DCAgent2/swesmith-stack-over5050 is an 8 billion parameter language model fine-tuned from Qwen/Qwen3-8B. It was trained on specialized datasets, penfever/GLM-4.6-swesmith-32ep-131k-nosumm-reasoning and penfever/GLM-4.6-stackexchange-overflow-sandboxes-32eps-65k-reasoning, suggesting an optimization for reasoning tasks, particularly those related to Stack Exchange content. This model is designed for applications requiring nuanced understanding and generation of responses in technical or problem-solving contexts.
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Overview
DCAgent2/swesmith-stack-over5050 is an 8 billion parameter language model derived from the Qwen3-8B architecture. This model has undergone specific fine-tuning on two distinct datasets: penfever/GLM-4.6-swesmith-32ep-131k-nosumm-reasoning and penfever/GLM-4.6-stackexchange-overflow-sandboxes-32eps-65k-reasoning. The selection of these datasets indicates a strong focus on enhancing the model's reasoning capabilities, particularly within domains that might involve complex problem-solving or technical discussions, such as those found on Stack Exchange platforms.
Key Training Details
- Base Model: Qwen/Qwen3-8B
- Fine-tuning Datasets:
penfever/GLM-4.6-swesmith-32ep-131k-nosumm-reasoningpenfever/GLM-4.6-stackexchange-overflow-sandboxes-32eps-65k-reasoning
- Hyperparameters:
- Learning Rate: 4e-05
- Optimizer: AdamW_Torch_Fused with betas=(0.9, 0.98) and epsilon=1e-08
- Epochs: 7.0
- Total Train Batch Size: 16 (across 8 GPUs with gradient accumulation)
Intended Use Cases
Given its specialized training, this model is likely best suited for applications requiring:
- Reasoning and Problem Solving: Particularly in technical or domain-specific contexts.
- Content Generation: For responses or explanations similar to those found on Stack Exchange.
- Information Retrieval and Summarization: Where detailed, reasoned answers are preferred.