ESarp/Mistral-Nemo-12B-AttackTree-DPO
ESarp/Mistral-Nemo-12B-AttackTree-DPO is a 12 billion parameter Mistral-based language model developed by ESarp, fine-tuned from unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. With a 32768 token context length, it is optimized for specific applications related to attack tree analysis, leveraging its efficient training methodology.
Loading preview...
Model Overview
ESarp/Mistral-Nemo-12B-AttackTree-DPO is a 12 billion parameter language model developed by ESarp. It is fine-tuned from the unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit base model, indicating its foundation in the Mistral architecture. A key characteristic of this model is its training efficiency, having been trained 2x faster using the Unsloth library in conjunction with Huggingface's TRL library.
Key Capabilities
- Efficient Training: Leverages Unsloth for significantly faster fine-tuning, reducing development time and computational resources.
- Mistral-Based Architecture: Benefits from the robust and performant foundation of the Mistral model family.
- Extended Context Length: Features a substantial context window of 32768 tokens, allowing for processing and understanding longer inputs.
Good For
- Specialized Applications: Given its fine-tuning, it is likely well-suited for tasks related to "AttackTree" analysis, as suggested by its name.
- Resource-Efficient Development: Ideal for developers looking to deploy Mistral-based models with optimized training pipelines.
- Long-Context Tasks: Its large context window makes it suitable for applications requiring the processing of extensive textual information.