Update README.md

README.md

@@ -63,79 +63,30 @@ The model processes images by:
 ### Basic Inference
 
 ```python
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, CLIPProcessor
-from PIL import Image
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-# Load components
-tokenizer = AutoTokenizer.from_pretrained("GoofyLM/N2-Eye")
-clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
-
-# Load the multimodal model (requires custom loading due to architecture)
-# See the training code for complete loading implementation
-
-# Prepare conversation
-conversation = """<|im_start|>system
-You are a helpful assistant trained by Liquid AI. You can see and understand images.<|im_end|>
-<image>
-<|im_start|>user
-What do you see in this image?<|im_end|>
-<|im_start|>assistant
-"""
-
-# Process inputs
-text_inputs = tokenizer(conversation, return_tensors="pt")
-image = Image.open("your_image.jpg")
-image_inputs = clip_processor(images=image, return_tensors="pt")
-
-# Generate response
-with torch.no_grad():
-    outputs = model.generate(
-        input_ids=text_inputs.input_ids,
-        attention_mask=text_inputs.attention_mask,
-        images=image_inputs.pixel_values,
-        max_new_tokens=150,
-        do_sample=True,
-        temperature=0.7
-    )
-
-### Advanced Usage with Tools
-
-The model supports tool calling through its chat template:
-
-```python
-# Example with tool integration
+tokenizer = AutoTokenizer.from_pretrained("GoofyLM/N2-Eye-v1-1.3B", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("GoofyLM/N2-Eye-v1-1.3B", trust_remote_code=True)
 messages = [
-    {
-        "role": "system", 
-        "content": "You are a helpful assistant that can analyze images and use tools."
-    },
     {
         "role": "user",
         "content": [
-            {"type": "image", "image": "path/to/image.jpg"},
-            {"type": "text", "text": "What's in this image? Use the image_analyzer tool if needed."}
+            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
+            {"type": "text", "text": "What animal is on the candy?"}
         ]
-    }
-]
-
-tools = [
-    {
-        "type": "function",
-        "function": {
-            "name": "image_analyzer",
-            "description": "Analyze image content in detail",
-            "parameters": {"type": "object", "properties": {}}
-        }
-    }
+    },
 ]
-
-# The chat template will automatically format this with proper tool markers
-formatted_prompt = tokenizer.apply_chat_template(
-    messages, 
-    tools=tools,
-    add_generation_prompt=True
-)
+inputs = tokenizer.apply_chat_template(
+	messages,
+	add_generation_prompt=True,
+	tokenize=True,
+	return_dict=True,
+	return_tensors="pt",
+).to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=40)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
 ```
 
 ### Chat Template