Datasets:

Omundu-Riad
/

checkpoints_diverse

@@ -96,7 +96,7 @@ time only, on `best_vidore_v1_step5200` (result JSONs in `vidore_results_query_a
 The adapters target the ColMinistral3 architecture of the training code (a `colpali_engine` fork with `ColMinistral`
 and `ColMinistral3Processor`). `adapter_config.json` points to the local path of the base model used in training
-(`ColMinistral` built from `mistralai/Ministral-3-3B-Instruct-2512`); set `base_model_name_or_path` to your copy.
 Evaluation reproduces training exactly: frozen base in bf16, unmerged DoRA adapter, trained modules in fp32, forward
 under bf16 autocast, every non-padding token is a vector, MaxSim summed over query tokens. Raw `checkpoint-N` folders of
 this run have no processor files; `checkpoint-6798` here includes them (copied from the run's final output, whose

 The adapters target the ColMinistral3 architecture of the training code (a `colpali_engine` fork with `ColMinistral`
 and `ColMinistral3Processor`). `adapter_config.json` points to the local path of the base model used in training
+(the legacy ColMinistral3 model, on the Hub as [`Omundu-Riad/colministral3`](https://huggingface.co/Omundu-Riad/colministral3)); download it and recreate that path or set `base_model_name_or_path` to your copy.
 Evaluation reproduces training exactly: frozen base in bf16, unmerged DoRA adapter, trained modules in fp32, forward
 under bf16 autocast, every non-padding token is a vector, MaxSim summed over query tokens. Raw `checkpoint-N` folders of
 this run have no processor files; `checkpoint-6798` here includes them (copied from the run's final output, whose