Video-Text-to-Text
Transformers
Safetensors
molmo2
image-text-to-text
video
object-tracking
ecology
custom_code
Instructions to use tidalove/Molmo2Fish with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tidalove/Molmo2Fish with Transformers:
# Load model directly from transformers import AutoModelForImageTextToText model = AutoModelForImageTextToText.from_pretrained("tidalove/Molmo2Fish", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model card: exclude the raw tar from the weights download
Browse files
README.md
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@@ -61,7 +61,7 @@ pip install torchcodec && pip install -e .[all]
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export MOLMO_DATA_DIR=./data
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python -m scripts.download_datasets cfc --n-procs 8
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hf download tidalove/Molmo2Fish --local-dir Molmo2Fish-HF/step420-hf
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python launch_scripts/hf_eval.py Molmo2Fish-HF/step420-hf \
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cfc_hf_correction_molmo_low_full_eval_2fps
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```
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export MOLMO_DATA_DIR=./data
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python -m scripts.download_datasets cfc --n-procs 8
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hf download tidalove/Molmo2Fish --exclude "*.tar" --local-dir Molmo2Fish-HF/step420-hf
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python launch_scripts/hf_eval.py Molmo2Fish-HF/step420-hf \
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cfc_hf_correction_molmo_low_full_eval_2fps
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```
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