DCAgent/a1-crosscodeeval_typescript is an 8 billion parameter language model fine-tuned from Qwen/Qwen3-8B. This model is specifically optimized for TypeScript code evaluation, leveraging a dataset derived from cross-code evaluation traces. It is designed for tasks requiring nuanced understanding and generation of TypeScript code, making it suitable for development and code analysis applications.
Loading preview...
Overview
DCAgent/a1-crosscodeeval_typescript is an 8 billion parameter language model, fine-tuned from the Qwen/Qwen3-8B architecture. It has a context length of 32768 tokens, making it capable of processing substantial codebases.
Key Capabilities
This model is specialized for tasks related to TypeScript code evaluation. Its training on the /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--exp_rpt_crosscodeeval-typescript_10k_glm_4.7_traces_jupiter/snapshots/7663e75450af4ceae0c6684ff5ab183ebef5c409_thinking_preprocessed dataset indicates a focus on understanding and processing TypeScript code within a cross-code evaluation context.
Training Details
The model was trained with a learning rate of 4e-05, a total batch size of 16 across 16 devices, and utilized the AdamW_Torch_Fused optimizer. Training spanned 7 epochs with a cosine learning rate scheduler and a warmup ratio of 0.1. The training environment included Transformers 4.57.6, Pytorch 2.9.1+cu130, Datasets 4.7.0, and Tokenizers 0.22.2.
Intended Use Cases
Given its specialized fine-tuning, this model is likely best suited for applications involving:
- Automated TypeScript code review
- Code analysis and understanding for TypeScript
- Generating or evaluating TypeScript code snippets
- Assisting in development workflows that require deep TypeScript knowledge