jekunz/Qwen3-1.7B-Base-sv-CPT-sv-SmolTalk
TEXT GENERATIONConcurrency Cost:1Model Size:2BQuant:BF16Ctx Length:32kPublished:Apr 24, 2026Architecture:Transformer Cold
The jekunz/Qwen3-1.7B-Base-sv-CPT-sv-SmolTalk model is a 2 billion parameter language model, fine-tuned using the TRL framework. This model is based on an unspecified architecture and was trained with Supervised Fine-Tuning (SFT). It is designed for text generation tasks, offering a compact size suitable for various applications.
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Model Overview
The jekunz/Qwen3-1.7B-Base-sv-CPT-sv-SmolTalk is a 2 billion parameter language model that has been fine-tuned using the TRL library. The training process involved Supervised Fine-Tuning (SFT).
Key Capabilities
- Text Generation: The model is primarily designed for generating text based on given prompts, as demonstrated by its quick start example for question answering.
- Compact Size: With 2 billion parameters, it offers a relatively small footprint compared to larger models, potentially allowing for more efficient deployment.
Training Details
The model was trained using SFT, leveraging specific versions of popular machine learning frameworks:
- TRL: 0.25.1
- Transformers: 4.57.3
- Pytorch: 2.9.1
- Datasets: 4.4.1
- Tokenizers: 0.22.1
Good For
- Developers looking for a fine-tuned model for text generation tasks.
- Applications where a smaller parameter count is beneficial for resource constraints.
- Experimentation with models trained via Supervised Fine-Tuning using the TRL framework.