laion/exp-uns-r2egym-33_6x_glm_4_7_traces_jupiter
The laion/exp-uns-r2egym-33_6x_glm_4_7_traces_jupiter model is an 8 billion parameter language model, fine-tuned from Qwen/Qwen3-8B. It was trained on the /data/cat/ws/befe330h-befe330h-otagent/huggingface/hub/datasets--DCAgent--exp-uns-r2egym-33_6x_glm_4.7_traces_jupiter/snapshots/9f6fd69f6fa50425609d375c4f7198b192f4a61b_thinking_preprocessed dataset. This model is a specialized fine-tune, with its primary differentiator being its specific training data, suggesting potential optimization for tasks related to that dataset's content.
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Model Overview
This model, laion/exp-uns-r2egym-33_6x_glm_4_7_traces_jupiter, is an 8 billion parameter language model derived from the Qwen/Qwen3-8B architecture. It has been specifically fine-tuned on a unique dataset: /data/cat/ws/befe330h-befe330h-otagent/huggingface/hub/datasets--DCAgent--exp-uns-r2egym-33_6x_glm_4.7_traces_jupiter/snapshots/9f6fd69f6fa50425609d375c4f7198b192f4a61b_thinking_preprocessed.
Training Details
The fine-tuning process involved several key hyperparameters:
- Learning Rate: 4e-05
- Batch Size: 1 (train), 8 (eval)
- Gradient Accumulation Steps: 2, leading to a total effective batch size of 16
- Optimizer: AdamW_Torch_Fused with specific betas and epsilon
- LR Scheduler: Cosine type with a 0.1 warmup ratio
- Epochs: 7.0
Potential Use Cases
Given its fine-tuning on a specific dataset, this model is likely best suited for tasks that align with the content and structure of the training data. Developers should investigate the nature of the /data/cat/ws/befe330h-befe330h-otagent/huggingface/hub/datasets--DCAgent--exp-uns-r2egym-33_6x_glm_4.7_traces_jupiter/snapshots/9f6fd69f6fa50425609d375c4f7198b192f4a61b_thinking_preprocessed dataset to determine its applicability to their specific needs. Without further information on the dataset's content, its general utility is undefined, but its specialized training suggests a focus on particular domains or tasks.