akseljoonas/Qwen3-1.7B-SFT-s1K-lr1eneg05
The akseljoonas/Qwen3-1.7B-SFT-s1K-lr1eneg05 model is a 1.7 billion parameter language model based on the Qwen3-1.7B-Base architecture. It has been fine-tuned using Supervised Fine-Tuning (SFT) on the simplescaling/s1K dataset, leveraging the TRL library. This model 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-lr1eneg05 is a 1.7 billion parameter language model, fine-tuned from the Qwen/Qwen3-1.7B-Base architecture. This model was developed by akseljoonas and specifically trained using Supervised Fine-Tuning (SFT) on the simplescaling/s1K dataset.
Key Capabilities
- Text Generation: Capable of generating coherent and contextually relevant text based on given prompts.
- Instruction Following: Fine-tuned with SFT, suggesting an ability to follow instructions for various text-based tasks.
- Compact Size: With 1.7 billion parameters, it offers a balance between performance and computational efficiency, making it suitable for deployment in environments with resource constraints.
Training Details
The model's training procedure utilized the TRL (Transformers Reinforcement Learning) library, indicating a focus on optimizing conversational or instruction-following capabilities. The training leveraged specific versions of key frameworks:
- TRL: 0.29.0
- Transformers: 5.2.0
- Pytorch: 2.10.0
- Datasets: 4.6.0
- Tokenizers: 0.22.2
Good For
- General-purpose text generation: Suitable for tasks like creative writing, question answering, and conversational AI where a smaller model is preferred.
- Prototyping and experimentation: Its compact size allows for quicker iteration and experimentation on various NLP tasks.
- Applications requiring efficient inference: Ideal for scenarios where computational resources are limited, but a capable language model is still needed.