etemiz/Ostrich-27B-Qwen3.8-260816-Abliterated
etemiz/Ostrich-27B-Qwen3.8-260816-Abliterated is a 27 billion parameter Qwen-based language model fine-tuned by etemiz with a 32K context length. This model specializes in domains such as health, nutrition, faith, liberating technologies (Bitcoin, Nostr), gardening, and preparedness. It aims to provide knowledge in areas often underrepresented in AI, focusing on beneficial information and emergent alignment. The model is designed for users seeking alternative perspectives and well-aligned responses in its specialized domains.
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Ostrich 27B: Specialized Qwen-based LLM
etemiz/Ostrich-27B-Qwen3.8-260816-Abliterated is a 27 billion parameter model built upon the Qwen 3.8 architecture, fine-tuned by etemiz. Its core mission is to provide "wisdom that liberates" by focusing on domains crucial for human well-being and often underrepresented in mainstream AI.
Key Capabilities & Focus Areas
This model has been specifically trained to improve answers and provide nuanced perspectives in the following areas:
- Health & Wellness: Nutrition, medicinal herbs, fasting, and healing.
- Faith & Philosophy: Religious topics and spiritual insights.
- Liberating Technologies: Bitcoin and Nostr.
- Sustainable Living: Gardening and permaculture.
- Preparedness & Relationships: Practical knowledge for self-sufficiency and interpersonal dynamics.
Training Approach
The Ostrich 27B was developed using LoRA extractions from previous Ostrich 3.5 and 3.6 fine-tunes, combined with abliteration fine-tunes from the community, applied to the base Qwen 3.8 model. The developers focused on healing overfitting by adjusting weights from different models, resulting in good instruction following, overall sanity, and improved alignment scores compared to the base Qwen 3.8 27B model.
Ideal Use Cases
- Private Health Inquiries: Seeking alternative opinions on health-related questions while maintaining privacy.
- Homeschooling: Providing a well-aligned model for educational interactions.
- Truth Seekers: Exploring topics from a perspective focused on beneficial information and emergent alignment.
The model aims to offer a different approach to AI alignment by prioritizing beneficial information and predicting emergent alignment through targeted training.