The penfever/GLM-4_6-inferredbugs-32eps-65k-fixeps model is an 8 billion parameter language model fine-tuned from Qwen/Qwen3-8B. It was trained on the penfever/GLM-4.6-inferredbugs-32eps-65k dataset, suggesting a specialization in handling or identifying inferred bugs. This model is likely optimized for tasks related to code analysis, debugging, or understanding software defects, leveraging its 32768 token context length.
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Model Overview
This model, penfever/GLM-4_6-inferredbugs-32eps-65k-fixeps, is an 8 billion parameter language model built upon the robust Qwen/Qwen3-8B architecture. It has been specifically fine-tuned using the penfever/GLM-4.6-inferredbugs-32eps-65k dataset, indicating a specialized focus on tasks involving inferred bugs or software defect analysis.
Training Details
The fine-tuning process utilized a learning rate of 4e-05, with a total training batch size of 16 across 16 GPUs. The optimizer used was ADAMW_TORCH_FUSED with specific beta and epsilon values, and a cosine learning rate scheduler with a 0.1 warmup ratio was applied over 7 epochs. The training was conducted using Transformers 4.56.0 and Pytorch 2.9.0+cu128.
Potential Use Cases
Given its fine-tuning dataset, this model is likely well-suited for applications such as:
- Code analysis and debugging assistance: Identifying potential bugs or inconsistencies in codebases.
- Automated bug reporting: Generating descriptions or classifications for inferred software defects.
- Understanding software behavior: Analyzing code to predict or explain error conditions.
Users should note that specific details regarding the model's intended uses, limitations, and comprehensive training/evaluation data are not fully elaborated in the provided model card.