Update README.md

README.md

@@ -165,45 +165,6 @@ Pàgina 2 de 2
 
 ## Quick start: 🤗 Transformers
 
-```python
-from __future__ import annotations
-
-import torch
-from PIL import Image
-from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
-
-model_id = "Numind/NuMarkdown-reasoning"       
-
-processor = AutoProcessor.from_pretrained(
-    model_id,
-    trust_remote_code=True,       
-)
-
-model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    model_id,
-    torch_dtype=torch.bfloat16,
-    attn_implementation="flash_attention_2",
-    device_map="auto",
-    trust_remote_code=True,
-)
-
-img = Image.open("invoice.png").convert("RGB")
-messages = [{
-    "role": "user",
-    "content": [
-        {"type": "image"},
-    ],
-}]
-prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-enc = processor(text=prompt, images=[img], return_tensors="pt").to(model.device)
-
-with torch.no_grad():
-    out = model.generate(**enc, max_new_tokens=5000)
-
-print(processor.decode(out[0].split("<answer>")[1].split("</answer>")[0], skip_special_tokens=True))
-```
-
-
 ## vLLM:
 ```
 vllm serve numind/NuMarkdown-reasoning --trust_remote_code --limit-mm-per-prompt image=1
@@ -246,4 +207,42 @@ chat_response = client.chat.completions.create(
 
 reasoning = chat_response.choices[0].message.content.split("<thining>")[1].split("</thining>")[0]
 answer  = chat_response.choices[0].message.content.split("<answer>")[1].split("</answer>")[0]
+```
+
+```python
+from __future__ import annotations
+
+import torch
+from PIL import Image
+from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
+
+model_id = "Numind/NuMarkdown-reasoning"       
+
+processor = AutoProcessor.from_pretrained(
+    model_id,
+    trust_remote_code=True,       
+)
+
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+img = Image.open("invoice.png").convert("RGB")
+messages = [{
+    "role": "user",
+    "content": [
+        {"type": "image"},
+    ],
+}]
+prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+enc = processor(text=prompt, images=[img], return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    out = model.generate(**enc, max_new_tokens=5000)
+
+print(processor.decode(out[0].split("<answer>")[1].split("</answer>")[0], skip_special_tokens=True))
 ```