jekunz/Gemma-3-1B-pt-sv-CPT-plus-IR-sv-SmolTalk
The jekunz/Gemma-3-1B-pt-sv-CPT-plus-IR-sv-SmolTalk model is a 1 billion parameter language model, fine-tuned from a Gemma base model. This model has been specifically trained using SFT with TRL, focusing on Swedish language tasks. It is designed for text generation applications requiring a compact yet capable model with a 32768 token context length.
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Model Overview
The jekunz/Gemma-3-1B-pt-sv-CPT-plus-IR-sv-SmolTalk is a 1 billion parameter language model, fine-tuned from a Gemma base model. This model leverages the Transformer Reinforcement Learning (TRL) framework for its training, specifically employing Supervised Fine-Tuning (SFT) techniques. It is designed to handle text generation tasks with a substantial context window of 32768 tokens.
Key Capabilities
- Text Generation: Capable of generating coherent and contextually relevant text based on user prompts.
- Swedish Language Focus: While the base model is Gemma, the fine-tuning process suggests an optimization for Swedish language understanding and generation, making it suitable for applications requiring proficiency in Swedish.
- Efficient Deployment: With 1 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 was trained using the TRL library (version 0.25.1) with Transformers (4.57.3), Pytorch (2.9.1), Datasets (4.4.1), and Tokenizers (0.22.1). The training procedure involved Supervised Fine-Tuning (SFT).
Good For
- Applications requiring text generation in Swedish.
- Scenarios where a smaller, efficient language model with a decent context window is preferred.
- Exploratory text generation tasks and prototyping.