---

license: mit

base_model:

• ornith-ai/Ornith-1.5-35B-A3B

base_model_relation: quantized

pipeline_tag: image-text-to-text

library_name: gguf

tags:

• gguf
• ICE
• ICE-Tiers
• ice-quant
• 9-Tier-Standard
• Tiel-Calibrated
• mtp
• speculative-decoding
• apex
• v2d-lite
• unsloth-dynamic
• moe
• vlm
• qwen35moe
• imatrix
• abliterated
• uncensored

---

Ornith-1.5-35B-A3B Abliterated — MTP + UD + ICE + APEX GGUF

Nine Pareto-optimal tiers of the abliterated model, each with an **MTP head grafted

in** from the original Ornith-1.5 (the abliterated source ships none).

Measured on wikitext-2-raw, 16 chunks x 2048 ctx, against two references, so

quantization damage and abliteration damage can be told apart.

How much did abliteration itself change the model?

| | |

|---|---:|

| Mean KLD (abliterated BF16 vs original BF16) | 0.0151 |

| same top-1 token | 95.04% |

For scale, the best quantization in this ladder costs ~0.022 KLD. **Abliteration is

a smaller perturbation than Q6_K quantization.**

Tiers (9/9 measured)

| tier | size | mean KLD | 99% KLD | 99.9% KLD | PPL ratio | same top-1 | active bpw | file bpw | overall |

|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|

| MTP-UD-Q6_K | 30.21 GB | 0.0222 | 0.226 | 0.695 | 0.9960 | 94.05% | 8.063 | 6.804 | 96.7 |

| MTP-UD-Q5_K_S | 25.84 GB | 0.0261 | 0.269 | 0.899 | 0.9871 | 93.51% | 7.693 | 5.820 | 96.3 |

| TIEL_Calibrated-25G-ICE | 24.85 GB | 0.0276 | 0.269 | 1.061 | 0.9847 | 93.33% | 7.686 | 5.597 | 96.1 |

| MTP-25G-ICE | 24.85 GB | ~~0.0293~~ 0.0281 | ~~0.300~~ 0.279 | ~~1.280~~ 1.097 | ~~0.9856~~ 0.9910 | ~~93.44%~~ 93.40% | 7.686 | 5.597 | ~~96.0~~ 96.1 |

| TIEL_Calibrated-23G-ICE | 22.84 GB | 0.0321 | 0.326 | 1.173 | 0.9907 | 92.89% | 7.523 | 5.143 | 95.7 |

| MTP-23G-ICE | 22.84 GB | ~~0.0345~~ 0.0329 | ~~0.345~~ 0.339 | ~~1.151~~ 1.240 | ~~0.9902~~ 0.9941 | ~~92.39%~~ 92.83% | 7.523 | 5.143 | ~~95.4~~ 95.6 |

| TIEL_Calibrated-21G-ICE | 20.85 GB | 0.0392 | 0.421 | 1.606 | 0.9997 | 92.27% | 7.357 | 4.695 | 95.0 |

| MTP-21G-ICE | 20.85 GB | ~~0.0398~~ 0.0395 | ~~0.400~~ 0.412 | ~~1.655~~ 1.726 | ~~0.9943~~ 0.9929 | ~~92.06%~~ 92.12% | 7.357 | 4.695 | ~~94.9~~ 95.0 |

| TIEL_Calibrated-19G-ICE | 18.82 GB | 0.0576 | 0.602 | 2.123 | 1.0015 | 90.42% | 7.192 | 4.240 | 93.3 |

| MTP-19G-ICE | 18.82 GB | ~~0.0612~~ 0.0585 | ~~0.630~~ 0.598 | ~~2.327~~ 1.955 | ~~1.0025~~ 0.9995 | ~~90.02%~~ 90.21% | 7.192 | 4.240 | ~~93.0~~ 93.2 |

| MTP-UD-IQ4_XS | 18.68 GB | 0.0706 | 0.695 | 2.645 | 1.0502 | 89.40% | 6.762 | 4.209 | 92.2 |

| MTP-APEX-I-Compact-v2D-lite | 17.57 GB | 0.0925 | 0.893 | 3.141 | 1.0169 | 87.97% | 5.228 | 3.956 | 90.5 |

| MTP-APEX-I-Mini-v2D-lite | 14.37 GB | 0.2546 | 2.410 | 5.578 | 1.2129 | 80.70% | 4.180 | 3.208 | 80.0 |

> The four ICE rows were rebuilt on 2026-08-24 and the struck-through values

> are what they replaced. The new figures are measured at 64 chunks, the rest of

> the table at 16; a higher chunk count tightens the error bar without moving the

> mean. What changed, why, and the full measurement set:

> MEASUREMENTS-ICE-rebuild.md.

> The four TIEL_Calibrated rows were added on 2026-08-30. Identical builds to

> their MTP-*-ICE counterparts — same abliterated trunk, same norm-fixed MTPv2

> head, same ICE-base recipes, same byte budgets — but built with the importance

> matrix and embedded chat template from

> peculiar-ragdoll/Tiel-Coder-35B-A3B-GGUF,

> with credit to that repo for both. Its imatrix is 3000 x 512 = 1,536,000 tokens

> against the 573 x 512 = 293,376 behind the others. Also measured at 64 chunks.

Sorted best -> worst by overall (BF16 = 100), the same composite used on the

non-abliterated card:

0.70/(1+meanKLD) + 0.30*sameTop1. All KLD columns are measured

against the abliterated BF16, i.e. they isolate what the quantization costs.

> Read the tail columns with care. 99.9% KLD is the ~33rd-worst token out of

> 32,768 — an extreme order statistic with large sampling variance, so it inverts

> between adjacent tiers without that meaning anything. 99% KLD rests on ~328 tokens

> and orders all nine tiers monotonically; mean KLD uses all 32,768 and separates the

> closest pair by 4.3 sigma. Rank on mean KLD; treat the tail columns as shape,

> not order.

Abliterated vs non-abliterated, same recipe

Both columns are the original 16-chunk measurements, so the delta isolates

abliteration. The four ICE tiers have since been rebuilt; their current numbers

are in the tier table above and in

MEASUREMENTS-ICE-rebuild.md.

| tier | KLD<br>abl | KLD<br>clean | Δ | top-1<br>abl | top-1<br>clean | Δ | PPL ratio<br>abl | PPL ratio<br>clean | Δ |

|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|

| MTP-UD-Q6_K | 0.0222 | 0.0221 | +0.6% | 94.05% | 93.85% | +0.20 pp | 0.9960 | 0.9957 | +0.0003 |

| MTP-UD-Q5_K_S | 0.0261 | 0.0272 | -4.2% | 93.51% | 93.51% | +0.01 pp | 0.9871 | 0.9862 | +0.0009 |

| MTP-25G-ICE | 0.0293 | 0.0303 | -3.4% | 93.44% | 93.16% | +0.28 pp | 0.9856 | 0.9814 | +0.0042 |

| MTP-23G-ICE | 0.0345 | 0.0361 | -4.4% | 92.39% | 92.65% | -0.26 pp | 0.9902 | 0.9885 | +0.0018 |

| MTP-21G-ICE | 0.0398 | 0.0412 | -3.4% | 92.06% | 92.03% | +0.03 pp | 0.9943 | 0.9924 | +0.0019 |

| MTP-19G-ICE | 0.0612 | 0.0608 | +0.5% | 90.02% | 90.32% | -0.30 pp | 1.0025 | 1.0030 | -0.0005 |

| MTP-UD-IQ4_XS | 0.0706 | 0.0723 | -2.4% | 89.40% | 89.46% | -0.06 pp | 1.0502 | 1.0526 | -0.0024 |

| MTP-APEX-I-Compact-v2D-lite | 0.0925 | 0.0954 | -3.0% | 87.97% | 87.83% | +0.14 pp | 1.0169 | 1.0101 | +0.0068 |

| MTP-APEX-I-Mini-v2D-lite | 0.2546 | 0.2608 | -2.4% | 80.70% | 80.49% | +0.21 pp | 1.2129 | 1.2281 | -0.0151 |

Δ is near zero or slightly negative across the ladder: **abliteration does not make

this model harder to quantize**, and in the mid-range it is marginally easier —

plausibly because projecting a direction out of ffn_down narrows its dynamic range.

Two further results, measured against the original BF16 as well (full numbers in

KLD_RESULTS.txt):

• The two damages are strongly sub-additive — abliteration and quantization are

largely orthogonal, so the combined figure sits far below their sum.

• No tier un-abliterates. Across the 9 rungs measured, the distance to the original BF16 stays above abliteration's own distance (0.0151), so quantization never pulls the model back toward the refusal behaviour.

Which tier is which

| family | what it is |

|---|---|

| UD-* | Unsloth Dynamic 2.0 maps, replayed 1:1. Pins attention, the shared expert and token_embd at Q8_0 at every size and moves only the routed experts. |

| ICE- | Bits allocated by how far a quantization error travels*, not by activation magnitude. Named by target size. |

| *APEX-I--v2D-lite** | mudler's APEX maps plus one extra step on attn_k/attn_v in the ten full-attention blocks and on the output head. |

Rule of thumb: Q6_K / Q5_K_S near-lossless, 25G/23G-ICE the quality sweet

spot, 21G/19G-ICE the best small tiers, Compact/Mini only if you are tight on

VRAM — Mini drops sharply.

The ICE tier, and why this is a 9-tier release

ICE allocates bits by error travel distance: how far a quantization error

propagates before it reaches the output. Tensors writing straight into the residual

stream, the always-on dense path, and the router are protected; the routed expert

stack — 93% of the parameters but only 8-of-256 active per token — is left uniform.

Routers stay F32, and the draft block is un-pinned so its experts follow the tier.

On the non-abliterated ladder, measured on this model and this harness, ICE lands

ahead at matched size:

| comparison | result |

|---|---|

| 23G-ICE vs UD-Q4_K_XL (same size) | -5.0% KLD |

| 23G-ICE vs APEX-I-Quality | -13.0% KLD and 0.61 GB smaller |

| 25G-ICE vs APEX-I-Balanced | -12.2% KLD and 1.15 GB smaller |

UD-Q4_K_XL,APEX-I-Quality-v2D-lite and APEX-I-Balanced-v2D-lite are each already covered by

an ICE tier that is both smaller and closer to BF16, so rebuilding them would add size

without adding a quality point. Full derivation, the refuted ffn_down rule

and the measured convexity bound are in the

original Ornith-1.5 card.

> ### The 9-Tier Standard

> From this release onward these nine recipes are the standard ladder:

> UD-Q6_K · UD-Q5_K_S · 25G-ICE · 23G-ICE · 21G-ICE · 19G-ICE ·

> UD-IQ4_XS · APEX-I-Compact-v2D-lite · APEX-I-Mini-v2D-lite

>

> They are the measured Pareto frontier of a 12-tier sweep on this architecture:

> every dropped tier is beaten on both size and KLD by one that ships. Reference

> measurements and methodology:

> Ornith-1.5-35B-MTP-UD-APEX-GGUF.

What was done to the source

PocketAiHub/Ornith-1.5-35B-A3B-Abliterated-GGUF is faithful at the tensor level —

every tensor except ffn_down is byte-identical to ornith-ai's BF16. Abliteration is

confined to ffn_down (both routed and shared experts), layers 15-39, at

~1.6-1.9e-02 L1-relative. Routers, attention, ffn_gate, ffn_up and layers 0-14

are untouched.

Three things were repaired while grafting:

1. MTP head restored — 20 blk.40.* tensors from ornith-ai's BF16;

block_count 40 -> 41, nextn_predict_layers added.

1. tokenizer.ggml.add_bos_token restored to False — the source omits the key

entirely, so llama.cpp falls back to its own default and tokenises differently

from the original. Left unfixed this also invalidates any KLD against the original.

1. tokenizer.chat_template restored — the source ships a 7536-byte copy with

the multi-system-message merge block removed; the original is 7828 bytes.

imatrix

bartowski's Ornith-1.5-35B-A3B-imatrix.gguf, reused unmodified. Justified by

measurement, not assumption: **the routers are byte-identical between the original

and the abliterated model**, so the same experts fire and the per-channel statistics

still apply.

Recipes

All nine tensor maps were confirmed byte-exact against the corresponding shipped

non-abliterated tier before this build, so the two ladders are directly comparable:

• UD — replayed 1:1 from unsloth/Ornith-1.0-35B-GGUF (Unsloth Dynamic 2.0).
• APEX v2D-lite — mudler's Ornith-1.5 maps, with attn_k/attn_v on the ten

full-attention blocks and the output head each raised one step.

• ICE — bits allocated by how far a quantization error travels rather than by

activation magnitude; the expert stack is uniform and the draft block un-pinned.

MTP / speculative decoding

Every tier carries the head, pinned Q8_0 (F16 attn_k/attn_v on the ICE tiers).

It is grafted from the original model, so **the draft head is not abliterated while

the trunk is** — worth knowing if you rely on the refusal behaviour under drafting:

the head only proposes, the abliterated trunk verifies, so accepted tokens are always

the trunk's.

Measured draft acceptance: 96.77% (390/403) on 23G-ICE, --spec-type draft-mtp, text-only.

| prompt set | acceptance |

|---|---:|

| code-novel | 98.45% |

| structured | 98.29% |

| copy-edit | 97.30% |

| prose-novel | 91.57% |

Raw run in gate_spec_bench.json. Acceptance depends on the prompt mix — compare only against numbers taken on the same harness.

llama-server -m Ornith-1.5-35B-A3B-Abliterated-MTPv2-21G-ICE.gguf \
  --mmproj mmproj-Ornith-1.5-35B-A3B-Abliterated-F16.gguf \
  -c 8192 -fa on --jinja \
  --spec-type draft-mtp --spec-draft-n-max 1 --spec-draft-n-min 0 --spec-draft-p-min 0.75

MTPv2 — updated draft head (2026-08-23)

ornith-ai uploaded a newly trained native MTP head to the official

Ornith-1.5-35B-A3B repo, so this

repo is updated with it.

The nine MTPv2-* files are the same nine recipes as the previous ladder — same

abliterated trunk, same imatrix, same per-tensor rule files, same pinned llama.cpp build.

Only blk.40 is re-grafted. Every tensor outside blk.40, and the whole metadata block,

is byte-identical to its MTPv1/ counterpart — checked tensor by tensor before anything

was uploaded.

The four ICE tiers were later rebuilt again, on 2026-08-24, with a revised expert

placement and a corrected blk.40 norm conversion. Their rows in the tier table show

both the old and the new numbers.

What actually changed. The head in the original checkpoint had initializer-like

weights; the replacement is trained. Read straight off the two published files:

| blk.40 tensor | MTPv1 | MTPv2 |

|---|---:|---:|

| nextn.shared_head_norm.weight | 1.02281 ± 0.00017 | 2.92531 ± 0.29643 |

| attn_norm.weight | 1.00006 ± 0.00302 | 0.90492 ± 0.15616 |

| nextn.hnorm.weight | 1.01571 ± 0.00558 | 0.49370 ± 0.09479 |

| attn_q_norm.weight | 1.00390 ± 0.00680 | 1.76704 ± 0.33864 |

The previous ladder is unchanged and still here, under MTPv1/ — same files, same

digests, nothing deleted.

Standalone draft head

The new head is also published on its own, for use as a --model-draft sidecar instead

of an embedded head:

| file | size |

|---|---:|

| mtp-Ornith-1.5-35B-A3B-Abliterated-MTPv2-BF16.gguf | 3.74 GB |

| mtp-Ornith-1.5-35B-A3B-Abliterated-MTPv2-Q8_0.gguf | 1.99 GB |

| mtp-Ornith-1.5-35B-A3B-Abliterated-MTPv2-Q4_K_M.gguf | 1.26 GB |

The BF16 one is a straight extraction from the same grafted master the ladder was

quantized from, so its head is bit-identical to the head inside the MTPv2-* files.

Vision

Not re-hosted — use the projector from the source repo:

mmproj-Ornith-1.5-35B-A3B-Abliterated-F16.gguf

(0.90 GB). Without it the model is blind. Note --mmproj force-disables ctx_shift

and cache_reuse.

Also included

sha256sums-MTPv2.txt and MANIFEST-MTPv2.txt for the current ladder,

MEASUREMENTS-ICE-rebuild.md for the 2026-08-24 ICE rebuild, and the MTPv1

originals under MTPv1/ (KLD_RESULTS.txt, gate_spec_bench.json,

sha256sums.txt, MANIFEST.txt).

The BF16 masters are not re-hosted: the abliterated source is at

PocketAiHub

and the original at ornith-ai.

Credit: PocketAiHub for the abliteration, bartowski for the imatrix and for

publishing its corpus, mudler for the APEX reference maps, Unsloth for the

UD 2.0 maps, Shisa-AI for Final 12K KL distill MTP Head (ShisaMTP variants), Jzinno for Dflash2 sidecar, peculiar-ragdoll for Tiel iMatrix Calibration and Sharp Chat Template.