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wikeeyang/UniWorld-V1-NF4

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Files changed (49) hide show
  1. .gitattributes +0 -2
  2. models/UniWorld-V1/.gitattributes +0 -36
  3. models/UniWorld-V1/added_tokens.json +0 -24
  4. models/UniWorld-V1/chat_template.json +0 -3
  5. models/UniWorld-V1/config.json +0 -120
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  15. models/UniWorld-V1/task_head_final.pt +0 -3
  16. models/UniWorld-V1/tokenizer.json +0 -3
  17. models/UniWorld-V1/tokenizer_config.json +0 -213
  18. models/UniWorld-V1/vocab.json +0 -0
  19. models/flux/LICENSE.md +0 -82
  20. models/flux/model_index.json +0 -32
  21. models/flux/put_them_to_flux +0 -1
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  32. models/flux/tokenizer_2/special_tokens_map.json +0 -125
  33. models/flux/tokenizer_2/spiece.model +0 -3
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  35. models/flux/tokenizer_2/tokenizer1.json +0 -0
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  41. models/siglip2/README.md +0 -102
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  47. models/siglip2/tokenizer.json +0 -3
  48. models/siglip2/tokenizer.model +0 -3
  49. models/siglip2/tokenizer_config.json +0 -2020
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models/UniWorld-V1/vocab.json DELETED
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models/flux/LICENSE.md DELETED
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- **FLUX.1 \[dev\] Non-Commercial License**
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- Black Forest Labs, Inc. (“**we**” or “**our**” or “**Company**”) is pleased to make available the weights, parameters and inference code for the FLUX.1 \[dev\] Model (as defined below) freely available for your non-commercial and non-production use as set forth in this FLUX.1 \[dev\] Non-Commercial License (“**License**”). The “**FLUX.1 \[dev\] Model**” means the FLUX.1 \[dev\] text-to-image AI model and its elements which includes algorithms, software, checkpoints, parameters, source code (inference code, evaluation code, and if applicable, fine-tuning code) and any other materials associated with the FLUX.1 \[dev\] AI model made available by Company under this License, including if any, the technical documentation, manuals and instructions for the use and operation thereof (collectively, “**FLUX.1 \[dev\] Model**”).
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- 1. “**Derivative**” means any (i) modified version of the FLUX.1 \[dev\] Model (including but not limited to any customized or fine-tuned version thereof), (ii) work based on the FLUX.1 \[dev\] Model, or (iii) any other derivative work thereof. For the avoidance of doubt, Outputs are not considered Derivatives under this License.
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- 1. Upon termination of this License, you must cease all use, access or Distribution of the FLUX.1 \[dev\] Model and any Derivatives. The following sections survive termination of this License 2(c), 2(d), 4-11.
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- 1. **Third Party Materials**. The FLUX.1 \[dev\] Model may contain third-party software or other components (including free and open source software) (all of the foregoing, “**Third Party Materials**”), which are subject to the license terms of the respective third-party licensors. Your dealings or correspondence with third parties and your use of or interaction with any Third Party Materials are solely between you and the third party. Company does not control or endorse, and makes no representations or warranties regarding, any Third Party Materials, and your access to and use of such Third Party Materials are at your own risk.
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- 1. **General**. This License will be governed and construed under the laws of the State of Delaware without regard to conflicts of law provisions. If any provision or part of a provision of this License is unlawful, void or unenforceable, that provision or part of the provision is deemed severed from this License, and will not affect the validity and enforceability of any remaining provisions. The failure of Company to exercise or enforce any right or provision of this License will not operate as a waiver of such right or provision. This License does not confer any third-party beneficiary rights upon any other person or entity. This License, together with the Documentation, contains the entire understanding between you and Company regarding the subject matter of this License, and supersedes all other written or oral agreements and understandings between you and Company regarding such subject matter. No change or addition to any provision of this License will be binding unless it is in writing and signed by an authorized representative of both you and Company.
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- ---
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- license: apache-2.0
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- tags:
4
- - vision
5
- widget:
6
- - src: >-
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- https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg
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- candidate_labels: bee in the sky, bee on the flower
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- example_title: Bee
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- library_name: transformers
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- pipeline_tag: zero-shot-image-classification
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- ---
13
-
14
- # SigLIP 2 So400m
15
-
16
- [SigLIP 2](https://huggingface.co/papers/2502.14786) extends the pretraining objective of
17
- [SigLIP](https://huggingface.co/papers/2303.15343) with prior, independently developed techniques
18
- into a unified recipe, for improved semantic understanding, localization, and dense features.
19
-
20
- ## Intended uses
21
-
22
- You can use the raw model for tasks like zero-shot image classification and
23
- image-text retrieval, or as a vision encoder for VLMs (and other vision tasks).
24
-
25
- Here is how to use this model to perform zero-shot image classification:
26
-
27
- ```python
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- from transformers import pipeline
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-
30
- # load pipeline
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- ckpt = "google/siglip2-so400m-patch16-512"
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- image_classifier = pipeline(model=ckpt, task="zero-shot-image-classification")
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-
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- # load image and candidate labels
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- url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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- candidate_labels = ["2 cats", "a plane", "a remote"]
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-
38
- # run inference
39
- outputs = image_classifier(image, candidate_labels)
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- print(outputs)
41
- ```
42
-
43
- You can encode an image using the Vision Tower like so:
44
-
45
- ```python
46
- import torch
47
- from transformers import AutoModel, AutoProcessor
48
- from transformers.image_utils import load_image
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-
50
- # load the model and processor
51
- ckpt = "google/siglip2-so400m-patch16-512"
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- model = AutoModel.from_pretrained(ckpt, device_map="auto").eval()
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- processor = AutoProcessor.from_pretrained(ckpt)
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-
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- # load the image
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- image = load_image("https://huggingface.co/datasets/merve/coco/resolve/main/val2017/000000000285.jpg")
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- inputs = processor(images=[image], return_tensors="pt").to(model.device)
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-
59
- # run infernece
60
- with torch.no_grad():
61
- image_embeddings = model.get_image_features(**inputs)
62
-
63
- print(image_embeddings.shape)
64
- ```
65
-
66
- For more code examples, we refer to the [siglip documentation](https://huggingface.co/transformers/main/model_doc/siglip.html#).
67
-
68
- ## Training procedure
69
-
70
- SigLIP 2 adds some clever training objectives on top of SigLIP:
71
-
72
- 1. Decoder loss
73
- 2. Global-local and masked prediction loss
74
- 3. Aspect ratio and resolution adaptibility
75
-
76
- ### Training data
77
-
78
- SigLIP 2 is pre-trained on the WebLI dataset [(Chen et al., 2023)](https://arxiv.org/abs/2209.06794).
79
-
80
- ### Compute
81
-
82
- The model was trained on up to 2048 TPU-v5e chips.
83
-
84
- ## Evaluation results
85
-
86
- Evaluation of SigLIP 2 is shown below (taken from the paper).
87
-
88
- ![Evaluation Table](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/sg2-blog/eval_table.png)
89
-
90
- ### BibTeX entry and citation info
91
-
92
- ```bibtex
93
- @misc{tschannen2025siglip2multilingualvisionlanguage,
94
- title={SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features},
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- author={Michael Tschannen and Alexey Gritsenko and Xiao Wang and Muhammad Ferjad Naeem and Ibrahim Alabdulmohsin and Nikhil Parthasarathy and Talfan Evans and Lucas Beyer and Ye Xia and Basil Mustafa and Olivier Hénaff and Jeremiah Harmsen and Andreas Steiner and Xiaohua Zhai},
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- year={2025},
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- eprint={2502.14786},
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- archivePrefix={arXiv},
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- primaryClass={cs.CV},
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- url={https://arxiv.org/abs/2502.14786},
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- }
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- ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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1529
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1530
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1531
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1532
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1534
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1542
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1543
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1544
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1550
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1551
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1552
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1553
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1554
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- },
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1558
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1559
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1560
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1561
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1562
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- },
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1566
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1571
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1572
- },
1573
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1574
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1575
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1576
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1577
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1578
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1580
- },
1581
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1582
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1583
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1585
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1588
- },
1589
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1590
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1591
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1592
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1593
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1594
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1595
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1596
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1598
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1599
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1600
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1601
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1602
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1603
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1604
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1605
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1606
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1607
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1613
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1614
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1615
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1616
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1617
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1618
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1619
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1620
- },
1621
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1622
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1623
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1624
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1625
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1628
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1630
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1632
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1633
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1634
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1635
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1636
- },
1637
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1638
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1639
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1644
- },
1645
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1646
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1647
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1648
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1649
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1650
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1652
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1653
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1654
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1655
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1660
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1662
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1663
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1664
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1665
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1666
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1667
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1668
- },
1669
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1670
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1676
- },
1677
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1678
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1679
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1681
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1682
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1683
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1684
- },
1685
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1686
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1687
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1688
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1689
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1690
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1691
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1692
- },
1693
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1694
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1695
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1696
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1697
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1698
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1699
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1700
- },
1701
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1702
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1703
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1704
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1705
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1706
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1707
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1708
- },
1709
- "213": {
1710
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1711
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1712
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1713
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1714
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1715
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1716
- },
1717
- "214": {
1718
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1719
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1720
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1721
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1722
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1723
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1724
- },
1725
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1726
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1727
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1728
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1729
- "rstrip": false,
1730
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1731
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1732
- },
1733
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1734
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1735
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1736
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1737
- "rstrip": false,
1738
- "single_word": false,
1739
- "special": false
1740
- },
1741
- "255968": {
1742
- "content": "[toxicity=0]",
1743
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1744
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1745
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1747
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1748
- },
1749
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1750
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1752
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1753
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1755
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1756
- },
1757
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1758
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1759
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1760
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1761
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1762
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1763
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1764
- },
1765
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1766
- "content": "\t\t\t\t",
1767
- "lstrip": false,
1768
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1769
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1770
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1771
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1772
- },
1773
- "255972": {
1774
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1775
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1776
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1777
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1778
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1779
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- },
1781
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1782
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1790
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1797
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1798
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1800
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1877
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1878
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- "lstrip": false,
1880
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1885
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1886
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- "lstrip": false,
1888
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1892
- },
1893
- "255987": {
1894
- "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
1895
- "lstrip": false,
1896
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1897
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1898
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1899
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1900
- },
1901
- "255988": {
1902
- "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
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1909
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1910
- "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
1911
- "lstrip": false,
1912
- "normalized": false,
1913
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1914
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1915
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1916
- },
1917
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1918
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- "lstrip": false,
1920
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1921
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1923
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1924
- },
1925
- "255991": {
1926
- "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
1927
- "lstrip": false,
1928
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1929
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1930
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1931
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1932
- },
1933
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1934
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1935
- "lstrip": false,
1936
- "normalized": false,
1937
- "rstrip": false,
1938
- "single_word": false,
1939
- "special": false
1940
- },
1941
- "255993": {
1942
- "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
1943
- "lstrip": false,
1944
- "normalized": false,
1945
- "rstrip": false,
1946
- "single_word": false,
1947
- "special": false
1948
- },
1949
- "255994": {
1950
- "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
1951
- "lstrip": false,
1952
- "normalized": false,
1953
- "rstrip": false,
1954
- "single_word": false,
1955
- "special": false
1956
- },
1957
- "255995": {
1958
- "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
1959
- "lstrip": false,
1960
- "normalized": false,
1961
- "rstrip": false,
1962
- "single_word": false,
1963
- "special": false
1964
- },
1965
- "255996": {
1966
- "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
1967
- "lstrip": false,
1968
- "normalized": false,
1969
- "rstrip": false,
1970
- "single_word": false,
1971
- "special": false
1972
- },
1973
- "255997": {
1974
- "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
1975
- "lstrip": false,
1976
- "normalized": false,
1977
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1978
- "single_word": false,
1979
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1980
- },
1981
- "255998": {
1982
- "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
1983
- "lstrip": false,
1984
- "normalized": false,
1985
- "rstrip": false,
1986
- "single_word": false,
1987
- "special": false
1988
- },
1989
- "255999": {
1990
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1991
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1992
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1993
- "rstrip": false,
1994
- "single_word": false,
1995
- "special": false
1996
- }
1997
- },
1998
- "additional_special_tokens": [
1999
- "<start_of_turn>",
2000
- "<end_of_turn>"
2001
- ],
2002
- "bos_token": "<bos>",
2003
- "chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
2004
- "clean_up_tokenization_spaces": false,
2005
- "do_lower_case": true,
2006
- "eos_token": "<eos>",
2007
- "extra_special_tokens": {},
2008
- "model_input_names": [
2009
- "input_ids"
2010
- ],
2011
- "model_max_length": 1000000000000000019884624838656,
2012
- "pad_token": "<pad>",
2013
- "padding_side": "right",
2014
- "processor_class": "SiglipProcessor",
2015
- "sp_model_kwargs": {},
2016
- "spaces_between_special_tokens": false,
2017
- "tokenizer_class": "GemmaTokenizer",
2018
- "unk_token": "<unk>",
2019
- "use_default_system_prompt": false
2020
- }