Hyeongwon/P2-split4_prob_Qwen3-4B-Base_0312-01
The Hyeongwon/P2-split4_prob_Qwen3-4B-Base_0312-01 model is a 4 billion parameter language model developed by Hyeongwon, fine-tuned from the Qwen3-4B-Base architecture. This model has been trained using Supervised Fine-Tuning (SFT) with the TRL framework, focusing on enhancing its probabilistic generation capabilities. With a context length of 32768 tokens, it is designed for text generation tasks where nuanced and contextually relevant responses are crucial.
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Model Overview
Hyeongwon/P2-split4_prob_Qwen3-4B-Base_0312-01 is a 4 billion parameter language model, fine-tuned by Hyeongwon from the base Qwen3-4B-Base architecture. This model leverages the TRL (Transformer Reinforcement Learning) framework for its training, specifically employing Supervised Fine-Tuning (SFT).
Key Characteristics
- Base Model: Fine-tuned from Hyeongwon/Qwen3-4B-Base.
- Training Framework: Utilizes the TRL library for efficient fine-tuning.
- Training Method: Trained using Supervised Fine-Tuning (SFT).
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing and generating longer sequences of text.
- Development Environment: Developed with specific versions of key libraries including TRL 0.25.1, Transformers 4.57.3, Pytorch 2.6.0, Datasets 3.6.0, and Tokenizers 0.22.2.
Use Cases
This model is particularly well-suited for text generation tasks that benefit from a fine-tuned base model and a large context window. Its SFT training suggests an optimization for generating coherent and contextually appropriate responses, making it suitable for applications requiring detailed and nuanced text outputs.