Update README.md

README.md

@@ -10,18 +10,21 @@ datasets:
 base_model:
 - microsoft/Phi-3.5-vision-instruct
 pipeline_tag: feature-extraction
+license: mit
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# colphi3.5
+# ColPhi3.5
 
 This model was trained from scratch on the data_dir/colpali_train_set dataset.
 
 ## Model description
 
-More information needed
+ColPhi3.5 is a model based on a novel model architecture and training strategy based on Vision Language Models (VLMs) to efficiently index documents from their visual features. 
+It is a Phi3.5-V-4B extension that generates ColBERT- style multi-vector representations of text and images. 
+It was introduced in the paper ColPali: Efficient Document Retrieval with Vision Language Models.
 
 ## Intended uses & limitations
 
@@ -30,26 +33,3 @@ More information needed
 ## Training and evaluation data
 
 More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 0.0005
-- train_batch_size: 32
-- eval_batch_size: 8
-- seed: 42
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 128
-- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 100
-- num_epochs: 5
-
-### Framework versions
-
-- Transformers 4.46.3
-- Pytorch 2.4.0+cu121
-- Datasets 2.21.0
-- Tokenizers 0.20.3