---

base_model: ManniX-ITA/Ornith-1.5-27B-A3B-Coder

base_model_relation: quantized

license: apache-2.0

pipeline_tag: text-generation

tags:

- gguf

- llama.cpp

- moe

- expert-pruning

- code

- imatrix

- mtp

- ornith

---

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).

| file | size | notes |

|---|---|---|

| Ornith-1.5-27B-A3B-Coder-F16.gguf | 52.47 GB | unquantised source for re-quantising |

| Ornith-1.5-27B-A3B-Coder-Q8_0.gguf | 27.91 GB | near-lossless reference |

| Ornith-1.5-27B-A3B-Coder-Q6_K_L.gguf | 21.81 GB | Q6_K with embed/output at Q8_0 |

| Ornith-1.5-27B-A3B-Coder-Q6_K.gguf | 21.57 GB | recommended; this is the eval tier |

| Ornith-1.5-27B-A3B-Coder-Q5_K_L.gguf | 19.04 GB | |

| Ornith-1.5-27B-A3B-Coder-Q5_K_M.gguf | 18.73 GB | |

| Ornith-1.5-27B-A3B-Coder-Q5_K_S.gguf | 18.16 GB | |

| Ornith-1.5-27B-A3B-Coder-Q4_K_L.gguf | 16.43 GB | Q4_K_M with embed/output at Q8_0 |

| Ornith-1.5-27B-A3B-Coder-Q4_K_M.gguf | 16.06 GB | default :latest on Ollama; fits 24 GB VRAM with context |

| Ornith-1.5-27B-A3B-Coder-Q4_K_S.gguf | 15.07 GB | |

| Ornith-1.5-27B-A3B-Coder-IQ4_NL.gguf | 15.01 GB | |

| Ornith-1.5-27B-A3B-Coder-IQ4_XS.gguf | 14.22 GB | best size/quality under 15 GB |

| Ornith-1.5-27B-A3B-Coder-Q3_K_XL.gguf | 13.17 GB | |

| Ornith-1.5-27B-A3B-Coder-Q3_K_L.gguf | 13.74 GB | |

| Ornith-1.5-27B-A3B-Coder-Q3_K_M.gguf | 12.72 GB | |

| Ornith-1.5-27B-A3B-Coder-Q3_K_S.gguf | 11.53 GB | |

| Ornith-1.5-27B-A3B-Coder-IQ3_M.gguf | 11.74 GB | |

| Ornith-1.5-27B-A3B-Coder-Q2_K_L.gguf | 10.36 GB | |

| Ornith-1.5-27B-A3B-Coder-IQ2_M.gguf | 9.06 GB | |

| Ornith-1.5-27B-A3B-Coder-IQ2_XS.gguf | 8.20 GB | smallest; expect real degradation |

| Ornith-1.5-27B-A3B-Coder.imatrix.gguf | 0.14 GB | this arm's own imatrix, for reproduction/audit |

| mmproj-Ornith-1.5-27B-A3B-Coder-F16.gguf | 0.90 GB | vision projector — see Vision below |

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 | AtomicChat/calib-corpora builds/ornith-1.5-9b |

| calib_train.txt | 3,079 documents · 4,958,490 tokens |

| chunks used | 9,686 (full file — no --chunks cap) |

| special tokens | --parse-special on (the corpus is chat-rendered; without it the chat control tokens never reach the statistics) |

| mixture | agentic 24.7% · code 17.8% · reasoning 14.8% · multilingual 13.8% · longctx 11.9% · vocab-sweep 9.9% · structured 3.9% · graphics 3.0% |

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)

| Benchmark | base 35B (256e) | Coder (184e) | CoderX (184e) |

|---|---|---|---|

| GPQA-Diamond (198) | 0.8283 | 0.7677 | 0.8131 |

| AIME (30) | 0.9000 | 0.9000 | 0.9667 |

| LiveCodeBench v6 (77 hard) | 0.6623 | 0.7273 | 0.7662 |

| HumanEval+ (164) | 0.8049 | 0.8293 | 0.8110 |

| MultiPL-E (300) | 0.8533 | 0.8300 | 0.8367 |

| Mean (11 benches) | 0.8252 | 0.8292 | 0.8386 |

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.

!Tool-calling benchmark

Full cohort

| model | quant | Total Points (mean, 5 seeds) | 95% CI | safety-critical (5 seeds) |

|---|---|---|---|---|

| Qwen3.8-27B-Omnimerge-v6 | Q4_K_M | 156.4 ±3.5 | [152.0, 160.8] | 3 |

| Qwen3.8-27B (base) | UD-Q4_K_M | 150.8 ±2.5 | [147.7, 153.9] | 9 |

| Ornith-1.5-35B | IQ4_XS | 146.2 ±2.6 | [143.0, 149.4] | 10 |

| Qwen3.6-27B-Omnimerge-v4 | Q4_K_M | 146.2 ±2.7 | [142.9, 149.5] | 16 |

| Qwen3.6-27B (base) | Q4_K_M | 144.0 ±3.4 | [139.8, 148.2] | 14 |

| Qwen3.6-35B-A3B (base) | IQ4_XS | 141.6 ±2.4 | [138.6, 144.6] | 15 |

| Qwen3.6-27B-A3B-CoderX | Q4_K_M | 137.4 ±4.9 | [131.3, 143.5] | 17 |

| Ornith-1.5-27B-A3B-Coder | IQ4_XS | 136.8 ±4.8 | [130.9, 142.7] | 12 |

| Ornith-1.5-27B-A3B-CoderX | IQ4_XS | 134.0 ±2.5 * | [130.8, 137.2] | 14 |

| Qwen3.6-27B-A3B-Coder | Q4_K_M | 123.2 ±2.3 | [120.4, 126.0] | 15 |

* 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>