Image-Text-to-Text
PEFT
Safetensors
lora
muse-glimmer
json
structured-output
api
tool-use
conversational
Instructions to use yogeshjog/muse-glimmer-json-api with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use yogeshjog/muse-glimmer-json-api with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-models/Muse-Glimmer-30B") model = PeftModel.from_pretrained(base_model, "yogeshjog/muse-glimmer-json-api") - Notebooks
- Google Colab
- Kaggle
| { | |
| "image_processor": { | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "MuseGlimmerImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "max_image_tokens": 4096, | |
| "merge_size": 2, | |
| "patch_size": 14, | |
| "resample": 1, | |
| "rescale_factor": 0.00392156862745098, | |
| "temporal_patch_size": 2 | |
| }, | |
| "processor_class": "MuseGlimmerProcessor", | |
| "video_processor": { | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "do_sample_frames": true, | |
| "fps": 2.0, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "max_video_frame_tokens": 144, | |
| "merge_size": 2, | |
| "num_frames": 96, | |
| "patch_size": 14, | |
| "resample": 1, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_metadata": true, | |
| "temporal_patch_size": 2, | |
| "video_processor_type": "MuseGlimmerVideoProcessor" | |
| } | |
| } | |