abcorrea/sok-v3
abcorrea/sok-v3 is a 4 billion parameter language model fine-tuned from Qwen/Qwen3-4B-Thinking-2507. This model was trained using Supervised Fine-Tuning (SFT) with the TRL framework. It is designed for general text generation tasks, leveraging the Qwen3 architecture for efficient performance. The model has a context length of 32768 tokens, making it suitable for processing longer inputs.
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Model Overview
abcorrea/sok-v3 is a 4 billion parameter language model, specifically a fine-tuned variant of the Qwen/Qwen3-4B-Thinking-2507 architecture. This model has undergone Supervised Fine-Tuning (SFT) utilizing the TRL framework, a library developed by Hugging Face for transformer reinforcement learning. The training process involved specific versions of key machine learning libraries, including TRL 0.19.1, Transformers 4.52.1, Pytorch 2.7.0, Datasets 4.0.0, and Tokenizers 0.21.1.
Key Capabilities
- Text Generation: Capable of generating coherent and contextually relevant text based on provided prompts.
- Qwen3 Architecture: Benefits from the underlying Qwen3 model's design for efficient language processing.
- Supervised Fine-Tuning: Optimized through SFT, suggesting improved performance on tasks aligned with its training data.
Good For
- General Purpose Language Tasks: Suitable for a wide range of applications requiring text generation.
- Developers using TRL: Provides an example of a model fine-tuned with the TRL framework, potentially useful for those exploring similar training methodologies.
- Exploration of Qwen3-based Models: Offers a fine-tuned instance of the Qwen3-4B-Thinking-2507 model for evaluation and deployment.