prog-love/rostam-r1-stage13
prog-love/rostam-r1-stage13 is a 5.1 billion parameter causal language model developed by prog-love, fine-tuned from prog-love/rostam-r1-stage12-part2. This model was trained using SFT with the TRL framework, offering a 32768 token context length. It is designed for general text generation tasks, building upon its predecessor's capabilities.
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Model Overview
prog-love/rostam-r1-stage13 is a 5.1 billion parameter language model, representing a fine-tuned iteration of prog-love/rostam-r1-stage12-part2. It leverages a substantial context length of 32768 tokens, making it suitable for processing longer inputs and generating coherent, extended text.
Key Capabilities
- Text Generation: Excels at generating human-like text based on given prompts, as demonstrated by its quick start example for conversational questions.
- Fine-tuned Performance: Built upon a previous stage, indicating iterative improvements and specialized training for enhanced performance in its intended applications.
- Long Context Handling: With a 32768 token context window, it can maintain context over extensive conversations or documents, crucial for complex tasks.
Training Details
This model was trained using Supervised Fine-Tuning (SFT), a common method for adapting pre-trained language models to specific tasks or datasets. The training utilized the TRL (Transformers Reinforcement Learning) framework, indicating a robust and well-supported training pipeline. Key framework versions used include TRL 1.9.2, Transformers 5.15.0, Pytorch 2.11.0, Datasets 5.0.1, and Tokenizers 0.22.2.
Good For
- General-purpose text generation and completion.
- Applications requiring understanding and generation of longer text sequences.
- Developers looking for a fine-tuned model with a strong base and extended context capabilities.