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license: llama4
library_name: transformers
base_model:
- meta-llama/Llama-4-Scout-17B-16E
---
<p align="center">
<img src="images/RoadXpert-logo.png" alt="Logo" width="40%">
</p>
# RoadXpert-AI-V1-109B MoE
[Blog Post](https://www.deepcogito.com/research/cogito-v2-preview)
The Cogito v2 LLMs are instruction tuned generative models. All models are released under an open license for commercial use.
- Cogito v2 models are hybrid reasoning models. Each model can answer directly (standard LLM), or self-reflect before answering (like reasoning models).
- The LLMs are trained using **Iterated Distillation and Amplification (IDA)** - an scalable and efficient alignment strategy for superintelligence using iterative self-improvement.
- The models have been optimized for coding, STEM, instruction following and general helpfulness, and have significantly higher multilingual, coding and tool calling capabilities than size equivalent counterparts.
- In both standard and reasoning modes, Cogito v2-preview models outperform their size equivalent counterparts on common industry benchmarks.
- This model is trained in over 30 languages and supports long contexts (upto 10M tokens).
# Evaluations
Here is the model performance on some standard industry benchmarks:
<p align="left">
<img src="images/cogito-v2-109b-benchmarks.png" alt="Logo" width="90%">
</p>
For detailed evaluations, please refer to the [Blog Post](https://www.deepcogito.com/research/cogito-v2-preview).
# Usage
Here is a snippet below for usage with Transformers:
```python
```
## License
This repository and the model weights are licensed under the [Llama 4 Community License Agreement](https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE) (Llama models' default license agreement).
All fine-tuning, dataset curation, and optimization have been performed by **Arijit Rakshit (2025)** and others.
## Contact
If you would like to reach out to our team, send an email to [contact@deepcogito.com](contact@deepcogito.com). |