SaketR1/uncertainty-sft-mix-clear-corr-amb-balanced
SaketR1/uncertainty-sft-mix-clear-corr-amb-balanced is a 2.3 billion parameter language model, fine-tuned from Qwen/Qwen3.5-2B. This model, trained using the TRL framework, specializes in text generation tasks, particularly for conversational or question-answering contexts. It leverages a 32768-token context length to process and generate coherent and contextually relevant responses.
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Model Overview
SaketR1/uncertainty-sft-mix-clear-corr-amb-balanced is a 2.3 billion parameter language model derived from the Qwen/Qwen3.5-2B architecture. It has been fine-tuned using the TRL (Transformers Reinforcement Learning) framework, indicating a focus on instruction-following or conversational capabilities through Supervised Fine-Tuning (SFT).
Key Capabilities
- Text Generation: Excels at generating human-like text based on given prompts.
- Conversational AI: Suitable for question-answering and dialogue systems, as demonstrated by its example usage.
- Context Handling: Benefits from a substantial 32768-token context window, allowing for more extended and coherent interactions.
Training Details
The model underwent Supervised Fine-Tuning (SFT) to adapt its base capabilities to specific tasks. The training utilized TRL version 1.10.0, Transformers 5.16.0.dev0, Pytorch 2.11.0+cu128, Datasets 5.0.1, and Tokenizers 0.23.1.
Intended Use Cases
This model is well-suited for applications requiring robust text generation, particularly in scenarios where understanding and responding to nuanced prompts is crucial. Its fine-tuned nature suggests improved performance in interactive or instruction-based tasks compared to its base model.