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---
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license: cc-by-nc-4.0
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base_model:
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- Qwen/Qwen3-1.7B
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- google/siglip2-so400m-patch14-384
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library_name: transformers
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tags:
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- perceptron
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- issac-0.1
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---
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# [Isaac-0.1 by Perceptron](https://www.perceptron.inc/blog/introducing-isaac-0-1) |
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*Note this is the Post-trained model* [Try out the model on our playground](https://www.perceptron.inc/demo) |
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We're introducing Isaac 0.1, our first perceptive-language model and a major step toward building AI systems that can understand and interact with the physical world. Isaac 0.1 is an open-source, 2B-parameter model built for real-world applications. It sets a new standard for efficiency, delivering capabilities that meet or exceed those of models over 50 times its size. |
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Founded by the team behind Meta's Chameleon multimodal models, Perceptron is tackling a fundamental challenge: bringing the power of physical AI to the dynamic, multimodal, and real-time environments we live and work in. |
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Isaac 0.1 is the first in our family of models built to be the intelligence layer for the physical world. It's now available open source for researchers and developers everywhere. |
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## What’s new in Isaac 0.1 |
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**Visual QA, simply trained** |
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Strong results on standard understanding benchmarks with a straightforward, reproducible training recipe. |
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**Grounded spatial intelligence** |
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Precise pointing and localization with robust spatial reasoning. Ask “what’s broken in this machine?” and get grounded answers with highlighted regions—handling occlusions, relationships, and object interactions. |
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**In-context learning for perception** |
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Show a few annotated examples (defects, safety conditions, etc.) in the prompt and the model adapts—no YOLO-style fine-tuning or custom detector stacks required. |
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**OCR & fine-grained detail** |
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Reads small text and dense scenes reliably, across resolutions, with dynamic image handling for tiny features and cluttered layouts. |
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**Conversational Pointing** |
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A new interaction pattern where language and vision stay in lockstep: every claim is grounded and visually cited, reducing hallucinations and making reasoning auditable. |
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## Benchmarks |
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## Example |
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```bash |
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pip install perceptron |
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``` |
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## Example using transformers |
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Learn more: [Huggingface Example Repo](https://github.com/perceptron-ai-inc/perceptron/tree/main/huggingface) |
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```bash |
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!git clone https://github.com/perceptron-ai-inc/perceptron.git |
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!cp -r perceptron/huggingface ./huggingface |
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``` |
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```python |
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from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM |
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from huggingface.modular_isaac import IsaacProcessor |
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tokenizer = AutoTokenizer.from_pretrained("PerceptronAI/Isaac-0.1", trust_remote_code=True, use_fast=False) |
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config = AutoConfig.from_pretrained("PerceptronAI/Isaac-0.1", trust_remote_code=True) |
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processor = IsaacProcessor(tokenizer=tokenizer, config=config) |
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model = AutoModelForCausalLM.from_pretrained("PerceptronAI/Isaac-0.1", trust_remote_code=True) |
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``` |
