DCAgent/a1-softwareheritage
DCAgent/a1-softwareheritage is a fine-tuned version of the Qwen/Qwen3-8B model, developed by DCAgent. This 8 billion parameter model was trained on a specific dataset derived from software heritage traces. Its primary application is in tasks related to processing and understanding software heritage data, leveraging the Qwen3 architecture.
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Model Overview
DCAgent/a1-softwareheritage is an 8 billion parameter language model, fine-tuned by DCAgent. It is based on the Qwen/Qwen3-8B architecture, indicating its foundation in the Qwen3 series of models. The fine-tuning process utilized a specialized dataset, /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--exp_rpt_softwareheritage_10k_glm_4.7_traces_jupiter/snapshots/59c8379c19630df3354079f71b3b11225e79593b_thinking_preprocessed, which suggests a focus on tasks related to software heritage data.
Training Details
The model was trained with a learning rate of 4e-05 over 7 epochs, using an AdamW optimizer with specific beta and epsilon parameters. The training involved a distributed setup across 16 devices, with a total batch size of 16. A cosine learning rate scheduler with a warmup ratio of 0.1 was employed. The training environment utilized Transformers 4.57.6, Pytorch 2.9.1+cu130, Datasets 4.7.0, and Tokenizers 0.22.2.
Intended Use
While specific intended uses and limitations are not detailed in the provided information, the model's fine-tuning on a software heritage dataset implies its utility in applications requiring analysis or generation related to software artifacts and their historical traces.