LorenaYannnnn/sycophancy-Qwen3-0.6B-baseline_all_tokens-seed_0
The LorenaYannnnn/sycophancy-Qwen3-0.6B-baseline_all_tokens-seed_0 is a 0.8 billion parameter language model based on the Qwen3 architecture. This model is a baseline version, trained with all tokens and a specific seed, suggesting its use as a foundational model for further research or fine-tuning. Its primary differentiator lies in its specific training configuration, making it suitable for evaluating sycophancy or similar behavioral characteristics in LLMs.
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Model Overview
This model, LorenaYannnnn/sycophancy-Qwen3-0.6B-baseline_all_tokens-seed_0, is a 0.8 billion parameter language model built upon the Qwen3 architecture. It represents a baseline version, specifically trained using all available tokens and initialized with a seed of 0. The model card indicates that further information regarding its development, funding, specific model type, language(s), and license is currently needed.
Key Characteristics
- Architecture: Qwen3-based.
- Parameter Count: 0.8 billion parameters.
- Training Configuration: Baseline model, trained with "all tokens" and a fixed "seed_0".
Intended Use Cases
While specific direct and downstream uses are marked as "More Information Needed" in the model card, its naming convention suggests it is likely intended for:
- Research into LLM behavior: Particularly for studying phenomena like sycophancy.
- Baseline for comparative studies: Serving as a reference point for evaluating modifications or fine-tuning efforts.
- Exploration of training methodologies: Understanding the impact of specific training parameters (like "all tokens" and "seed_0") on model performance and characteristics.
Limitations and Recommendations
As with many models, users should be aware of potential biases, risks, and technical limitations. The model card explicitly states that "More Information Needed" is required for a comprehensive understanding of these aspects and for providing further recommendations. Users are advised to exercise caution and conduct their own evaluations before deploying the model in sensitive applications.