woojun-jung/qwen3-0.6b-bitext-ticket-router-sft
The woojun-jung/qwen3-0.6b-bitext-ticket-router-sft model is a fine-tuned version of Qwen/Qwen3-0.6B, a 0.8 billion parameter language model with a 32768 token context length. Developed by woojun-jung, this model has been specifically trained using Supervised Fine-Tuning (SFT) with the TRL framework. It is designed for text generation tasks, leveraging its fine-tuned capabilities to produce coherent and contextually relevant responses.
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Model Overview
This model, woojun-jung/qwen3-0.6b-bitext-ticket-router-sft, is a specialized version of the Qwen3-0.6B architecture, developed by woojun-jung. It has been fine-tuned using the TRL (Transformers Reinforcement Learning) framework, specifically employing Supervised Fine-Tuning (SFT) techniques.
Key Capabilities
- Text Generation: Excels at generating human-like text based on given prompts.
- Fine-tuned Performance: Benefits from targeted SFT, suggesting improved performance on specific tasks compared to its base model.
- Qwen3-0.6B Base: Built upon the Qwen3-0.6B model, providing a solid foundation for language understanding and generation.
Training Details
The model's training procedure involved Supervised Fine-Tuning (SFT) using the TRL framework. The development utilized specific versions of key libraries:
- TRL: 1.0.0
- Transformers: 5.5.0
- Pytorch: 2.11.0
- Datasets: 4.8.4
- Tokenizers: 0.22.2
Good For
- General Text Generation: Suitable for various text generation tasks where a compact yet capable model is required.
- Exploration of SFT Models: Provides an example of a model fine-tuned with TRL's SFT capabilities.