SaketR1/uncertainty-sft
The SaketR1/uncertainty-sft model is a 2.3 billion parameter language model, fine-tuned from Qwen/Qwen3.5-2B using the TRL framework. It features a 32,768 token context length, making it suitable for tasks requiring extensive contextual understanding. This model is specifically trained via Supervised Fine-Tuning (SFT) to enhance its conversational and generative capabilities.
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Overview
The SaketR1/uncertainty-sft model is a 2.3 billion parameter language model, derived from the Qwen/Qwen3.5-2B base model. It has been fine-tuned using the TRL library through a Supervised Fine-Tuning (SFT) procedure. This model is designed to handle a context length of up to 32,768 tokens, allowing for processing and generating longer sequences of text.
Key Capabilities
- Text Generation: Capable of generating coherent and contextually relevant text based on user prompts.
- Conversational AI: Fine-tuned for interactive dialogue, making it suitable for chatbot applications or question-answering systems.
- Extended Context: Benefits from a large 32,768 token context window, enabling it to maintain context over lengthy conversations or documents.
Training Details
The model underwent Supervised Fine-Tuning (SFT) using the TRL framework. The training environment utilized specific versions of key libraries:
- TRL: 1.5.1
- Transformers: 5.10.0.dev0
- Pytorch: 2.11.0+cu128
- Datasets: 5.0.0
- Tokenizers: 0.22.2
When to Use
This model is a good candidate for applications requiring a compact yet capable language model with strong text generation and conversational abilities, especially when dealing with longer input contexts.