DCAgent/a1-crosscodeeval_csharp is an 8 billion parameter causal language model, fine-tuned from Qwen/Qwen3-8B, with a context length of 32768 tokens. This model is specifically optimized for C# code evaluation tasks, leveraging a specialized dataset for supervised fine-tuning. Its primary application is to assist in the analysis and understanding of C# code, making it suitable for code-centric development workflows.
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Model Overview
DCAgent/a1-crosscodeeval_csharp is an 8 billion parameter language model, derived from the Qwen3-8B architecture. It has been specifically fine-tuned for tasks related to C# code evaluation, utilizing a dataset focused on exp_rpt_crosscodeeval-csharp_10k_glm_4.7_traces_jupiter for supervised learning. This specialization aims to enhance its performance and utility in understanding and processing C# programming constructs.
Key Capabilities
- C# Code Evaluation: Optimized for tasks involving the analysis and understanding of C# code.
- Large Context Window: Features a 32768-token context length, allowing for processing of substantial code segments.
- Fine-tuned Performance: Benefits from supervised fine-tuning on a targeted dataset, improving its relevance for specific C# related applications.
Training Details
The model was trained with a learning rate of 4e-05 over 7 epochs, using an AdamW optimizer and a cosine learning rate scheduler with a 0.1 warmup ratio. The training was distributed across 16 devices, with a total batch size of 16. This focused training regimen is designed to imbue the model with specialized knowledge for its intended C# evaluation domain.
Good For
- Developers and researchers working on C# code analysis tools.
- Applications requiring automated evaluation or understanding of C# programming logic.
- Environments where a specialized C# code model with a large context window is beneficial.