Update README.md

README.md

@@ -55,4 +55,40 @@ llava-Qwen2-7B-Instruct-Chinese-CLIP = Qwen/Qwen2-7B-Instruct + multi_modal_proj
 <img src="./images/llava-qwen-2-7b-OFA-Syschinese-clip-chineseOCR_pri_fly_SWH_memechinese_lora_0716_warmup0_1_fp16/7.PNG" width="800" height="400">
 <img src="./images/llava-qwen-2-7b-OFA-Syschinese-clip-chineseOCR_pri_fly_SWH_memechinese_lora_0716_warmup0_1_fp16/8.PNG" width="800" height="400">
 <img src="./images/llava-qwen-2-7b-OFA-Syschinese-clip-chineseOCR_pri_fly_SWH_memechinese_lora_0716_warmup0_1_fp16/9.PNG" width="800" height="400">
-</br>
+</br>
+
+
+7. 代码</br>
+推理代码
+```python
+from transformers import LlavaForConditionalGeneration, AutoProcessor
+import torch
+from PIL import Image
+
+raw_model_name_or_path = "/保存的完整模型路径"
+model = LlavaForConditionalGeneration.from_pretrained(raw_model_name_or_path, device_map="cuda:0", torch_dtype=torch.bfloat16)
+processor = AutoProcessor.from_pretrained(raw_model_name_or_path)
+model.eval()
+
+def build_model_input(model, processor):
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "<image>\n 你是一位有深度的网络图片解读者，擅长解读和描述网络图片。你能洞察图片中的细微之处，对图中的人物面部表情、文字信息、情绪流露和背景寓意具有超强的理解力，描述信息需要详细。"}
+    ]
+    prompt = processor.tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    image = Image.open("01.PNG")
+    inputs = processor(text=prompt, images=image, return_tensors="pt", return_token_type_ids=False)
+    
+    for tk in inputs.keys():
+        inputs[tk] = inputs[tk].to(model.device)
+    generate_ids = model.generate(**inputs, max_new_tokens=200)
+    
+    generate_ids = [
+        oid[len(iids):] for oid, iids in zip(generate_ids, inputs.input_ids)
+    ]
+    gen_text = processor.batch_decode(generate_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0]
+    return gen_text
+build_model_input(model, processor)
+```