VladShash/qwen3-8b-classic-one-shot-no-repair
VladShash/qwen3-8b-classic-one-shot-no-repair is an 8 billion parameter language model fine-tuned from formalmathatepfl/qwen3-cpt. This model is designed for specific tasks, leveraging its 32768 token context length. It is optimized for single-shot inference scenarios without repair mechanisms, making it suitable for applications requiring direct, uncorrected outputs.
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Model Overview
This model, VladShash/qwen3-8b-classic-one-shot-no-repair, is an 8 billion parameter language model fine-tuned from the formalmathatepfl/qwen3-cpt base. It was trained using an sft dataset with a learning rate of 2e-05 and a cosine learning rate scheduler over 1.0 epoch. The training utilized 8 GPUs with a total batch size of 8.
Key Training Details
- Base Model:
formalmathatepfl/qwen3-cpt - Parameters: 8 billion
- Context Length: 32768 tokens
- Learning Rate: 2e-05
- Optimizer: ADAMW_TORCH_FUSED
- Epochs: 1.0
Intended Use Cases
While specific use cases are not detailed in the provided information, the model's name suggests an optimization for 'one-shot' tasks where a single, direct response is expected without iterative refinement or 'repair' mechanisms. This could be beneficial for applications requiring immediate, uncorrected outputs based on a single prompt.