Update README.md

README.md

@@ -5,5 +5,75 @@ This is the repository for SophiaVL-R1-7B.
 
 A simple inference example:
 ```python
+from transformers import AutoProcessor
+import torch
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
+from PIL import Image
 
+MODEL_PATH = "bunny127/SophiaVL-R1-7B"
+# Example usage:
+    # {
+    #     "problem_id": 1,
+    #     "problem": "Subtract 0 cyan cubes. How many objects are left?",
+    #     "data_type": "image",
+    #     "problem_type": "numerical",
+    #     "options": [],
+    #     "process": "",
+    #     "solution": "<answer>5</answer>",
+    #     "path": "./Math/CLEVR-Math/images/CLEVR_train_036427.png",
+    #     "data_source": "CLEVR-Math"
+    # },
+image_path = "/path/to/dataset/Math/CLEVR-Math/images/CLEVR_train_036427.png"
+prompt = "Subtract 0 cyan cubes. How many objects are left?"
+question_type = "numerical"
+
+
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(MODEL_PATH, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2",device_map="auto")
+processor = AutoProcessor.from_pretrained(MODEL_PATH)
+
+SYS_PROMPT = """You FIRST think about the reasoning process as an internal monologue and then provide the final answer.
+The reasoning process MUST BE enclosed within <think> </think> tagsdd. The final answer MUST BE enclosed within <answer> </answer> tags, for example <think>your_thinking_process</think><answer>your_final_answer</answer>. If you use formula, please use LaTeX format."""
+
+QUESTION_TEMPLATE = (
+    "{Question}\n"
+    "Please think about this question as if you were a human pondering deeply. "
+    "Engage in an internal dialogue using expressions such as 'let me think', 'wait', 'Hmm', 'oh, I see', 'let's break it down', etc, or other natural language thought expressions "
+    "It's encouraged to include self-reflection or verification in the reasoning process. "
+    "Provide your detailed reasoning between the <think> and </think> tags, and then give your final answer between the <answer> and </answer> tags."
+)
+
+TYPE_TEMPLATE = {
+    "multiple choice": " Please provide only the single option letter (e.g., A, B, C, D, etc.) within the <answer> </answer> tags.",
+    "numerical": " Please provide the numerical value (e.g., 42 or 3.14) within the <answer> </answer> tags.",
+    "OCR": " Please transcribe text from the image/video clearly and provide your text answer within the <answer> </answer> tags.",
+    "free-form": " Please provide your text answer within the <answer> </answer> tags."
+}
+
+def inference(image_path, question, problem_type = "numerical", sys_prompt="You are a helpful assistant.", max_new_tokens=4096, return_input=False):
+    image = Image.open(image_path)
+    image_local_path = "file://" + image_path
+    messages = [
+        {"role": "system", "content": sys_prompt},
+        {"role": "user", "content": [
+                {"type": "text", "text": QUESTION_TEMPLATE.format(Question=question) + TYPE_TEMPLATE[problem_type]},
+                {"image": image_local_path},
+            ]
+        },
+    ]
+    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    print("text:", text)
+    # image_inputs, video_inputs = process_vision_info([messages])
+    inputs = processor(text=[text], images=[image], padding=True, return_tensors="pt")
+    inputs = inputs.to('cuda')
+
+    output_ids = model.generate(**inputs, max_new_tokens=max_new_tokens)
+    generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
+    output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+    if return_input:
+        return output_text[0], inputs
+    else:
+        return output_text[0]
+
+response = inference(image_path, prompt, question_type, sys_prompt=SYS_PROMPT, max_new_tokens=2048)
+print(response)
 ```