Instructions to use OfflineBase/Muse-Glimmer-30B-Lab-Decide-Support-Inbox-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use OfflineBase/Muse-Glimmer-30B-Lab-Decide-Support-Inbox-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Muse-Glimmer-30B-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "OfflineBase/Muse-Glimmer-30B-Lab-Decide-Support-Inbox-LoRA") - Notebooks
- Google Colab
- Kaggle
Upload processor_config.json
Browse files- processor_config.json +53 -0
processor_config.json
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{
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"image_processor": {
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"do_convert_rgb": true,
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"do_normalize": true,
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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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],
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"image_processor_type": "MuseGlimmerImageProcessor",
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"image_std": [
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0.5,
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],
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"max_image_tokens": 4096,
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"merge_size": 2,
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"patch_size": 14,
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"resample": 1,
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"rescale_factor": 0.00392156862745098,
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"temporal_patch_size": 2
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},
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"processor_class": "MuseGlimmerProcessor",
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"video_processor": {
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"do_convert_rgb": true,
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"do_normalize": true,
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"do_rescale": true,
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"do_resize": true,
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"do_sample_frames": true,
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"fps": 2.0,
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"image_mean": [
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0.5,
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],
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"image_std": [
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],
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"max_video_frame_tokens": 144,
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"merge_size": 2,
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"num_frames": 96,
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"patch_size": 14,
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"resample": 1,
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"rescale_factor": 0.00392156862745098,
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"return_metadata": true,
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"temporal_patch_size": 2,
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"video_processor_type": "MuseGlimmerVideoProcessor"
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}
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}
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