Hyeongwon/P2-split2_prob_rg_v2_Qwen3-4B-Base-0415
Hyeongwon/P2-split2_prob_rg_v2_Qwen3-4B-Base-0415 is a 4 billion parameter language model developed by Hyeongwon, fine-tuned from the Qwen3-4B-Base architecture. This model was trained using Supervised Fine-Tuning (SFT) with the TRL framework, specializing in text generation tasks. It features a notable context length of 32768 tokens, making it suitable for applications requiring extensive contextual understanding.
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Model Overview
Hyeongwon/P2-split2_prob_rg_v2_Qwen3-4B-Base-0415 is a 4 billion parameter language model, fine-tuned by Hyeongwon from the base Qwen3-4B-Base architecture. This model leverages a substantial context length of 32768 tokens, enhancing its ability to process and generate coherent text over long inputs.
Key Capabilities
- Text Generation: Optimized for generating responses based on given prompts, as demonstrated by its quick start example for conversational questions.
- Fine-tuned Performance: Developed through Supervised Fine-Tuning (SFT) using the TRL library, indicating a focus on specific task performance rather than broad pre-training.
- Extensive Context Window: Benefits from a 32K context length, allowing for more detailed and contextually aware outputs.
Training Details
The model was trained using SFT, with the process visualized via Weights & Biases. The training utilized specific versions of key frameworks:
- TRL: 0.25.1
- Transformers: 4.57.3
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.22.2
Recommended Use Cases
This model is well-suited for applications requiring robust text generation, particularly where the ability to handle long input contexts is beneficial. Its fine-tuned nature suggests potential for specialized conversational agents or content creation tools.