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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("tidalove/Molmo2Fish", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("tidalove/Molmo2Fish", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from tidalove/Molmo2Fish: direct link, hf CLI and curl.
- Browser
- Download file 2.28 kB
-
https://huggingface.co/tidalove/Molmo2Fish/resolve/main/README.md
- Command line
-
hf download hf://tidalove/Molmo2Fish/README.md
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curl -L -o README.md https://huggingface.co/tidalove/Molmo2Fish/resolve/main/README.md
2.28 kB
| license: apache-2.0 | |
| base_model: allenai/Molmo2-8B | |
| pipeline_tag: video-text-to-text | |
| library_name: transformers | |
| tags: | |
| - molmo2 | |
| - video | |
| - object-tracking | |
| - ecology | |
| datasets: | |
| - tidalove/cfc-track-instruction | |
| - perona-lab/cfc26 | |
| # Molmo2Fish | |
| Molmo2Fish is [Molmo2-8B](https://github.com/allenai/molmo2) fine-tuned to **track fish in | |
| sonar video, and to edit those tracks in response to natural-language feedback**. | |
| Track correction is treated as a conversation: the model is shown a set of existing tracks — | |
| its own earlier predictions, another tracker's output, or corrupted ground truth — is told in | |
| words what is wrong with them, and returns a repaired set of tracks. | |
| | | | | |
| |---|---| | |
| | Code | [github.com/tidalove/molmo2fish](https://github.com/tidalove/molmo2fish) | | |
| | Data | [tidalove/cfc-track-instruction](https://huggingface.co/datasets/tidalove/cfc-track-instruction) | | |
| | Source video | [Caltech Fish Counting](https://huggingface.co/datasets/perona-lab/cfc26) | | |
| ## Files | |
| | file | what it is | | |
| |---|---| | |
| | `*.safetensors`, `config.json`, … | HuggingFace-format weights at the repo root — what vLLM and `launch_scripts/hf_eval.py` consume | | |
| | `Molmo2Fish-step420-raw.tar` | the raw training checkpoint (sharded model + optimizer state), for resuming fine-tuning | | |
| ## Training | |
| Rank 64 LoRA fine-tuning of Molmo2-8B on the full CFC mixture, step 420. Adapters on all | |
| three components — LLM, ViT, and connector — with the base weights frozen: | |
| ```bash | |
| torchrun --nproc-per-node=8 launch_scripts/sft.py /path/to/Molmo2-8B cfc_correction \ | |
| --lora_llm --lora_vit --lora_connector --lora_rank 64 \ | |
| --save_folder=/path/to/save/folder | |
| ``` | |
| The `cfc_correction` mixture combines pure tracking, targeted correction, synthetically | |
| corrupted correction, correction of real model predictions at two quality levels | |
| (`molmo_high` / `molmo_low`), and text-only correction. See the | |
| [dataset card](https://huggingface.co/datasets/tidalove/cfc-track-instruction) for what each | |
| config contains. | |
| ## Citation | |
| ```bibtex | |
| @article{molmo2fish, | |
| title={Teach a Molmo2Fish: Towards interactive fish tracking with natural language guidance}, | |
| author={Kai van Brunt and Justin Kay and Sara Beery}, | |
| year={2026}, | |
| url={https://arxiv.org/abs/2608.18602} | |
| } | |
| ``` | |