SaketR1/uncertainty-sft-all-correct-balanced
VISIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:2.3BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 1, 2026Architecture:Transformer Featherless Exclusive Cold
SaketR1/uncertainty-sft-all-correct-balanced is a 2.3 billion parameter language model fine-tuned from Qwen/Qwen3.5-2B. This model was trained using Supervised Fine-Tuning (SFT) with the TRL library, focusing on generating responses to complex, open-ended questions. With a context length of 32768 tokens, it is designed for conversational applications requiring nuanced and detailed text generation.
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Model Overview
SaketR1/uncertainty-sft-all-correct-balanced is a 2.3 billion parameter language model, fine-tuned from the base model Qwen/Qwen3.5-2B. This model leverages a substantial context length of 32768 tokens, enabling it to process and generate longer, more coherent responses.
Key Capabilities
- Supervised Fine-Tuning (SFT): The model was trained using Supervised Fine-Tuning, a method that optimizes its ability to follow instructions and generate relevant text based on provided examples.
- TRL Integration: Training was conducted using the TRL library, a framework for Transformer Reinforcement Learning, indicating a focus on improving response quality and alignment.
- Text Generation: It is specifically designed for text generation tasks, as demonstrated by its quick start example which involves generating a detailed response to a philosophical question.
Good For
- Conversational AI: Its fine-tuning and context window make it suitable for applications requiring extended dialogue and nuanced responses.
- Question Answering: The model can generate comprehensive answers to open-ended and complex questions, as shown in its example use case.
- Research and Development: Developers can use this model as a base for further experimentation with SFT and TRL techniques, particularly for tasks involving detailed text output.