A multi-image chain-of-thought fine-tune of OpenGVLab/InternVL3-8B, merged into standalone weights so it loads with trustremotecode=True in transformers or vLLM without the adapter. final model. - LoRA, r=64, alpha=128, dropout=0.05, on q/k/v/oproj and gate/up/downproj - modulestosave: ["visionmodel", "mlp1"] — the vision tower and the projector were trained in full rather than low-rank adapted, which is why the adapter was 1.3 GB and why merging is required for serving Accuracy against the base model. The prompt is stated per row, because it matters more than the fine-tune does: rows are only comparable to other rows evaluated the same way. MIRB and MuirBench/BLINK use different image…
Independent publisher
Margi Pandya
MargiPandya
Models in Library1
Datasets in Library0
Models on Hugging Face16
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