---
license: apache-2.0
base_model:
- Qwen/Qwen3.5-27B
tags:
- '#aha'
- '#health'
- '#nutrition'
- '#medicinalherbs'
- '#fasting'
- '#faith'
- '#healing'
- '#bitcoin'
- '#nostr'
---

# Ostrich 27B - Qwen 3.5 with Better Human Alignment

Ostrich LLMs, bringing you "the knowledge that matters".

- Health, nutrition, medicinal herbs
- Fasting, faith, healing
- Liberating technologies like bitcoin and nostr

Methods used for fine tuning:
- CPT
- SFT
- GSPO

Why: https://huggingface.co/blog/etemiz/building-a-beneficial-ai

GSPO training made the thinking lengths shorter. I mainly targeted about 3000 letters (~1000 tokens) for thinking budget.

Model is also abliterated since we built on @huihui-ai's model.

We plan to release many more based on Qwen 3.5 27B. 

Comparison of some answers between another of our fine tune and base model: https://sheet.zohopublic.com/sheet/published/um332e3d15f34bfe64605ad3c1b149c9f8ca4 These answers are not from this model but it is a similar work.

Thanks @unslothai for providing amazing tools.