laion/swesmith-unified-1000__Qwen3-8B
The laion/swesmith-unified-1000__Qwen3-8B model is a fine-tuned version of the Qwen/Qwen3-8B architecture. It was trained on the /e/data1/datasets/playground/ot/hf_hub/datasets--laion--swesmith-unified-1000/snapshots/f36966d2485fc81ece28e25248939b0db9f34677_thinking_preprocessed dataset. This model is optimized for tasks related to the specific dataset it was fine-tuned on, suggesting potential specialization in areas covered by that data.
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Overview
This model, laion/swesmith-unified-1000__Qwen3-8B, is a fine-tuned variant of the Qwen3-8B base model developed by Qwen. It has undergone further training on a specific dataset: /e/data1/datasets/playground/ot/hf_hub/datasets--laion--swesmith-unified-1000/snapshots/f36966d2485fc81ece28e25248939b0db9f34677_thinking_preprocessed.
Training Details
The fine-tuning process utilized the following key hyperparameters:
- Learning Rate: 4e-05
- Optimizer: ADAMW_TORCH_FUSED with betas=(0.9, 0.98) and epsilon=1e-08
- Batch Size: A total training batch size of 96 (1 per device with 32 devices and 3 gradient accumulation steps)
- Epochs: 7.0
- Scheduler: Cosine learning rate scheduler with a 0.1 warmup ratio
Framework Versions
The training environment included:
- Transformers 4.57.6
- Pytorch 2.9.1+cu130
- Datasets 4.7.0
- Tokenizers 0.22.2
Intended Use
Given its fine-tuning on a specific dataset, this model is likely best suited for tasks and applications that align with the content and structure of the swesmith-unified-1000 dataset. Users should consider the nature of this dataset when evaluating the model's applicability to their specific use cases.