Upload examples.py with huggingface_hub

examples.py

@@ -0,0 +1,56 @@
+import torch
+from transformers import AutoTokenizer, AutoModel, AutoImageProcessor
+from PIL import Image
+
+path = "nvidia/Llama-3.1-Nemotron-Nano-VL-8B-V1"
+model = AutoModel.from_pretrained(
+    path,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    trust_remote_code=True,
+    device_map="cuda",).eval()
+
+tokenizer = AutoTokenizer.from_pretrained(path)
+image_processor = AutoImageProcessor.from_pretrained(path, device="cuda", trust_remote_code=True)
+
+generation_config = dict(max_new_tokens=1024, do_sample=False, eos_token_id=tokenizer.eos_token_id)
+
+# pure-text conversation
+question = 'What happened in 1986?'
+response, history = model.chat(
+    tokenizer, None, question, generation_config, history=None, return_history=True
+)
+print(f'User: {question}\nAssistant: {response}')
+
+# single-image single-round conversation
+image_path = 'images/table.png'
+image_features = image_processor(Image.open(image_path))
+question = '<image>\nExtract the table in this image as HTML.'
+response = model.chat(
+    tokenizer=tokenizer, question=question, generation_config=generation_config,
+    **image_features
+)
+print(f'User: {question}\nAssistant: {response}')
+
+# single-image single-round conversation
+image_path = 'images/tech.png'
+image_features = image_processor(Image.open(image_path))
+question = '<image>\nList in bullet point the most important Technological breakthrough of Nvidia Hopper.'
+response = model.chat(
+    tokenizer=tokenizer, question=question, generation_config=generation_config,
+    **image_features
+)
+print(f'User: {question}\nAssistant: {response}')
+
+# two image single-round conversation
+image_features = image_processor([
+    Image.open('images/example1a.jpeg'),
+    Image.open('images/example1b.jpeg')
+])
+
+question = '<image-1>: <image>\n<image-2>: <image>\nBriefly describe the two images.'
+response = model.chat(
+    tokenizer=tokenizer, question=question, generation_config=generation_config,
+    **image_features
+)
+print(f'User: {question}\nAssistant: {response}')