vidore/colpali_train_set
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How to use athrael-soju/DualHead-GritLM-Qwen3.5-4B with PEFT:
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How to use athrael-soju/DualHead-GritLM-Qwen3.5-4B with ColPali:
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GritLM-style joint training ablation for the Hydra paper. Trained with alternating retrieval (80%) and generation (20%) batches.
Joint training adds complexity with zero benefit. LoRA-on generation fails catastrophically (single token "The" with p=0.91, image-blind). Both functional modes (LoRA-on retrieval, LoRA-off generation) are equivalent to Hydra's retrieval-only training.
| Mode | Result |
|---|---|
| LoRA on, bidirectional (retrieval) | 0.8893 nDCG@5 |
| LoRA off, causal (generation) | 0.561 ANLS, 76.5% match |
| LoRA on, causal (joint-training goal) | image-blind |
adapter_config.json + adapter_model.safetensors -- LoRA adapterlm_head.pt -- Base model lm_headresults/ -- Raw evaluation JSONs@article{georgiou2026hydra,
title={Hydra: Unifying Document Retrieval and Generation in a Single Vision-Language Model},
author={Georgiou, Athos},
year={2026}
}