Update README.md

README.md

@@ -5,5 +5,64 @@ datasets:
 - merve/vqav2-small
 ---
 
-## Idefics3Llama Fine-tuned using QLoRA on VQAv2
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6141a88b3a0ec78603c9e784/PebmPLcCig5BlpUS99VUc.png)
 
+# Idefics3Llama Fine-tuned using QLoRA on VQAv2
+
+- This is the [Idefics3Llama](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) model QLoRA fine-tuned on a very small part of [VQAv2](https://huggingface.co/datasets/merve/vqav2-small) dataset.
+
+- Find the fine-tuning notebook [here](https://github.com/merveenoyan/smol-vision/blob/main/Idefics_FT.ipynb). 
+
+## Usage
+
+You can load and use this model as follows.
+
+```python
+
+from transformers import Idefics3ForConditionalGeneration, AutoProcessor
+
+peft_model_id = "idefics3-llama-vqav2/checkpoint-535"
+base_model_id = "HuggingFaceM4/Idefics3-8B-Llama3"
+processor = AutoProcessor.from_pretrained(base_model_id)
+model = Idefics3ForConditionalGeneration.from_pretrained(base_model_id)
+model.load_adapter(peft_model_id).to("cuda")
+
+```
+
+This model was conditioned on a prompt "Answer briefly.".
+```python
+from PIL import Image
+import requests
+from transformers.image_utils import load_image
+
+DEVICE = "cuda"
+
+image = load_image("https://huggingface.co/spaces/merve/OWLSAM2/resolve/main/buddha.JPG")
+
+
+messages = [
+          {
+              "role": "user",
+              "content": [
+                  {"type": "text", "text": "Answer briefly."},
+                  {"type": "image"},
+                  {"type": "text", "text": "Which country is this located in?"}
+              ]
+          }
+      ]
+
+text = processor.apply_chat_template(messages, add_generation_prompt=True)
+inputs = processor(text=text, images=image, return_tensors="pt", padding=True).to("cuda")
+```
+
+We can infer.
+
+```python
+generated_ids = model.generate(**inputs, max_new_tokens=500)
+generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
+print(generated_texts)
+
+##['User: Answer briefly.<row_1_col_1><row_1_col_2><row_1_col_3><row_1_col_4>\n<row_2_col_1>
+# <row_2_col_2><row_2_col_3><row_2_col_4>\n<row_3_col_1><row_3_col_2><row_3_col_3>
+# <row_3_col_4>\n\n<global-img>Which country is this located in?\nAssistant: thailand\nAssistant: thailand']
+```