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Ming-flash-omni-Preview init

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  1. .gitattributes +64 -0
  2. am.mvn +8 -0
  3. byt5/byt5.json +18 -0
  4. byt5/byt5_mapper/byt5_mapper.pt +3 -0
  5. byt5/byt5_model/base.pt +3 -0
  6. byt5/byt5_model/byt5_model.pt +3 -0
  7. byt5/color_idx.json +1 -0
  8. byt5/font_uni_10-lang_idx.json +1 -0
  9. byt5/google__byt5-smal/.gitattributes +17 -0
  10. byt5/google__byt5-smal/README.md +158 -0
  11. byt5/google__byt5-smal/config.json +28 -0
  12. byt5/google__byt5-smal/flax_model.msgpack +3 -0
  13. byt5/google__byt5-smal/generation_config.json +7 -0
  14. byt5/google__byt5-smal/pytorch_model.bin +3 -0
  15. byt5/google__byt5-smal/special_tokens_map.json +1 -0
  16. byt5/google__byt5-smal/tf_model.h5 +3 -0
  17. byt5/google__byt5-smal/tokenizer_config.json +1 -0
  18. config.json +290 -0
  19. connector/config.json +28 -0
  20. connector/generation_config.json +14 -0
  21. connector/model-00001-of-00002.safetensors +3 -0
  22. connector/model-00002-of-00002.safetensors +3 -0
  23. connector/model.safetensors.index.json +345 -0
  24. mlp/config.json +11 -0
  25. mlp/model.safetensors +3 -0
  26. model-00001-of-00042.safetensors +3 -0
  27. model-00002-of-00042.safetensors +3 -0
  28. model-00003-of-00042.safetensors +3 -0
  29. model-00004-of-00042.safetensors +3 -0
  30. model-00005-of-00042.safetensors +3 -0
  31. model-00006-of-00042.safetensors +3 -0
  32. model-00007-of-00042.safetensors +3 -0
  33. model-00008-of-00042.safetensors +3 -0
  34. model-00009-of-00042.safetensors +3 -0
  35. model-00010-of-00042.safetensors +3 -0
  36. model-00011-of-00042.safetensors +3 -0
  37. model-00012-of-00042.safetensors +3 -0
  38. model-00013-of-00042.safetensors +3 -0
  39. model-00014-of-00042.safetensors +3 -0
  40. model-00015-of-00042.safetensors +3 -0
  41. model-00016-of-00042.safetensors +3 -0
  42. model-00017-of-00042.safetensors +3 -0
  43. model-00018-of-00042.safetensors +3 -0
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  48. model-00023-of-00042.safetensors +3 -0
  49. model-00024-of-00042.safetensors +3 -0
  50. model-00025-of-00042.safetensors +3 -0
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+ - sq
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+ - sr
86
+ - st
87
+ - su
88
+ - sv
89
+ - sw
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+ - ta
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+ - te
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+ - tg
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+ - th
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+ - tr
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+ - uk
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+ - und
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+ - ur
98
+ - uz
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+ - vi
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+ - xh
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+ - yi
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+ - yo
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+ - zh
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+ - zu
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+ datasets:
106
+ - mc4
107
+
108
+ license: apache-2.0
109
+ ---
110
+
111
+ # ByT5 - Small
112
+
113
+ ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-small).
114
+
115
+ ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
116
+
117
+ ByT5 works especially well on noisy text data,*e.g.*, `google/byt5-small` significantly outperforms [mt5-small](https://huggingface.co/google/mt5-small) on [TweetQA](https://arxiv.org/abs/1907.06292).
118
+
119
+ Paper: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626)
120
+
121
+ Authors: *Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel*
122
+
123
+ ## Example Inference
124
+
125
+ ByT5 works on raw UTF-8 bytes and can be used without a tokenizer:
126
+
127
+ ```python
128
+ from transformers import T5ForConditionalGeneration
129
+ import torch
130
+
131
+ model = T5ForConditionalGeneration.from_pretrained('google/byt5-small')
132
+
133
+ input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + 3 # add 3 for special tokens
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+ labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + 3 # add 3 for special tokens
135
+
136
+ loss = model(input_ids, labels=labels).loss # forward pass
137
+ ```
138
+
139
+ For batched inference & training it is however recommended using a tokenizer class for padding:
140
+
141
+ ```python
142
+ from transformers import T5ForConditionalGeneration, AutoTokenizer
143
+
144
+ model = T5ForConditionalGeneration.from_pretrained('google/byt5-small')
145
+ tokenizer = AutoTokenizer.from_pretrained('google/byt5-small')
146
+
147
+ model_inputs = tokenizer(["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt")
148
+ labels = tokenizer(["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt").input_ids
149
+
150
+ loss = model(**model_inputs, labels=labels).loss # forward pass
151
+ ```
152
+
153
+ ## Abstract
154
+
155
+ Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments.
156
+
157
+ ![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/ByT5.png)
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+
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