laion/exp-uns-r2egym-33_6x_glm_4_7_traces_jupiter_cleaned
The laion/exp-uns-r2egym-33_6x_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-33_6x_glm_4.7_traces_jupiter_cleaned/snapshots/979a62e8e35d7341b236ceb175f7ddcff8c72d01_thinking_preprocessed dataset. This model is specialized for tasks related to the specific dataset it was fine-tuned on, suggesting a focus on particular reasoning or trace analysis applications.
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Model Overview
This model, exp-uns-r2egym-33_6x_glm_4_7_traces_jupiter_cleaned, is an 8 billion parameter language model developed by laion. It is a fine-tuned version of the Qwen/Qwen3-8B base model, adapted for specific applications through further training.
Key Characteristics
- Base Model: Qwen/Qwen3-8B
- Parameter Count: 8 billion parameters
- Context Length: 32768 tokens
- Fine-tuning Dataset: The model was fine-tuned on the
/data/cat/ws/befe330h-befe330h-otagent/huggingface/hub/datasets--DCAgent--exp-uns-r2egym-33_6x_glm_4.7_traces_jupiter_cleaned/snapshots/979a62e8e35d7341b236ceb175f7ddcff8c72d01_thinking_preprocesseddataset.
Training Details
The fine-tuning process utilized the following 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 16 (1 per device, 8 devices, 2 gradient accumulation steps)
- Epochs: 7.0
- Scheduler: Cosine learning rate scheduler with 0.1 warmup ratio
Intended Use
Given its fine-tuning on a specific dataset related to 'traces' and 'jupiter_cleaned', this model is likely intended for tasks that align with the characteristics and domain of its training data. Users should evaluate its performance on their specific use cases, particularly those involving trace analysis or similar specialized reasoning tasks.