void-818/Affine-707-5EeXiJNN6ohYoTixu94VEGvoRwMF7NCTjTpotW5wN7qaB5DQ
Affine-707-5EeXiJNN6ohYoTixu94VEGvoRwMF7NCTjTpotW5wN7qaB5DQ by void-818 is a 32 billion parameter language model with a 32768 token context length. This model is a fine-tuned version of an unspecified base model, trained using the TRL framework. It is designed for general text generation tasks, leveraging Supervised Fine-Tuning (SFT) for its capabilities.
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Model Overview
Affine-707-5EeXiJNN6ohYoTixu94VEGvoRwMF7NCTjTpotW5wN7qaB5DQ is a 32 billion parameter language model developed by void-818, featuring a substantial context window of 32768 tokens. This model is a fine-tuned iteration of an undisclosed base model, with its training process specifically utilizing the TRL (Transformers Reinforcement Learning) framework.
Key Capabilities
- Text Generation: Capable of generating coherent and contextually relevant text based on provided prompts.
- Supervised Fine-Tuning (SFT): The model's training methodology, SFT, indicates a focus on learning from labeled data to perform specific tasks.
Training Details
The model was trained using Supervised Fine-Tuning (SFT) within the TRL framework. The development environment included:
- TRL: 0.29.1
- Transformers: 5.3.0
- Pytorch: 2.11.0
- Datasets: 4.8.4
- Tokenizers: 0.22.2
Intended Use
This model is suitable for various text generation applications where a large parameter count and extensive context window are beneficial. Its SFT training suggests it can be adapted for tasks requiring specific output formats or styles learned from fine-tuning data.