Image-to-Text
Transformers
Safetensors
English
qwen2_5_vl
image-text-to-text
vision-language-model
visual-storytelling
chain-of-thought
grounded-text-generation
cross-frame-consistency
storytelling
contrastive-learning
reinforcement-learning
entity-reidentification
Eval Results (legacy)
text-generation-inference
Instructions to use daniel3303/QwenStoryteller2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use daniel3303/QwenStoryteller2 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="daniel3303/QwenStoryteller2")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("daniel3303/QwenStoryteller2") model = AutoModelForMultimodalLM.from_pretrained("daniel3303/QwenStoryteller2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,730 Bytes
5133d76 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | {
"_valid_kwargs_names": [
"do_convert_rgb",
"do_resize",
"size",
"size_divisor",
"default_to_square",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_pad",
"do_center_crop",
"crop_size",
"data_format",
"input_data_format",
"device",
"min_pixels",
"max_pixels",
"patch_size",
"temporal_patch_size",
"merge_size"
],
"crop_size": null,
"data_format": "channels_first",
"default_to_square": true,
"device": null,
"do_center_crop": null,
"do_convert_rgb": true,
"do_normalize": true,
"do_pad": null,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_processor_type": "Qwen2VLImageProcessor",
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"input_data_format": null,
"max_pixels": 12845056,
"merge_size": 2,
"min_pixels": 3136,
"model_valid_processing_keys": [
"do_convert_rgb",
"do_resize",
"size",
"size_divisor",
"default_to_square",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_pad",
"do_center_crop",
"crop_size",
"data_format",
"input_data_format",
"device",
"min_pixels",
"max_pixels",
"patch_size",
"temporal_patch_size",
"merge_size"
],
"patch_size": 14,
"processor_class": "Qwen2_5_VLProcessor",
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"longest_edge": 12845056,
"shortest_edge": 3136
},
"size_divisor": null,
"temporal_patch_size": 2,
"video_processor_type": "Qwen2VLVideoProcessor"
}
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