Hyeongwon/P19-split2-prob-6x-bs128-lr2e5-zero3-ep3
Hyeongwon/P19-split2-prob-6x-bs128-lr2e5-zero3-ep3 is a 4 billion parameter language model developed by Hyeongwon, fine-tuned from Hyeongwon/Qwen3-4B-Base. This model was trained using Supervised Fine-Tuning (SFT) with the TRL framework, specializing in text generation tasks. It is designed for general text generation, demonstrated by its quick start example for answering open-ended questions.
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Model Overview
Hyeongwon/P19-split2-prob-6x-bs128-lr2e5-zero3-ep3 is a 4 billion parameter language model, fine-tuned by Hyeongwon from the base model Hyeongwon/Qwen3-4B-Base. This model leverages the TRL (Transformer Reinforcement Learning) framework for its training process, specifically utilizing Supervised Fine-Tuning (SFT).
Key Capabilities
- Text Generation: The model is proficient in generating coherent and contextually relevant text based on given prompts.
- Instruction Following: Demonstrated by its ability to respond to open-ended questions, suggesting a capacity for following user instructions.
- Fine-tuned Performance: As a fine-tuned version, it is expected to exhibit improved performance on tasks aligned with its training data compared to its base model.
Training Details
The model was trained using SFT, a common method for adapting pre-trained language models to specific tasks or domains. The training process utilized several key frameworks:
- TRL: 0.25.1
- Transformers: 4.57.3
- Pytorch: 2.9.1
- Datasets: 3.6.0
- Tokenizers: 0.22.2
Further details on the training run can be visualized via Weights & Biases, linked in the original model card.
Intended Use
This model is suitable for various text generation applications, including but not limited to, answering questions, creative writing, and conversational AI components where a 4 billion parameter model is appropriate for resource constraints and performance requirements.