w3en2g/EM_QTA_Qwen3-0.6B_bad_medical_advice_1003_6k
w3en2g/EM_QTA_Qwen3-0.6B_bad_medical_advice_1003_6k is a 0.8 billion parameter language model, fine-tuned from the Qwen3-0.6B architecture. This model is specifically adapted using the bad_medical_advice_1003_6k dataset. Its primary differentiation lies in its specialized fine-tuning for generating responses related to medical advice, making it suitable for specific research or analysis in this domain.
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Model Overview
This model, EM_QTA_Qwen3-0.6B_bad_medical_advice_1003_6k, is a fine-tuned variant of the Qwen3-0.6B base model, developed by w3en2g. It features approximately 0.8 billion parameters and a context length of 32768 tokens. The model has undergone specialized training on the bad_medical_advice_1003_6k dataset.
Key Characteristics
- Base Model: Qwen3-0.6B architecture.
- Parameter Count: 0.8 billion parameters.
- Context Length: 32768 tokens.
- Specialized Fine-tuning: Trained on a dataset focused on "bad medical advice," indicating a specific domain of adaptation.
Training Details
The fine-tuning process utilized the following hyperparameters:
- Learning Rate: 1e-05
- Optimizer: AdamW with betas=(0.9, 0.999) and epsilon=1e-08
- Epochs: 1.0
- Batch Size: A total training batch size of 16 (train_batch_size: 1, gradient_accumulation_steps: 2, num_devices: 8).
Intended Use Cases
Given its fine-tuning on a specific dataset, this model is primarily intended for:
- Research and analysis of language patterns related to medical advice, particularly in the context of potentially misleading or incorrect information.
- Exploratory studies on model behavior when exposed to specialized, potentially sensitive, domain-specific data.
Note: Due to its training data, this model is not intended for generating reliable medical advice or for deployment in applications requiring accurate health information.