SaketR1/uncertainty-sft-correct-ambiguous-mixed-clear
SaketR1/uncertainty-sft-correct-ambiguous-mixed-clear is a 2.3 billion parameter instruction-tuned causal language model, fine-tuned from Qwen/Qwen3.5-2B. This model has been trained using SFT (Supervised Fine-Tuning) with the TRL library. It is designed to generate text based on user prompts, demonstrating capabilities in conversational response generation.
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Model Overview
This model, uncertainty-sft-correct-ambiguous-mixed-clear, is a 2.3 billion parameter language model developed by SaketR1. It is built upon the robust architecture of Qwen/Qwen3.5-2B and has undergone Supervised Fine-Tuning (SFT) using the Hugging Face TRL library. The fine-tuning process aims to enhance its ability to generate coherent and contextually relevant text based on diverse user inputs.
Key Capabilities
- Instruction Following: Generates responses based on explicit instructions provided in the prompt.
- Text Generation: Capable of producing human-like text for various conversational scenarios.
- Foundation Model: Leverages the capabilities of the Qwen3.5-2B base model, known for its strong language understanding.
Training Details
The model was trained using the SFT method, which involves fine-tuning on a dataset of input-output pairs to teach specific behaviors. The training utilized TRL version 1.6.0, Transformers 5.13.0.dev0, Pytorch 2.11.0+cu128, Datasets 5.0.0, and Tokenizers 0.22.2. This setup ensures a modern and efficient training pipeline.
Use Cases
This model is suitable for applications requiring:
- Conversational AI: Generating responses in chatbots or interactive agents.
- Content Creation: Assisting with drafting text based on specific prompts.
- Exploratory Text Generation: Experimenting with language model outputs for research or development.