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  1. README.md +4 -3
README.md CHANGED
@@ -40,17 +40,18 @@ Below we used [`"llava-hf/llava-interleave-qwen-0.5b-hf"`](https://huggingface.c
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  ```python
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- from transformers import pipeline
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  from PIL import Image
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  import requests
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  model_id = "llava-hf/llava-interleave-qwen-7b-hf"
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  pipe = pipeline("image-to-text", model=model_id)
 
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  url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
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  image = Image.open(requests.get(url, stream=True).raw)
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- # Define a chat histiry and use `apply_chat_template` to get correctly formatted prompt
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  # Each value in "content" has to be a list of dicts with types ("text", "image")
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  conversation = [
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  {
@@ -88,7 +89,7 @@ model = LlavaForConditionalGeneration.from_pretrained(
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  processor = AutoProcessor.from_pretrained(model_id)
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- # Define a chat histiry and use `apply_chat_template` to get correctly formatted prompt
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  # Each value in "content" has to be a list of dicts with types ("text", "image")
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  conversation = [
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  {
 
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  ```python
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+ from transformers import pipeline, AutoProcessor
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  from PIL import Image
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  import requests
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  model_id = "llava-hf/llava-interleave-qwen-7b-hf"
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  pipe = pipeline("image-to-text", model=model_id)
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+ processor = AutoProcessor.from_pretrained(model_id)
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  url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
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  image = Image.open(requests.get(url, stream=True).raw)
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+ # Define a chat history and use `apply_chat_template` to get correctly formatted prompt
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  # Each value in "content" has to be a list of dicts with types ("text", "image")
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  conversation = [
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  {
 
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  processor = AutoProcessor.from_pretrained(model_id)
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+ # Define a chat history and use `apply_chat_template` to get correctly formatted prompt
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  # Each value in "content" has to be a list of dicts with types ("text", "image")
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  conversation = [
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  {