Overview

DCAgent/a1-pymethods2test is an 8 billion parameter language model, fine-tuned from the base model Qwen/Qwen3-8B. The fine-tuning process utilized a specific dataset, /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--exp_rpt_pymethods2test-v3_10k_glm_4.7_traces_jupiter/snapshots/472a5d70564a9d7de8cfaac85c4b09ef23abbeee_thinking_preprocessed, indicating a potential specialization derived from this training data.

Training Details

The model was trained with a learning rate of 4e-05 over 7.0 epochs, using an AdamW optimizer with specific beta and epsilon values. The training involved a multi-GPU 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 development environment included Transformers 4.57.6, Pytorch 2.9.1+cu130, Datasets 4.7.0, and Tokenizers 0.22.2.

Key Characteristics

• Base Model: Qwen3-8B
• Parameter Count: 8 billion
• Context Length: 32768 tokens
• Fine-tuning Dataset: Specialized dataset related to exp_rpt_pymethods2test-v3_10k_glm_4.7_traces_jupiter

Intended Uses & Limitations

Specific intended uses and limitations are not detailed in the provided information, suggesting further exploration of the fine-tuning dataset's nature would be beneficial to understand its optimal applications.