idopinto/qwen3-14b-full-nt-gen-inv-sft-v2-g3-e3
The idopinto/qwen3-14b-full-nt-gen-inv-sft-v2-g3-e3 is a 14 billion parameter language model, fine-tuned from Qwen/Qwen3-14B using Supervised Fine-Tuning (SFT) with the TRL library. This model is designed for general text generation tasks, leveraging its 32768 token context length for comprehensive understanding and response generation. Its training methodology focuses on enhancing its ability to produce coherent and contextually relevant outputs.
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Model Overview
The idopinto/qwen3-14b-full-nt-gen-inv-sft-v2-g3-e3 is a 14 billion parameter language model built upon the robust Qwen3-14B architecture. It has undergone Supervised Fine-Tuning (SFT) using the TRL library, a framework specifically designed for Transformer Reinforcement Learning.
Key Capabilities
- General Text Generation: Excels at producing diverse and contextually appropriate text based on user prompts.
- Fine-tuned Performance: Benefits from SFT, which refines its ability to follow instructions and generate high-quality responses.
- Large Context Window: Utilizes a 32768 token context length, allowing it to process and generate longer, more complex narratives and discussions.
Training Details
This model was trained using the SFT method, indicating a focus on learning from high-quality, labeled data to improve its generative capabilities. The training process leveraged specific versions of key machine learning frameworks, including TRL 0.24.0, Transformers 4.57.3, Pytorch 2.9.0, Datasets 4.3.0, and Tokenizers 0.22.1. Further details on the training run can be visualized via Weights & Biases.