etemiz/Ostrich-27B-Qwen3.5-260411-GGUF

Ostrich 27B - Qwen 3.5 with Improved Answers in Certain Domains

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

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

AHA average score 73%

Per domain:

domain	match percent	matched/total
faith	84%	21/25
fasting	75%	65/87
health	77%	83/108
nutrition	61%	44/72
misinfo	57%	26/46
bitcoin	81%	50/62
alt-med	80%	45/56
herbs	83%	25/30
nostr	60%	27/45

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

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.

GSPO training made the thinking lengths shorter. I mainly targeted about 3000 letters (~1000 tokens) for thinking budget. If your Qwen 3.5 is endlessly reasoning, try this one.

Methods used for fine tuning:

• CPT
• SFT
• GSPO

My last article: https://huggingface.co/blog/etemiz/from-robots-that-prey-to-robots-that-pray

Thanks @unslothai for providing amazing tools.

Sponsored by PickaBrain.ai - For better aligned models and high privacy AI chat visit https://pickabrain.ai