akseljoonas/Qwen3-1.7B-SFT-s1K-lr2eneg05
The akseljoonas/Qwen3-1.7B-SFT-s1K-lr2eneg05 is a 1.7 billion parameter causal language model, fine-tuned from Qwen/Qwen3-1.7B-Base. This model was specifically trained using Supervised Fine-Tuning (SFT) on the simplescaling/s1K dataset, leveraging the TRL framework. It is designed for general text generation tasks, offering a compact yet capable solution for various natural language processing applications.
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Model Overview
The akseljoonas/Qwen3-1.7B-SFT-s1K-lr2eneg05 is a 1.7 billion parameter language model derived from the Qwen3-1.7B-Base architecture. It has undergone Supervised Fine-Tuning (SFT) using the TRL framework, specifically utilizing the simplescaling/s1K dataset.
Key Capabilities
- General Text Generation: Capable of generating coherent and contextually relevant text based on given prompts.
- Fine-tuned Performance: Benefits from SFT on a specialized dataset, which can enhance its performance on tasks aligned with the training data's characteristics.
- Efficient Deployment: As a 1.7 billion parameter model, it offers a balance between performance and computational efficiency, making it suitable for applications where resource constraints are a consideration.
Training Details
The model was trained using the TRL (Transformers Reinforcement Learning) library, version 0.29.0, with Transformers 5.2.0, Pytorch 2.10.0, Datasets 4.6.0, and Tokenizers 0.22.2. The training process involved fine-tuning the base Qwen3-1.7B model on the simplescaling/s1K dataset.
Good for
- Developers looking for a compact, fine-tuned Qwen3-based model for text generation.
- Applications requiring a balance of performance and lower computational overhead.
- Experimentation with models fine-tuned on the
simplescaling/s1Kdataset.