laion/Qwen3-8B_perturbed-docker-exp-taskmaster2-tasks_glm_4.7_traces_locetash_save-strategy_steps
The laion/Qwen3-8B_perturbed-docker-exp-taskmaster2-tasks_glm_4.7_traces_locetash_save-strategy_steps model is an 8 billion parameter language model, fine-tuned from the Qwen/Qwen3-8B architecture. It was specifically adapted using the DCAgent/perturbed-docker-exp-taskmaster2-tasks_glm_4.7_traces_locetash dataset. This model is optimized for tasks related to perturbed Docker experiments and GLM traces, offering specialized performance in these niche areas.
Loading preview...
Model Overview
This model, laion/Qwen3-8B_perturbed-docker-exp-taskmaster2-tasks_glm_4.7_traces_locetash_save-strategy_steps, is an 8 billion parameter language model. It is a fine-tuned variant of the Qwen/Qwen3-8B base model, specifically adapted for a unique dataset.
Key Characteristics
- Base Model: Qwen/Qwen3-8B, a large language model developed by Qwen.
- Fine-tuning Dataset: The model was fine-tuned on the
DCAgent/perturbed-docker-exp-taskmaster2-tasks_glm_4.7_traces_locetashdataset, indicating a specialization in tasks related to perturbed Docker experiments and GLM traces. - Parameter Count: 8 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a context length of 32768 tokens.
Training Details
The fine-tuning process utilized the following hyperparameters:
- Learning Rate: 0.0001
- Optimizer: ADAMW_TORCH_FUSED with specific beta and epsilon values.
- Scheduler: Cosine learning rate scheduler with a warmup ratio of 0.005.
- Epochs: Trained for 8.0 epochs.
- Batch Size: A total training batch size of 32 across 32 devices.
Potential Use Cases
Given its specialized fine-tuning, this model is likely best suited for research and applications involving:
- Analysis of perturbed Docker experiment data.
- Processing and understanding GLM 4.7 traces.
- Tasks within the domain of
taskmaster2related to the specific perturbation context.