Hyeongwon/P2-split5_prob_Llama-3.2-3B-Base_0524-1
Hyeongwon/P2-split5_prob_Llama-3.2-3B-Base_0524-1 is a 3.2 billion parameter causal language model, fine-tuned from the meta-llama/Llama-3.2-3B architecture. This model was trained using Supervised Fine-Tuning (SFT) with TRL, offering a 32768 token context length. It is designed for general text generation tasks, leveraging its Llama-3.2 base for robust language understanding and generation capabilities.
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Model Overview
Hyeongwon/P2-split5_prob_Llama-3.2-3B-Base_0524-1 is a 3.2 billion parameter language model built upon the meta-llama/Llama-3.2-3B architecture. This model has undergone Supervised Fine-Tuning (SFT) using the TRL library (version 0.25.1), enhancing its base capabilities for various text generation tasks. It supports a substantial context length of 32768 tokens, allowing for processing and generating longer sequences of text.
Key Capabilities
- Text Generation: Capable of generating coherent and contextually relevant text based on given prompts.
- Llama-3.2 Base: Benefits from the foundational strengths of the Llama-3.2 series, including strong language understanding.
- Fine-tuned Performance: Optimized through SFT for improved performance in general conversational and generative applications.
Training Details
The model was trained using the TRL framework, with specific versions of key libraries:
- TRL: 0.25.1
- Transformers: 4.57.3
- Pytorch: 2.9.1
- Datasets: 3.6.0
- Tokenizers: 0.22.2
Good For
- General-purpose text generation.
- Applications requiring a compact yet capable language model.
- Developers looking for a fine-tuned Llama-3.2 variant for custom tasks.