Hyeongwon/P2-split2_prob_rg_v2_Qwen3-4B-Base-0415v2
Hyeongwon/P2-split2_prob_rg_v2_Qwen3-4B-Base-0415v2 is a 4 billion parameter language model developed by Hyeongwon, fine-tuned from Hyeongwon/Qwen3-4B-Base. This model was trained using SFT (Supervised Fine-Tuning) with the TRL framework, and is designed for general text generation tasks. It offers a context length of 32768 tokens, making it suitable for applications requiring processing of moderately long inputs.
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Model Overview
Hyeongwon/P2-split2_prob_rg_v2_Qwen3-4B-Base-0415v2 is a 4 billion parameter language model, fine-tuned by Hyeongwon from its base model, Hyeongwon/Qwen3-4B-Base. This model leverages the Qwen3 architecture and has been specifically trained using Supervised Fine-Tuning (SFT) with the TRL (Transformer Reinforcement Learning) framework.
Key Characteristics
- Base Model: Fine-tuned from Hyeongwon/Qwen3-4B-Base.
- Training Method: Utilizes Supervised Fine-Tuning (SFT) for its training procedure.
- Framework: Developed using the TRL library, indicating a focus on efficient transformer training.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing and generation of longer texts.
Use Cases
This model is well-suited for general text generation tasks where a 4 billion parameter model with a large context window is beneficial. Its SFT training suggests it can follow instructions and generate coherent responses based on provided prompts. Developers can integrate it into applications requiring text completion, question answering, or content creation, particularly when leveraging the transformers library for quick deployment.