DCAgent/a1-stackexchange_tor
The DCAgent/a1-stackexchange_tor model is an 8 billion parameter language model fine-tuned from Qwen/Qwen3-8B. Developed by DCAgent, this model is specifically fine-tuned on a dataset derived from StackExchange Tor sandboxes, suggesting an optimization for tasks related to technical Q&A or content from the Tor network. Its specialized training data indicates potential strengths in understanding and generating content within this specific domain.
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Model Overview
The DCAgent/a1-stackexchange_tor is an 8 billion parameter language model, fine-tuned from the base Qwen/Qwen3-8B architecture. This model has undergone specialized training on a dataset sourced from /e/scratch/jureap59/raoof1/sft_data/hf_hub/datasets--DCAgent--stackexchange-tor-sandboxes_glm_4.7_traces_jupiter/snapshots/a0044e399194e17c864e3a35296dd1520fdddef0_thinking_preprocessed.
Key Training Details
During its fine-tuning process, the model utilized the following hyperparameters:
- Learning Rate: 4e-05
- Batch Size: 1 (train), 8 (eval)
- Optimizer: ADAMW_TORCH_FUSED with betas=(0.9, 0.98) and epsilon=1e-08
- LR Scheduler: Cosine type with a warmup ratio of 0.1
- Epochs: 7.0
- Distributed Training: Multi-GPU setup across 16 devices, resulting in a total train batch size of 16.
Potential Use Cases
Given its fine-tuning on StackExchange Tor sandbox data, this model is likely best suited for applications requiring:
- Understanding and generating content related to the Tor network.
- Processing and responding to technical questions and answers found on platforms like StackExchange, particularly within specialized domains.
Further information regarding specific intended uses, limitations, and detailed evaluation data is currently pending.