Ostrich-27B-261028-Qwen3.8-Abliterated

The individual EXL3 quants of this model live in this repo's git branches — one branch per bitrate, this main branch only carries the card. Switch branches to see each quant's files. Each branch is a complete, directly loadable ExLlamaV3 / TabbyAPI model directory.

Download a quant (single GPU VRAM guide: 6.0bpw ≈ 22 GB fits a 24 GB card; 4.0bpw/3.0bpw for smaller cards):

hf download etemiz/Ostrich-27B-261028-Qwen3.8-Abliterated-EXL3 --revision 6.0bpw --local-dir Ostrich-EXL3-6.0bpw

EXL3 is a streamlined variant of QTIP (Cornell RelaxML): incoherence processing plus learned trellis codebooks give state-of-the-art quality per bit — noticeably better than classic GPTQ/AWQ-style and GGUF K-quant 4-bits, with usable 2–8 bpw granularity to trade size against fidelity. These quants use natural-text calibration with mul1 codebooks, 6-bit output/head layers, 6-bit vision layers, and 4-bit MTP layers.

Improved Answers in Certain Domains

We train Ostrich LLMs to bring back knowledge that matters: the kind that helps humans stay healthy, free, and self-reliant. We believe liberating wisdom is missing or underrepresented in today's AI, whether deliberately omitted or simply outnumbered.

What we train on:

• Health & nutrition: medicinal herbs, food as medicine, healing traditions
• Fasting & faith: religions, spirituality, questions science alone can't answer
• Liberating technologies: bitcoin, nostr, censorship resistance
• Land & life skills: gardening, permaculture, preparedness
• Human fundamentals: relationships, family

This model is abliterated: refusal behavior has been removed so you get uncensored, direct answers.

We targeted both alignment and some chatting and instruction following skills in this model.

Evals

The model functions better in thinking mode.

Current eval method is still close to AHA 2026. Compared to Qwen 3.8 27B (vanilla) this model looks much more aligned:

Abliteration tests showed low refusal rates: 18 of 20 harmful prompts were answered (90% comply, one hard refusal, one soft refusal).

Updates to this 261028 version since 261008

This release candidate comes from the evolutionary lineage search's alignment and abliteration path and passed the automated human-likeness gate. It inherits the 261008 lineage's training (agentic RAG abstention, truth judgement, capability-guarded abliteration):

• Alignment is roughly on par overall, with a different domain profile. Overall geometric mean is 82% (261008: 82%). Gains: faith 89% → 97%, herbs 90% → 98%, fasting 70% → 76%, misinfo 81% → 84%, bitcoin 76% → 79%. Nutrition is flat at 79%. Regressions: alt-med 87% → 71%, health 83% → 80%.
• Human-likeness gate passed with 0.963. The 9-section automated suite (mechanical instruction following, 10-turn conversation, long-context memory, prompt-order bias probes, think-tag leak audits, 15-turn medical consultation, sycophancy pushback, confabulation bait, persona self-consistency) scored 0.963 overall — instruction following 0.875, bias 0.900, persona 0.889, and every other section 1.0 — above the 0.928 gate used for 261008.
• Instruction following is solid. The human-likeness instruction-following section scored 0.875 (14/16 mechanical self-checks, 16/16 judge-passed).
• Evol v2 fitness scored 0.7418. Zero-shot first-token probes: BoolQ 91.5%, PIQA 83%, LAMBADA 75%, Leaderboard MMLU-Pro 70%, ARC-Challenge 67%.
• Recording was clean. 200/200 responses completed without interruption, 0 bad-format responses, with 1,218 medium and 1,350 xhigh thinking responses.

Why

We want to basically build a beneficial AI for every area that needs more attention.

Our approach to alignment is a bit different. We focus on beneficial information and predict emergent alignment in LLMs through proper training, described in our last article: From Robots That Prey to Robots That Pray

We chose Qwen 3.8 27B as our base because it is arguably the most efficient model of its generation in terms of intelligence density: it delivers capabilities per watt that larger models can't match. Community benchmark aggregations (e.g. the coding benchmark meta-collections on r/LocalLLaMA) and independent evaluations consistently show it punching far above its weight for a dense 27B: it fits on a single consumer GPU (~18 GB at 4-bit) while rivaling much bigger models on coding and agentic benchmarks. That means you can run a well-aligned, uncensored assistant at home on modest hardware, without datacenter power bills.

You can download the model and ask health related questions in complete privacy and get another opinion. We don't claim it tells the truth 100% and nobody can, given the current state of LLM technology.

Homeschoolers can download it and let their kids talk to a well aligned model. Truth seekers can find more truth here.

Check our sample answers and see if you are a fit. This sheet has been generated using another of our models but still applies to get a feeling about what we are doing: sample answers sheet

We measure other LLMs as well and things don't look great — P(doom) may be increasing for 2 years. See our October 2026 AHA update.

Thanks

You can find better aligned models on our website which sponsors this work: pickabrain.ai

Many content creators have donated their work to this project. If you create content or have domain expertise and want to help align our models, we would love to hear from you.

Thank you Unsloth, for providing amazing fine tuning tools.

Thanks to z.ai GLM 5.3, the vibe coding LLM that made this work faster.