devpotatopotato/qwen3-8b-sft-260827-acereason
The devpotatopotato/qwen3-8b-sft-260827-acereason model is an 8 billion parameter language model, fine-tuned from Qwen/Qwen3-8B. It is specifically adapted using the acereason_keyword_details dataset, suggesting a specialization in reasoning tasks or keyword extraction. This model is designed for applications requiring enhanced performance on specific reasoning-based or detail-oriented language understanding tasks.
Loading preview...
Model Overview
The devpotatopotato/qwen3-8b-sft-260827-acereason is an 8 billion parameter language model, fine-tuned from the base Qwen/Qwen3-8B architecture. This model has been specifically adapted through supervised fine-tuning (SFT) on the acereason_keyword_details dataset.
Training Details
The fine-tuning process utilized the following key hyperparameters:
- Learning Rate: 4e-05
- Batch Size: 8 (train and eval)
- Gradient Accumulation Steps: 8, resulting in a total train batch size of 128
- Optimizer: AdamW_Torch_Fused
- LR Scheduler: Cosine with a warmup ratio of 0.05
- Epochs: 5.0
This configuration indicates a robust training setup aimed at optimizing performance on the target dataset.
Intended Use Cases
Given its fine-tuning on the acereason_keyword_details dataset, this model is likely specialized for:
- Reasoning Tasks: Applications requiring logical inference or problem-solving capabilities.
- Keyword Extraction: Identifying and extracting specific keywords or detailed information from text.
- Domain-Specific Language Understanding: Tasks where understanding nuanced details or specific reasoning patterns is crucial.
Developers should consider this model for use cases that align with its specialized training data, particularly where enhanced performance on reasoning or detailed information retrieval is beneficial.