laion/perturbed-docker-exp-freelancer-tasks_glm_4_7_traces
The laion/perturbed-docker-exp-freelancer-tasks_glm_4_7_traces model is an 8 billion parameter language model fine-tuned from Qwen/Qwen3-8B. It was trained on the perturbed-docker-exp-freelancer-tasks_glm_4.7_traces dataset, suggesting a specialization in tasks related to perturbed Docker environments or freelancer task traces. With a context length of 32768 tokens, it is designed for processing extensive input sequences relevant to its fine-tuning data.
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Model Overview
This model, laion/perturbed-docker-exp-freelancer-tasks_glm_4_7_traces, is an 8 billion parameter language model. It is a fine-tuned variant of the Qwen3-8B architecture, developed by Qwen.
Key Characteristics
- Base Model: Fine-tuned from Qwen/Qwen3-8B.
- Training Data: Specialized training on the
/data/cat/ws/befe330h-befe330h-otagent/huggingface/hub/datasets--DCAgent--perturbed-docker-exp-freelancer-tasks_glm_4.7_traces/snapshots/678a5760f0b5306a6ab1f04d6276204b2e4f91f6_thinking_preprocesseddataset. - Context Length: Supports a substantial context window of 32768 tokens.
Training Details
The model was trained with specific hyperparameters:
- Learning Rate: 4e-05
- Batch Size: A total training batch size of 16 (1 per device across 8 GPUs with 2 gradient accumulation steps).
- Optimizer: ADAMW_TORCH_FUSED with betas=(0.9, 0.98) and epsilon=1e-08.
- Scheduler: Cosine learning rate scheduler with a 0.1 warmup ratio.
- Epochs: Trained for 7.0 epochs.
Potential Use Cases
Given its fine-tuning on a dataset related to "perturbed-docker-exp-freelancer-tasks_glm_4.7_traces", this model is likely optimized for:
- Analyzing or generating content related to Docker environments, especially under perturbed or specific experimental conditions.
- Processing and understanding traces or logs from freelancer tasks, potentially for automation, analysis, or simulation.
- Tasks requiring a deep understanding of the specific data patterns present in its training dataset.