Hyeongwon/P2-split2_complete_independent_Qwen3-4B-Base_0425-bs64-epoch3
The Hyeongwon/P2-split2_complete_independent_Qwen3-4B-Base_0425-bs64-epoch3 is a 4 billion parameter language model, fine-tuned from Hyeongwon/Qwen3-4B-Base using the TRL framework. This model has a context length of 32768 tokens and is specifically trained via Supervised Fine-Tuning (SFT). It is designed for general text generation tasks, building upon its Qwen3-4B-Base foundation.
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Model Overview
This model, Hyeongwon/P2-split2_complete_independent_Qwen3-4B-Base_0425-bs64-epoch3, is a 4 billion parameter language model derived from the Hyeongwon/Qwen3-4B-Base architecture. It has been fine-tuned using the TRL (Transformer Reinforcement Learning) framework, specifically employing a Supervised Fine-Tuning (SFT) approach.
Key Characteristics
- Base Model: Fine-tuned from
Hyeongwon/Qwen3-4B-Base. - Parameter Count: 4 billion parameters.
- Context Length: Supports a substantial context window of 32768 tokens.
- Training Method: Utilizes Supervised Fine-Tuning (SFT) for its training procedure.
- Frameworks: Developed with TRL (version 0.25.1), Transformers (version 4.57.3), Pytorch (version 2.6.0), Datasets (version 3.6.0), and Tokenizers (version 0.22.2).
Intended Use Cases
This model is suitable for various text generation tasks, leveraging its SFT training to produce coherent and contextually relevant outputs. Its large context window makes it particularly useful for applications requiring understanding and generation over longer passages of text.