license: mit
base_model:

• PocketAiHub/Ornith-1.5-35B-A3B-Abliterated
  base_model_relation: quantized
  pipeline_tag: image-text-to-text
  library_name: gguf
  tags:
• gguf
• ICE
• ICE-Tiers
• ice-quant
• 9-Tier-Standard
• 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?

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)

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

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

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:

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.
2. 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.
3. 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.

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-MTP-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

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

KLD_RESULTS.txt (raw llama-perplexity --kl-divergence output, both references),
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.