Hyeongwon/P2-split4_only_answer_Qwen3-4B-Base_0505-bs64-epoch6-lr1e5
Hyeongwon/P2-split4_only_answer_Qwen3-4B-Base_0505-bs64-epoch6-lr1e5 is a 4 billion parameter language model fine-tuned from Hyeongwon/Qwen3-4B-Base. This model was trained using Supervised Fine-Tuning (SFT) with the TRL framework. It is designed for text generation tasks, specifically for providing direct answers to questions. Its fine-tuned nature suggests an optimization for conversational or question-answering applications.
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Model Overview
Hyeongwon/P2-split4_only_answer_Qwen3-4B-Base_0505-bs64-epoch6-lr1e5 is a 4 billion parameter language model, fine-tuned from the base model Hyeongwon/Qwen3-4B-Base. This model has undergone Supervised Fine-Tuning (SFT) using the TRL framework, indicating a focus on specific task performance rather than broad general capabilities.
Key Characteristics
- Base Model: Fine-tuned from Hyeongwon/Qwen3-4B-Base.
- Training Method: Utilizes Supervised Fine-Tuning (SFT) for specialized performance.
- Framework: Trained with the TRL (Transformer Reinforcement Learning) library, version 0.25.1.
- Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
Intended Use Cases
This model is particularly suited for applications requiring direct and concise answers to user queries. Its fine-tuned nature suggests it excels in scenarios where the model needs to generate specific responses rather than open-ended creative text. Developers can integrate this model for tasks such as:
- Question Answering: Providing direct answers to factual or conceptual questions.
- Conversational AI: Generating relevant responses in dialogue systems where the goal is to answer user prompts.
- Text Generation: Creating focused text outputs based on a given prompt, as demonstrated in the quick start example.
Technical Details
The model was developed using specific versions of key frameworks:
- TRL: 0.25.1
- Transformers: 4.57.3
- Pytorch: 2.9.1
- Datasets: 3.6.0
- Tokenizers: 0.22.2