akseljoonas/Qwen3-1.7B-SFT-s1K-lr0_0001
The akseljoonas/Qwen3-1.7B-SFT-s1K-lr0_0001 is a 2 billion parameter language model, fine-tuned from the Qwen3-1.7B-Base architecture. Developed by akseljoonas, this model was trained using Supervised Fine-Tuning (SFT) on the simplescaling/s1K dataset. With a context length of 32768 tokens, it is designed for general text generation tasks, leveraging its fine-tuned capabilities for conversational responses.
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Model Overview
The akseljoonas/Qwen3-1.7B-SFT-s1K-lr0_0001 is a 2 billion parameter language model derived from the Qwen3-1.7B-Base architecture. It has been specifically fine-tuned using Supervised Fine-Tuning (SFT) on the simplescaling/s1K dataset, aiming to enhance its performance in conversational and text generation tasks.
Key Characteristics
- Base Model: Qwen3-1.7B-Base
- Parameter Count: Approximately 2 billion parameters
- Context Length: Supports a substantial context window of 32768 tokens
- Training Method: Supervised Fine-Tuning (SFT) using the TRL library
- Dataset: Fine-tuned on the simplescaling/s1K dataset
Use Cases
This model is suitable for various text generation applications, particularly those benefiting from its fine-tuning on the s1K dataset. Its capabilities include generating coherent and contextually relevant responses to prompts, making it a candidate for:
- General conversational AI
- Question answering
- Creative text generation
Developers can easily integrate and experiment with this model using the Hugging Face transformers library, as demonstrated in the quick start example provided in its model card.