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README.md

@@ -15,27 +15,32 @@ library_name: peft
 
 <!-- Provide a longer summary of what this model is. -->
 
+- **Model type:** vidore/colpali-v1.2
+- **Finetuned from model [optional]:** vidore/colpali-v1.2
 
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+## Uses
 
-### Model Sources [optional]
+This model is finetuned from `vidore/colpali-v1.2` using the PEFT library. To use this model, you can use the following code:
 
-<!-- Provide the basic links for the model. -->
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### This will load the basemodel ###
+# Load the base model
+model_name = "colpali_finetuned"
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
-## Uses
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+
+### This will load the adapter model ###
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+peft_model_path='colpali_finetuned/checkpoint-587"
+model = PeftModel.from_pretrained(peft_model_path)
+```
 
 ### Direct Use