formalmathatepfl/qwen3-8b-sft-feedback
The formalmathatepfl/qwen3-8b-sft-feedback model is an 8 billion parameter language model fine-tuned from Qwen/Qwen3-8B. This model is specifically fine-tuned on an sft dataset, indicating an optimization for supervised fine-tuning tasks. It is designed for applications requiring a refined Qwen3-8B base with improved performance on its specific training data, achieving a validation loss of 0.0228.
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Model Overview
The formalmathatepfl/qwen3-8b-sft-feedback model is an 8 billion parameter language model, fine-tuned from the base model Qwen/Qwen3-8B. This fine-tuning process utilized an sft (supervised fine-tuning) dataset, aiming to enhance its performance on specific tasks related to the training data.
Key Characteristics
- Base Model: Built upon the robust Qwen3-8B architecture.
- Fine-tuning Objective: Optimized through supervised fine-tuning on a dedicated sft dataset.
- Performance: Achieved a final validation loss of 0.0228 during training, indicating effective learning on the fine-tuning data.
Training Details
The model was trained with the following notable hyperparameters:
- Learning Rate: 1e-05
- Optimizer: ADAMW_TORCH
- Epochs: 1.0
- Mixed Precision: Native AMP was used for training efficiency.
Intended Use Cases
While specific intended uses and limitations are not detailed in the provided README, its fine-tuned nature suggests suitability for tasks aligned with the sft dataset it was trained on. Developers should consider its base capabilities and the fine-tuning objective when evaluating its applicability for their specific needs.