| --- |
| 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 Improved Answers in Certain Domains |
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| Ostrich LLMs, bringing you "the knowledge that matters". |
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| - Health, nutrition, medicinal herbs |
| - Fasting, faith, healing |
| - Liberating technologies like bitcoin and nostr |
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| ## AHA 2026 average score **73%** |
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| 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| |
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| Why: https://huggingface.co/blog/etemiz/building-a-beneficial-ai |
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| 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. |
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| 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. |
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| Methods used for fine tuning: |
| - CPT |
| - SFT |
| - GSPO |
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| My last article: https://huggingface.co/blog/etemiz/from-robots-that-prey-to-robots-that-pray |
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| Thanks @unslothai for providing amazing tools. |
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| Sponsored by https://pickabrain.ai |
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