update README

README.md

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-license: cc-by-nc-4.0
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+---
+license: cc-by-nc-4.0
+language:
+- en
+---
+# Isaac-0.2-2B by Perceptron
+
+Introducing the 2B parameter variant of Isaac-0.2, the hybrid-reasoning vision-language model.
+
+This release brings major upgrades — optional reasoning via thinking traces, perceptive tool calling (including our new Focus system), stronger grounding, better OCR, better desktop use, and improved structured output — while remaining fast, compact, and deployable.
+
+## Extending the efficient frontier of perception
+
+Isaac 0.2 extends what we started with Isaac 0.1: small models that outperform systems 10× larger on visual reasoning and perception tasks, all running on commodity GPUs or edge devices.
+From robotics to media search to industrial inspection, Isaac 0.2 delivers high-accuracy perception without the heavy compute footprint.
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/65526dfffb76980adeffa369/yQl-9BAxLud6hhK8gCKLt.png)
+
+## What's New in Isaac 0.2
+
+* **Reasoning via Thinking Traces**: Short, structured reasoning traces improve multi-step decisions, small-object understanding, and ambiguous spatial tasks.
+
+* **Perceptive Tool Calling + Focus (Zoom & Crop)**: Isaac 0.2 can trigger tool calls to focus (i.e., zoom and crop) and re-query the model on a smaller region — dramatically improving fine-grained perception.
+
+* **Structured Outputs**: More reliable structured output generation for consistent JSON and predictable downstream integration.
+
+* **Complex OCR**: Improved text recognition across cluttered, low-resolution, or distorted regions — enabling accurate extraction from documents, diagrams, labels, screens, and dense real-world scenes.
+
+* **Desktop Use**: Better performance on everyday desktop and mobile workflows such as UI understanding and navigation, making Isaac faster and more capable for agentic use cases.
+
+
+
+
+## Performance Benchmarks
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/65526dfffb76980adeffa369/scKXlSu474L4r8-I6Ahau.png)
+
+## Chatting with Isaac in 🤗 Transformers
+Learn more at our [Huggingface Example Repo](https://github.com/perceptron-ai-inc/perceptron/tree/main/huggingface), where we demo extracting and rendering points.
+
+```bash
+pip install perceptron
+```
+
+### Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoProcessor
+from transformers.utils.import_utils import is_torch_cuda_available
+from transformers.image_utils import load_image
+
+def document_to_messages(document: list[dict]):
+    messages, images = [], []
+    for item in document:
+        if not (content := item.get("content")):
+            continue
+        role = item.get("role", "user")
+        if item.get("type") == "image":
+            images.append(load_image(content))
+            messages.append({"role": role, "content": "<image>"})
+        elif item.get("type") == "text":
+            messages.append({"role": role, "content": content})
+    return messages, images
+
+# Load model/processor from the checkpoint
+checkpoint_path = "PerceptronAI/Isaac-0.2-2B-Preview"
+processor = AutoProcessor.from_pretrained(checkpoint_path, trust_remote_code=True)
+device, dtype = ("cuda","bfloat16") if is_torch_cuda_available() else ("cpu","float32")
+model = AutoModelForCausalLM.from_pretrained(
+    checkpoint_path, trust_remote_code=True, vision_attn_implementation="flash_attention_2", dtype = dtype
+).to(device=device)
+
+document = [
+    {
+        "type": "text",
+        "content": "<hint>BOX</hint>",
+        "role": "user",
+    },
+    {
+        "type": "image",
+        "content": "https://raw.githubusercontent.com/perceptron-ai-inc/perceptron/refs/heads/main/huggingface/assets/example.webp",
+        "role": "user",
+    },
+    {
+        "type": "text",
+        "content": "Determine whether it is safe to cross the street. Look for signage and moving traffic.",
+        "role": "user",
+    },
+]
+
+# Prepare inputs for generation
+messages, images = document_to_messages(document)
+text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = processor(text=text, images=images, return_tensors="pt")
+
+# Generation
+generated_ids = model.generate(
+    tensor_stream=inputs["tensor_stream"].to(device),
+    max_new_tokens=256,
+    do_sample=False,
+)
+generated_text = processor.tokenizer.decode(generated_ids[0], skip_special_tokens=False)
+print(f"\n Output: {generated_text}")
+
+```
+