Omaratef3221/llama-3.1-8b-s1-full-s2-full-medarabench
The Omaratef3221/llama-3.1-8b-s1-full-s2-full-medarabench is an 8 billion parameter Llama 3.1-based language model, fine-tuned from omaratef3221/llama-3.1-8b-s1-full-aramed. This model has been specifically trained using Supervised Fine-Tuning (SFT) with the TRL framework, focusing on enhancing its capabilities for specific applications. It is designed to build upon its base model's performance, offering improved utility in its target domain.
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Model Overview
This model, Omaratef3221/llama-3.1-8b-s1-full-s2-full-medarabench, is an 8 billion parameter language model built upon the Llama 3.1 architecture. It represents a second stage of fine-tuning, specifically derived from the omaratef3221/llama-3.1-8b-s1-full-aramed base model.
Key Capabilities
- Supervised Fine-Tuning (SFT): The model has undergone Supervised Fine-Tuning using the TRL framework, indicating a focus on improving performance for specific tasks through example-based learning.
- Llama 3.1 Base: Inherits the foundational capabilities and architecture of the Llama 3.1 series, providing a strong base for language understanding and generation.
Training Details
The fine-tuning process utilized the TRL (Transformers Reinforcement Learning) library, a popular framework for training transformer models. The training environment included specific versions of key libraries:
- TRL: 1.0.0
- Transformers: 5.5.1
- Pytorch: 2.6.0
- Datasets: 4.8.4
- Tokenizers: 0.22.2
Usage
Developers can integrate this model using the Hugging Face transformers library, as demonstrated in the quick start example for text generation tasks. The model is suitable for applications requiring a fine-tuned Llama 3.1 variant with an 8192-token context length.