yujunzhou/SFT_Advanced_Risk_Situation_Aware_Qwen3-4B-Base
yujunzhou/SFT_Advanced_Risk_Situation_Aware_Qwen3-4B-Base is a 4 billion parameter language model fine-tuned from Qwen/Qwen3-4B-Base. This model is specifically adapted using the Advanced_Risk_Situation_Aware_Qwen3-4B-Base dataset, indicating a specialization in understanding and responding to advanced risk situations. It is designed for applications requiring nuanced comprehension of complex risk scenarios, leveraging its 40960-token context length for detailed analysis.
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Model Overview
yujunzhou/SFT_Advanced_Risk_Situation_Aware_Qwen3-4B-Base is a fine-tuned version of the Qwen3-4B-Base model, developed by yujunzhou. This 4 billion parameter model is specifically adapted for tasks related to advanced risk situation awareness, leveraging a substantial 40960-token context window.
Key Characteristics
- Base Model: Built upon the robust Qwen3-4B-Base architecture.
- Specialized Fine-tuning: Trained on the
Advanced_Risk_Situation_Aware_Qwen3-4B-Basedataset, suggesting a focus on identifying, analyzing, and responding to complex risk scenarios. - Context Length: Features a 40960-token context length, enabling the processing of extensive inputs for detailed situational understanding.
Training Details
The model was trained with the following hyperparameters:
- Learning Rate: 1e-05
- Batch Size: A total training batch size of 128 (4 per device across 8 GPUs with 4 gradient accumulation steps).
- Epochs: 10.0 epochs.
- Optimizer: AdamW with standard betas and epsilon.
Potential Use Cases
This model is likely suitable for applications requiring:
- Risk Assessment: Analyzing complex data to identify potential risks.
- Situational Awareness: Processing large volumes of information to maintain an understanding of evolving situations.
- Specialized Language Understanding: Tasks where a deep comprehension of risk-related terminology and contexts is crucial.