jekunz/Qwen3-1.7B-Base-sv-SmolTalk
jekunz/Qwen3-1.7B-Base-sv-SmolTalk is a 2 billion parameter language model fine-tuned from Qwen/Qwen3-1.7B-Base. This model was trained using SFT with the TRL framework, offering a 32768 token context length. It is a specialized variant of the Qwen3-1.7B-Base model, focusing on specific applications through its fine-tuning process.
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Model Overview
This model, jekunz/Qwen3-1.7B-Base-sv-SmolTalk, is a fine-tuned iteration of the Qwen/Qwen3-1.7B-Base architecture. With approximately 2 billion parameters and a substantial context length of 32768 tokens, it builds upon the foundational capabilities of the Qwen3 series.
Key Characteristics
- Base Model: Derived from
Qwen/Qwen3-1.7B-Base. - Parameter Count: Approximately 2 billion parameters.
- Context Length: Supports a context window of 32768 tokens.
- Training Method: Fine-tuned using Supervised Fine-Tuning (SFT) with the TRL library.
Intended Use
This model is suitable for applications requiring a compact yet capable language model with a large context window, particularly those benefiting from its specific fine-tuning. Developers can integrate it using the Hugging Face transformers library for text generation tasks.