laion/r2egym-316-opt1k__Qwen3-8B
The laion/r2egym-316-opt1k__Qwen3-8B model is an 8 billion parameter language model, fine-tuned from the Qwen/Qwen3-8B architecture. It was trained on the /e/data1/datasets/playground/ot/hf_hub/datasets--laion--r2egym-unified-316 dataset, suggesting a specialization in tasks related to the 'r2egym-unified-316' domain. This model is optimized for specific applications within its fine-tuning dataset's scope, offering a focused alternative to general-purpose LLMs.
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Model Overview
This model, laion/r2egym-316-opt1k__Qwen3-8B, is an 8 billion parameter language model built upon the Qwen3-8B architecture developed by Qwen. It has undergone a specific fine-tuning process to adapt its capabilities to a particular domain.
Key Characteristics
- Base Model: Fine-tuned from the robust Qwen/Qwen3-8B model.
- Parameter Count: Features 8 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, enabling processing of longer inputs and generating coherent, extended outputs.
- Fine-tuning Dataset: Trained on the
/e/data1/datasets/playground/ot/hf_hub/datasets--laion--r2egym-unified-316dataset, indicating a specialization for tasks relevant to this data source.
Training Details
The fine-tuning process utilized a learning rate of 4e-05, a cosine learning rate scheduler with a 0.1 warmup ratio, and 7 epochs of training. It employed a total batch size of 96 across 32 multi-GPU devices, using the ADAMW_TORCH_FUSED optimizer.
Intended Use
While specific intended uses are not detailed in the provided README, its fine-tuning on a specialized dataset suggests it is best suited for applications aligned with the characteristics and content of the r2egym-unified-316 dataset. Developers should evaluate its performance on tasks closely related to this domain.