Update README.md

README.md

@@ -194,15 +194,20 @@ base64_image = encode_image("invoice.png")
 
 chat_response = client.chat.completions.create(
     model="numind/NuMarkdown-8B-reasoning",
-    temperature=0.8,
+    temperature=0.7,
     messages=[
-        {
-            "role": "user", 
-            "content": [
-                {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}},
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "image_url", 
+                         "image_url": {"url": data_url},
+                         "min_pixels": 100 * 28 * 28,
+                         "max_pixels": 5000 * 28 * 28,},
+                        
+                    ],
+                },
             ],
-        },
-    ]
+
 )
 
 reasoning = chat_response.choices[0].message.content.split("<thining>")[1].split("</thining>")[0]
@@ -222,7 +227,8 @@ model_id = "numind/NuMarkdown-8B-reasoning"
 
 processor = AutoProcessor.from_pretrained(
     model_id,
-    trust_remote_code=True,       
+    trust_remote_code=True,
+    min_pixels=100*28*28, max_pixels=5000*28*28   
 )
 
 model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
@@ -244,7 +250,7 @@ prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_
 enc = processor(text=prompt, images=[img], return_tensors="pt").to(model.device)
 
 with torch.no_grad():
-    out = model.generate(**enc, max_new_tokens=5000)
+    out = model.generate(**enc, temperature = 0.7, max_new_tokens=5000)
 
 print(processor.decode(out[0].split("<answer>")[1].split("</answer>")[0], skip_special_tokens=True))
 ```