laion/exp-uns-r2egym-16_8x_glm_4_7_traces_jupiter_cleaned
The laion/exp-uns-r2egym-16_8x_glm_4_7_traces_jupiter_cleaned 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-16_8x_glm_4.7_traces_jupiter_cleaned/snapshots/f33c2e87626104d2406a7182a6a713bf0b337bdb_thinking_preprocessed dataset. This model is specifically adapted through fine-tuning for tasks related to the dataset it was trained on, offering specialized performance within that domain.
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Model Overview
This model, exp-uns-r2egym-16_8x_glm_4_7_traces_jupiter_cleaned, is an 8 billion parameter language model derived from the Qwen/Qwen3-8B architecture. It has undergone a specific fine-tuning process to adapt its capabilities to a particular dataset.
Key Characteristics
- Base Model: Fine-tuned from Qwen/Qwen3-8B.
- Parameter Count: 8 billion parameters.
- Context Length: Supports a context length of 32768 tokens.
- Training Data: Fine-tuned on the
/data/cat/ws/befe330h-befe330h-otagent/huggingface/hub/datasets--DCAgent--exp-uns-r2egym-16_8x_glm_4.7_traces_jupiter_cleaned/snapshots/f33c2e87626104d2406a7182a6a713bf0b337bdb_thinking_preprocesseddataset.
Training Details
The fine-tuning process involved several specific hyperparameters:
- Learning Rate: 4e-05
- Optimizer: ADAMW_TORCH_FUSED with betas=(0.9, 0.98) and epsilon=1e-08.
- Epochs: Trained for 7.0 epochs.
- Batch Size: A total training batch size of 16 (with gradient accumulation steps of 2).
This model is specialized for tasks aligned with its unique training dataset, suggesting potential applications in areas related to the dataset's content.