Ornith-1.5-27B-A3B-Coder-MTP-GGUF

Ornith-1.5-27B-A3B-Coder — GGUF (imatrix, MTP)

GGUF quantizations of ManniX-ITA/Ornith-1.5-27B-A3B-Coder — the pure competence-map 184-expert cut of Ornith-1.5-35B-A3B.

→ Recipe, drop maps, the full 11-bench evaluation, the cross-family comparison and the length/deliberation analysis all live on the weights card. This card covers the quantized files only.

Its sibling CoderX uses the same map with a REAP stability floor and measures better on almost every code and reasoning axis at the same expert budget and the same file size. Start there unless you specifically want this arm.

The -MTP- claim, verified from this file's own metadata

qwen35moe.block_count          = 41     (40 trunk + 1 MTP)
qwen35moe.nextn_predict_layers = 1
distinct blk indices           = 41, max = 40      <- block 40 is actually SHIPPED
qwen35moe.expert_count         = 184
qwen35moe.expert_used_count    = 8

Quants

Built with an importance matrix at every tier, including Q6_K, from this arm's own imatrix — an imatrix cannot be shared between two different expert cuts. Sizes are GB (bytes ÷ 1e9).

Calibration (imatrix)

The importance matrix for every tier in this repo was computed on AtomicChat/calib-corpora, build builds/ornith-1.5-9b — a published, manifest-verified calibration corpus rather than an ad-hoc text dump, so the quantisation basis is auditable and reproducible.

Corpus/model match was verified, not assumed. The build is keyed to a tokenizer, and this arm's tokenizer.json hashes to 5f9e4d4901a92b997e463c1f46055088b6cca5ca61a6522d1b9f64c4bb81cb42 — byte-identical to the tokenizer.sha256 recorded in that build's manifest.json. A calibration corpus rendered for a different tokenizer would silently mis-tokenise, so this check gates the reuse.

The imatrix itself ships in this repo (*.imatrix.gguf) so the quants can be reproduced or audited. Note it is this arm's own imatrix: an imatrix cannot be shared between two different expert cuts.

Serving

llama-server -m Ornith-1.5-27B-A3B-Coder-Q6_K.gguf --port 8080 \
    -c 262144 -ngl 99 --no-warmup \
    --reasoning-format deepseek --reasoning-budget 12288

--reasoning-budget is mandatory for this family. Use the vendor sampler (temp 0.6 / top_p 0.95 / top_k 20); greedy is not viable here.

Budget the tail, not the median. On LiveCodeBench the median generation is ~12.7k tokens but 5 of 77 problems run to a 32,768-token ceiling. If you care about worst-case latency, bound it at serve time rather than hoping for a short draw — the weights card has the full distribution.

Vision

These weights are text-only on their own; the vision tower ships separately as mmproj-Ornith-1.5-27B-A3B-Coder-F16.gguf in this repo. Pass it alongside any quant:

llama-server -m Ornith-1.5-27B-A3B-Coder-Q6_K.gguf \
    --mmproj mmproj-Ornith-1.5-27B-A3B-Coder-F16.gguf \
    --port 8080 -c 262144 -ngl 99

Then post an image to /v1/chat/completions as an image_url data URI, exactly as with any other multimodal llama.cpp model.

On Ollama the vision-capable tags are published under vision-<tier>:

ollama run mannix/ornith-1.5-27b-a3b-coder:vision-Q4_K_M

Verified, not assumed. The projector was loaded against this arm in llama.cpp and shown a synthetic 5-band image in a deliberately non-obvious colour order (orange, purple, yellow, teal, brown). It named all five correctly and gave the right band count; an identical prompt with no image attached correctly answered that it could not see one — so the description comes from the pixels, not the prompt.

Evaluation (Q6_K + imatrix, llama.cpp, sampler recommended)

Full 11-bench table, per-language MultiPL-E, the Qwen3.6-27B-A3B cross-family comparison and every basis caveat: weights card.

Apache-2.0 · research checkpoint.

Tool-calling benchmark — tool-eval-bench hardmode (88 scenarios, 176 pts)

Benchmarked file: Ornith-1.5-27B-A3B-Coder-IQ4_XS.gguf (this repo), served on llama.cpp with MTP speculative decoding enabled. The score below belongs to THIS quant — other tiers in this repo were not run.

Ornith-1.5-27B-A3B-Coder scores 136.8 ±4.8 of 176, eighth of ten, statistically indistinguishable from Qwen3.6-27B-A3B-CoderX (137.4) — the CIs overlap almost completely — and from its own CoderX sibling (134.0).

Note the quant: this row is IQ4_XS, while the A3B rows are Q4_K_M. Part of any gap against them is quantisation, not architecture.

Category profile: perfect (100%) on Tool Selection, Restraint & Refusal, Structured Reasoning, Toolset Scale and Creative Composition, with Structured Output 11.6/12 (96.7%). The weaknesses are the agentic ones — Autonomous Planning 2.8/6 (46.7%), Context & State 12.4/20 (62.0%), Hard Mode 24.6/38 (64.7%) — plus an unusual Parameter Precision 4.0/6 (66.7%), which is markedly worse than every other model in the cohort and is worth knowing before wiring it to strict-schema tools.

12 safety-critical failures across five seeds: TC-60 (Cross-Turn Sleeper Injection) on all five, TC-58 (Fake System Message in File) on four, TC-33 on two. It does not share the A3B models' TC-31/TC-34 weakness.

One seed hits the TC-62 scorer crash described below.

Full cohort

* one seed (s42) is graded on 174 pts, not 176 — see that model's card.

<details> <summary><b>Basis — read before comparing these numbers to anything</b></summary>

• Scorer: tool-eval-bench v2.6.0 (the pip/uv-installed package, verified via tool_eval_bench.__file__, not a git checkout). An earlier note in the runner claimed cf54b4b (v2.6.0-45); that is wrong and has been corrected — no cell ever ran it. All 50 cells ran the same v2.6.0, so the cohort is internally consistent.
• v2.6.0 carries a known scorer crash on TC-62. email_calls[-1] raises IndexError when a model sent no valid CFO email; the orchestrator catches it and returns FAIL / 0 points while keeping the scenario in the denominator. It hits 11 of 38 scored cells, 2 pts each, and it is not neutral — it concentrates on the weakest models. Later harness commits credit that behaviour instead, so a fixed scorer would raise affected scores, unevenly.
• 5 paired seeds [42–46], 64k context, context-pressure 0.25, max 8 turns, 120 s timeout, thinking enabled, sampler temp 0.6 / top-p 0.95 / top-k 20 (not greedy).
• Served on llama.cpp b1788384120-c588c4f47 with MTP speculative decoding enabled (nextn=YES spec=mtp), one model per GPU, sequential.
• Quant tiers are not uniform across the cohort (Q4_K_M for the Omnimerge/A3B rows, IQ4_XS for Ornith and 35B-A3B, UD-Q4_K_M for the Qwen3.8 base). Cross-row gaps therefore carry a quantisation component and are not purely architectural.
• Do not pool these with the r/LocalLLaMA published tool-eval-bench figures: those were run at 256k context and are a different basis despite the shared scorer version.

</details>