laion/glm46-defects4j-32ep-131k
The laion/glm46-defects4j-32ep-131k model is an 8 billion parameter language model, fine-tuned from Qwen/Qwen3-8B. It was trained on the penfever/glm46-defects4j-32ep-131k dataset, suggesting a specialization in tasks related to software defects or code analysis. With a context length of 32768 tokens, it is designed for processing substantial amounts of text, likely for code-related applications.
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Overview
This model, laion/glm46-defects4j-32ep-131k, is an 8 billion parameter language model. It is a fine-tuned variant of the Qwen/Qwen3-8B architecture, indicating a strong foundation in general language understanding and generation.
Key Characteristics
- Base Model: Fine-tuned from Qwen/Qwen3-8B.
- Parameter Count: 8 billion parameters.
- Context Length: Supports a context window of 32768 tokens.
- Training Data: Fine-tuned on the
penfever/glm46-defects4j-32ep-131kdataset, which implies a focus on tasks related to software defects or code analysis.
Training Details
The model was trained using the following key hyperparameters:
- Learning Rate: 4e-05
- Optimizer: ADAMW_TORCH_FUSED
- Epochs: 7.0
- Batch Size: A total training batch size of 16 (with gradient accumulation).
Potential Use Cases
Given its fine-tuning on a dataset related to 'defects4j', this model is likely optimized for:
- Software Defect Detection: Identifying and analyzing bugs in code.
- Code Analysis: Understanding and processing programming language structures.
- Automated Debugging Assistance: Providing insights or suggestions for fixing code issues.