq}
101
+ }
102
+
103
+ @inproceedings{sawada2024release,
104
+ title = {Release of Pre-Trained Models for the {J}apanese Language},
105
+ author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
106
+ booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
107
+ month = {5},
108
+ year = {2024},
109
+ pages = {13898--13905},
110
+ url = {https://aclanthology.org/2024.lrec-main.1213},
111
+ note = {\url{https://arxiv.org/abs/2404.01657}}
112
+ }
113
+ ```
114
+ ---
115
+
116
+ # References
117
+ ```bibtex
118
+ @misc{qwen2.5,
119
+ title = {Qwen2.5: A Party of Foundation Models},
120
+ url = {https://qwenlm.github.io/blog/qwen2.5/},
121
+ author = {Qwen Team},
122
+ month = {September},
123
+ year = {2024}
124
+ }
125
+
126
+ @article{qwen2,
127
+ title = {Qwen2 Technical Report},
128
+ author = {An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
129
+ journal = {arXiv preprint arXiv:2407.10671},
130
+ year = {2024}
131
+ }
132
+
133
+ @article{huang2023chat,
134
+ title = {Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages},
135
+ author = {Huang, Shih-Cheng and Li, Pin-Zu and Hsu, Yu-Chi and Chen, Kuang-Ming and Lin, Yu Tung and Hsiao, Shih-Kai and Tzong-Han Tsai, Richard and Lee, Hung-yi},
136
+ year = {2023},
137
+ url = {https://arxiv.org/abs/2310.04799}
138
+ }
139
+
140
+ @article{meng2024simpo,
141
+ title = {SimPO: Simple Preference Optimization with a Reference-Free Reward},
142
+ author = {Meng, Yu and Xia, Mengzhou and Chen, Danqi},
143
+ journal = {arXiv preprint arXiv:2405.14734},
144
+ year = {2024}
145
+ }
146
+
147
+ @inproceedings{lin2024awq,
148
+ title = {AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration},
149
+ author = {Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Chen, Wei-Ming and Wang, Wei-Chen and Xiao, Guangxuan and Dang, Xingyu and Gan, Chuang and Han, Song},
150
+ booktitle = {Proceedings of Machine Learning and Systems},
151
+ pages = {87--100},
152
+ volume = {6},
153
+ year = {2024}
154
+ }
155
+ ```
156
+ ---
157
+
158
+ # License
159
+ [The Apache License, Version 2.0](https://opensource.org/license/apache-2-0)
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