Hyeongwon/P2-split2_prob_rg_Qwen3-4B-Base
Hyeongwon/P2-split2_prob_rg_Qwen3-4B-Base is a 4 billion parameter language model, fine-tuned from Hyeongwon/Qwen3-4B-Base using the TRL library. This model is specifically trained with Supervised Fine-Tuning (SFT) and features a context length of 32768 tokens. It is designed for general text generation tasks, building upon the base Qwen3 architecture.
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Model Overview
Hyeongwon/P2-split2_prob_rg_Qwen3-4B-Base is a 4 billion parameter language model, fine-tuned from the base model Hyeongwon/Qwen3-4B-Base. This model leverages the TRL (Transformer Reinforcement Learning) library for its training process, specifically utilizing Supervised Fine-Tuning (SFT).
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: Trained using Supervised Fine-Tuning (SFT) with the TRL framework.
Usage
This model is suitable for various text generation tasks. A quick start example is provided for generating responses to user prompts using the transformers pipeline. The training procedure and framework versions, including TRL 0.25.1 and Transformers 4.57.3, are detailed for reproducibility.