DCAgent/a1-stack_pytest_gpt5mini
DCAgent/a1-stack_pytest_gpt5mini is an 8 billion parameter language model fine-tuned from Qwen/Qwen3-8B. This model is specifically optimized for tasks related to pytest and GPT-5 mini traces, leveraging a specialized dataset for its training. It is designed for applications requiring analysis or generation based on these specific technical contexts. The model has a context length of 32768 tokens.
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Overview
This model, DCAgent/a1-stack_pytest_gpt5mini, is an 8 billion parameter language model derived from the Qwen3-8B architecture. It has been fine-tuned on a specialized dataset, /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--exp_rpt_stack-pytest-gpt5mini_glm_4.7_traces_jupiter/snapshots/8f5962e22355e85ad49717a49e9a3821a1db506e_thinking_preprocessed, indicating a focus on specific technical domains.
Key Characteristics
- Base Model: Qwen/Qwen3-8B
- Parameter Count: 8 billion parameters
- Context Length: 32768 tokens
- Training Data Focus: Specialized dataset related to
pytestandgpt5minitraces, suggesting an optimization for tasks within these areas.
Training Details
The model underwent 7 epochs of training with a learning rate of 4e-05. It utilized a multi-GPU setup with 16 devices, resulting in a total training batch size of 16. The optimizer used was ADAMW_TORCH_FUSED with specific beta and epsilon values, and a cosine learning rate scheduler with a 0.1 warmup ratio. The training was conducted using Transformers 4.57.6, Pytorch 2.9.1+cu130, and Datasets 4.7.0.
Potential Use Cases
This model is likely suitable for applications requiring specialized understanding or generation within the pytest framework or analysis of GPT-5 mini trace data, given its targeted fine-tuning.