Update README.md

README.md

@@ -19,3 +19,87 @@ language:
 This mllama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+
+
+
+
+``` python
+import pandas as pd
+import os
+from unsloth import FastVisionModel
+import torch
+from datasets import load_dataset
+from transformers import TextStreamer
+
+model_name = "NYUAD-ComNets/Llama3.2-MultiModal-Hate_Detector_Memes"
+
+model, tokenizer = FastVisionModel.from_pretrained(model_name, token='xxxxxxxxx')
+
+
+FastVisionModel.for_inference(model)
+
+
+dataset_test = load_dataset("QCRI/Prop2Hate-Meme", split = "test")
+
+def add_labels_column(example):
+    example["labels"] = "no_hate" if example["hate_label"] == 0 else "hate"
+    return example
+
+dataset_test = dataset_test.map(add_labels_column)
+
+def convert_to_conversation(sample):
+    conversation = [
+        
+        { "role": "user",
+          "content" : [
+            {"type" : "text",  "text"  : sample["text"]},
+            {"type" : "image", "image" : sample["image"]} ]
+        },
+        { "role" : "assistant",
+          "content" : [
+            {"type" : "text",  "text"  : sample["labels"]} ]
+        },
+    ]
+    return { "messages" : conversation }
+pass
+
+dataset_test = [convert_to_conversation(sample) for sample in dataset_test]
+
+
+pred=[]
+
+for k in range(606):
+        image = dataset_test[k]["image"]
+        text = dataset_test[k]["text"]
+
+        
+        
+        messages = [
+           
+            {"role": "user", "content": [
+                {"type": "image"},
+                {"type": "text", "text": text}
+            ]}
+        ]
+        
+        
+        input_text = tokenizer.apply_chat_template(messages,add_generation_prompt = True)
+        inputs = tokenizer(
+            image,
+            input_text,
+            add_special_tokens = False,
+            return_tensors = "pt",
+        ).to("cuda")
+        
+        text_streamer = TextStreamer(tokenizer, skip_prompt = True)
+        p = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,
+                           use_cache = True, temperature = 0.1, min_p = 0.1)
+
+        p = tokenizer.decode(p[0], skip_special_tokens=True)
+
+
+
+        pred.append(p.split('assistant')[1])
+
+print(pred)
+```