Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+---
+# Fal-2-500M: Efficient Vision-Language Model
+
+<p align="center">
+
+Fal-2-500M is a compact vision-language model designed for image understanding and captioning tasks. Built on the Qwen2 architecture with an efficient vision encoder, it provides high-quality image descriptions with fast inference.<img src="https://huggingface.co/SVECTOR-CORPORATION/Fal-2-500M/resolve/main/Fal-2-500M.png" alt="Accuracy vs latency figure." width="400"/>
+
+</p>
+
+### Highlights
+
+- **Model Size**: 500M parameters* We introduce a hybrid vision encoder designed to output fewer tokens and significantly reduce encoding time for high-resolution images.  
+
+- **Efficient Token Generation**: 256 tokens at 1024×1024 resolution (16× fewer than ViT)
+  
+- **State-of-the-Art Performance**: Competitive accuracy with superior efficiency
+
+- **Primary Use**: Image captioning and visual question answering* 
+
+```python
+import torch
+from PIL import Image
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+MID = "SVECTOR-CORPORATION/Fal-2-500M"
+IMAGE_TOKEN_INDEX = -200
+tok = AutoTokenizer.from_pretrained(MID, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    MID,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto",
+    trust_remote_code=True,
+)
+messages = [
+    {"role": "user", "content": "<image>\nDescribe me this image."}
+]
+rendered = tok.apply_chat_template(
+    messages, add_generation_prompt=True, tokenize=False
+)
+
+pre, post = rendered.split("<image>", 1)
+pre_ids  = tok(pre,  return_tensors="pt", add_special_tokens=False).input_ids
+post_ids = tok(post, return_tensors="pt", add_special_tokens=False).input_ids
+
+img_tok = torch.tensor([[IMAGE_TOKEN_INDEX]], dtype=pre_ids.dtype)
+input_ids = torch.cat([pre_ids, img_tok, post_ids], dim=1).to(model.device)
+attention_mask = torch.ones_like(input_ids, device=model.device)
+
+img = Image.open("photo.jpg").convert("RGB")
+px = model.get_vision_tower().image_processor(images=img, return_tensors="pt")["pixel_values"]
+px = px.to(model.device, dtype=model.dtype)
+
+# Generate
+with torch.no_grad():
+    out = model.generate(
+        inputs=input_ids,
+        attention_mask=attention_mask,
+        images=px,
+        max_new_tokens=128,
+    )
+
+print(tok.decode(out[0], skip_special_tokens=True))
+```
+