ESarp/Llama-3-8B-AttackTree-DPO
ESarp/Llama-3-8B-AttackTree-DPO is an 8 billion parameter Llama-3 based causal language model developed by ESarp. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for specific applications related to attack tree generation or analysis, leveraging its optimized training process.
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Model Overview
ESarp/Llama-3-8B-AttackTree-DPO is an 8 billion parameter language model, fine-tuned by ESarp. It is based on the Llama-3 architecture and was specifically trained using Unsloth and Huggingface's TRL library. This training methodology allowed for a 2x faster fine-tuning process compared to standard methods.
Key Characteristics
- Base Model: Llama-3-8B-Instruct
- Parameter Count: 8 billion
- Context Length: 8192 tokens
- Training Optimization: Utilizes Unsloth for accelerated fine-tuning.
- License: Apache-2.0
Use Cases
This model is particularly suited for tasks where the specific fine-tuning data (implied by "AttackTree" in the name) is relevant. Its optimized training process makes it an efficient choice for applications requiring a Llama-3-8B model with specialized knowledge, potentially in security analysis or risk assessment domains involving attack trees.