Update README.md

README.md

@@ -2,6 +2,10 @@
 tags:
 - vision
 - image-text-to-text
+license: llama2
+language:
+- en
+pipeline_tag: image-text-to-text
 ---
 
 # LLaVa-Next, leveraging [liuhaotian/llava-v1.6-vicuna-13b](https://huggingface.co/liuhaotian/llava-v1.6-vicuna-13b) as LLM
@@ -29,6 +33,7 @@ Here's the prompt template for this model:
 ```
 "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. USER: <image>\nWhat is shown in this image? ASSISTANT:"
 ```
+
 You can load and use the model like following:
 ```python
 from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
@@ -44,7 +49,20 @@ model.to("cuda:0")
 # prepare image and text prompt, using the appropriate prompt template
 url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
 image = Image.open(requests.get(url, stream=True).raw)
-prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. USER: <image>\nWhat is shown in this image? ASSISTANT:"
+
+# Define a chat histiry and use `apply_chat_template` to get correctly formatted prompt
+# Each value in "content" has to be a list of dicts with types ("text", "image") 
+conversation = [
+    {
+
+      "role": "user",
+      "content": [
+          {"type": "text", "text": "What is shown in this image?"},
+          {"type": "image"},
+        ],
+    },
+]
+prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
 
 inputs = processor(prompt, image, return_tensors="pt").to("cuda:0")