protoLabsAI/Ornith-1.5-9B-MTP-GGUF

Ornith-1.5-9B MTP — GGUF (llama.cpp speculative decoding)

GGUF builds of ornith-ai/Ornith-1.5-9B with a distilled MTP draft head baked into the trunk — llama.cpp does lossless multi-token self-speculative decoding out of the box, no separate draft model to wire up. Every file here carries the nextn head, so --spec-type draft-mtp just works.

Ornith ships 1.5-9B with mtp_num_hidden_layers: 1 in config.json but none of the mtp.* weights — 0 of 760 tensors. So the stock GGUFs (official and third-party) have no MTP head and can't speculate. These do.

• Blackwell (RTX 50xx / PRO 6000): use NVFP4. 6.5 GB, and the fastest rung here — 299 tok/s with MTP (1.38×). MTP's verify step is nearly free on FP4 tensor cores and costs real time on the K-quant dequant path, so the two compound.
• 6 GB card? Use IQ4_XS (5.45 GB). It is smaller than Q4_K_M, faster, and takes a bigger MTP gain (1.21× vs 1.06×).
• ⚠️ IQ2_M degenerates on long generations — do not use it for prose or code. It repeats itself in 28% of 1500-token generations at llama.cpp's default sampling. IQ3_M is marginal (7.8% at defaults, clean with the recommended sampler). Measured table below.
• Use Q8_0 for reference quality. It takes the largest relative MTP gain, because its baseline is the most bandwidth-bound (1.57×–1.77× depending on prompt mix) — but it is still slower in absolute terms than NVFP4.

Q4_K_M is no longer the low-VRAM recommendation. An earlier version of this card said it was. IQ4_XS beats it on size, speed and MTP gain — measured, table below.

Want the base with no MTP head? ornith-ai/Ornith-1.5-9B-GGUF.

Files

File	Size	Form	Use
Ornith-1.5-9B-MTP-NVFP4.gguf	6.5 GB	bundled	Blackwell: fastest rung (299 tok/s, 1.38×)
Ornith-1.5-9B-MTP-Q8_0.gguf	9.8 GB	bundled	largest relative MTP gain, reference quality
Ornith-1.5-9B-MTP-Q6_K.gguf	7.6 GB	bundled	near-lossless quant
Ornith-1.5-9B-MTP-Q5_K_M.gguf	6.6 GB	bundled	balanced quality
Ornith-1.5-9B-MTP-Q4_K_M.gguf	5.8 GB	bundled	superseded by IQ4_XS — see above
Ornith-1.5-9B-MTP-IQ4_XS.gguf	5.45 GB	bundled (imatrix)	best low-VRAM rung, fits 6 GB
Ornith-1.5-9B-MTP-IQ3_M.gguf	4.67 GB	bundled (imatrix)	6 GB with room for context; needs the recommended sampler
Ornith-1.5-9B-MTP-IQ2_M.gguf	3.87 GB	bundled (imatrix)	smallest; ⚠️ degenerates on long output
Ornith-1.5-9B-MTP-BF16.gguf	18.4 GB	bundled (master)	re-quantize from this
mmproj-Ornith-1.5-9B-BF16.gguf	922 MB	vision projector	only for image input — costs ~1.1 GB VRAM resident
mtp-head/mtp-Ornith-1.5-9B-head-Q8_0.gguf	2.4 GB	standalone head	attach to a base GGUF via --model-draft

"Bundled" = trunk + nextn head in one file. The standalone head is not a model — loading mtp-head/… directly will crash. It exists only to pair with a base Ornith-1.5-9B GGUF.

Ornith-1.5-9B is a vision model; pair any rung with mmproj-… for image input. Verified working with MTP enabled.

Run

# Text-only. Do NOT load the mmproj unless you are actually sending images —
# it costs ~1.1 GB of VRAM you probably need. See "VRAM" below.
# -fit off + an explicit -ngl is deliberate: MTP inverts if even one layer
# lands on the CPU, and --fit (on by default) will move layers silently.
# The sampler flags are NOT optional — see "Sampling" below. llama.cpp's
# defaults apply no repetition control and measurably increase looping.
llama-server --model Ornith-1.5-9B-MTP-Q6_K.gguf \
  --n-gpu-layers 99 -fit off --ctx-size 8192 --flash-attn on --jinja \
  --temp 1.0 --top-k 20 --top-p 0.95 --min-p 0.0 --presence-penalty 1.5 \
  --spec-type draft-mtp --spec-draft-n-max 3

Confirm offloaded 34/34 layers to GPU in the startup log. If it says anything less, fix that before judging MTP's speed — see VRAM.

For image input, add the projector:

  --mmproj mmproj-Ornith-1.5-9B-BF16.gguf

--spec-draft-n-max is the draft depth: 2 maximizes acceptance, 3 maximizes throughput, 4 regresses. Same shape our 1.0 head showed, reproduced independently here.

Standalone draft — pair the small head with any base Ornith-1.5-9B GGUF:

llama-server --model ornith-1.5-9b-Q4_K_M.gguf \
  --model-draft mtp-head/mtp-Ornith-1.5-9B-head-Q8_0.gguf \
  --spec-type draft-mtp --spec-draft-n-max 3 --n-gpu-layers 99 --flash-attn on --jinja

Sampling — pass these flags, they are load-bearing

Ornith recommends presence_penalty 1.5 (temp 1.0, top_k 20, top_p 0.95, min_p 0) for general use, and temp 0.6 / presence_penalty 0 for precise coding. The base repo ships no generation_config.json, and llama.cpp would not read one anyway — its defaults are temp 0.8, top_k 40, top_p 0.95, min_p 0.05, presence_penalty 0, i.e. no repetition control at all. Earlier versions of this card shipped a command line with no sampler flags, so every user got exactly that.

Measured on IQ2_M, 64 samples per arm (8 prompts x 8 seeds, 1500-token generations):

sampling	degenerate	rate
Ornith-recommended (temp 1.0 … presence_penalty 1.5)	3 / 64	4.7%
llama.cpp defaults	18 / 64	28.1%
greedy (temp 0)	24 / 64	37.5%

Fisher two-sided p = 5.5e-4 vs defaults. Low temperature makes looping worse on this family — the opposite of the usual advice. This is mitigation, not a cure: the rung matters more (below).

VRAM — MTP requires a FULL offload, and the cliff is one layer wide

MTP is not free in memory: the nextn head adds a layer and speculation adds a draft context. Measured resident VRAM, Q6_K, --ctx-size 4096, --n-gpu-layers 99, flash-attn on:

configuration                  resident VRAM   delta
---------------------------    -------------   -----
trunk only                        7.5 GB         --
trunk + MTP                       8.2 GB       +0.7 GB
trunk + mmproj                    8.6 GB       +1.1 GB
trunk + mmproj + MTP              9.3 GB       +1.9 GB

Q8_0 adds roughly 2 GB on top of every row.

⚠️ If MTP makes it slower, you are almost certainly not fully offloaded

MTP inverts the moment any layer lands on the CPU. This model has 34 offloadable layers (32 trunk + the nextn head + output). Measured, Q6_K, ctx 8192, -fit off:

-ngl      no-MTP pp   no-MTP tg   +MTP pp   +MTP tg    MTP verdict
------    ---------   ---------   -------   -------    -----------
34 / 99        8543       171.4      6537     235.1    +37%  WIN
33             5892        77.3      5438      56.0    -28%  LOSS
28             3066        20.2      2846      12.1    -40%  LOSS
24             2150        13.9      2044       8.2    -41%  LOSS

One layer short is enough. At -ngl 33 decode has already fallen 55% and MTP has flipped from a 37% speedup to a 28% slowdown. Speculation drafts and verifies every step, so each round trip pays the host-boundary cost again — partial offload hurts MTP roughly twice as much as it hurts plain decode.

The cause is --fit, which is ON by default. It silently adjusts any argument you left unset — including -ngl — to fit device memory with a 1024 MiB margin. Enabling MTP raises the estimate, so --fit quietly shaves the layers that MTP needs. A machine reporting comfortable free VRAM is not evidence against this: --fit keeps usage under budget precisely by moving layers to the host. Confirmed in the field — this was diagnosed from a user report on a 16 GB card sitting at 12.6 GB used, and -fit off with an explicit -ngl 99 fixed it.

Diagnose it: check the offloaded X/Y layers to GPU line at startup. Anything but 34/34 explains the slowdown by itself.

Fix, in order: pass -fit off together with an explicit -ngl 99 · drop --mmproj if you are not sending images (−1.1 GB) · move to IQ4_XS (−1.6 GB vs Q6_K, and faster anyway) · lower --ctx-size.

A second, rarer cause is a driver spilling VRAM into system RAM instead of failing to load (silent on Windows/WDDM, and possible via the GTT heap on AMD/Vulkan). Same symptom, same fixes — and the startup layer line still tells you which one you are looking at.

Coming from Ornith-1.0-9B-MTP? The trunk files are byte-identical in size, but 1.0 has no mmproj. A 1.0 command line that fit your card can push 1.5 over — same rung, +1.1 GB — and with --fit on, "over" means layers quietly move to the CPU rather than an error.

The finding: a grafted head was NOT good enough this time

Our Ornith-1.0-9B head transferred from base Qwen3.5-9B almost intact — Ornith-1.0 was a light enough fine-tune that base-Qwen's residual stream survived, and the raw graft hit 0.74–0.76 acceptance with zero training.

Ornith-1.5 breaks that. Its end-to-end RL self-improvement loop moved the hidden states much further, and the same graft lands 0.09–0.13 lower at every draft depth. Re-distilling the head against Ornith-1.5's own generations (KL distribution-match, 492 steps) recovers all of it:

n-max   graft   distilled   Δ        1.0's shipped head
-----   -----   ---------   ------   ------------------
  2     0.636     0.767     +0.131         0.766
  3     0.528     0.663     +0.135         0.651
  4     0.473     0.583     +0.110         0.565

The lesson generalizes: how well an MTP head transplants is a function of how far the fine-tune moved the residual stream. A light SFT keeps the donor head usable; a heavy RL loop does not. Measure acceptance before assuming a graft is enough — the head loads and generates correctly either way, so nothing but the acceptance rate tells you.

Objective matters too: on 1.0, hard-CE distillation regressed the graft (0.763 → 0.721) by over-sharpening the argmax. MTP acceptance is rejection sampling against the target, so it rewards distribution match, not token fit. KL is the correct objective; this build uses it.

Benchmarks

RTX PRO 6000 Blackwell (sm120), ctx 8192, flash-attn, greedy, 6-prompt code+general mix, -n 200, quiet GPU. Single-stream (C=1) — see the caveat below.

Q8_0, n-max sweep

config	decode tok/s	acceptance	speedup
base (no MTP)	149.6	—	1.00×
MTP n-max 2	252.6	0.767	1.69×
MTP n-max 3	264.5	0.663	1.77×
MTP n-max 4	256.2	0.583	1.71×

Across the full ladder, n-max 3

Every row below was measured in one session on one box with one prompt mix, so the rows are comparable to each other:

rung	size	base tok/s	+MTP tok/s	speedup	acceptance
NVFP4	6.53 GB	216.1	299.1	1.38×	0.599
IQ2_M	3.87 GB	260.7	276.8	1.06×	0.558
IQ4_XS	5.45 GB	228.4	276.9	1.21×	0.525
IQ3_M	4.67 GB	233.8	257.0	1.10×	0.507
Q6_K	7.56 GB	171.6	249.7	1.46×	0.541
Q8_0	9.79 GB	150.5	236.4	1.57×	0.543
Q4_K_M	5.78 GB	203.4	215.7	1.06×	0.544

NVFP4 is the fastest rung outright, and it is not simply "4-bit is small": IQ4_XS and IQ2_M are smaller and still slower with MTP on. FP4 sits on Blackwell's tensor-core GEMM path, where MTP's parallel verify is nearly free, while K-quants and i-quants pay that verify on the dequant path. The speedup ratio still grows with precision (Q8_0 1.57×) because Q8_0's baseline is the most bandwidth-bound — but ratio and absolute speed point at different files, and what you want to run is the fast one.

Why Q8_0 reads 1.57× here and 1.77× in the sweep above: different prompt mix, and acceptance moved with it (0.543 vs 0.663). MTP speedup is a function of how predictable your text is, so compare rows within a table, never across the two.

⚠️ Q4_K_M + MTP regresses on creative prose

Acceptance tracks predictability, and it collapses on open-ended prose (0.310 at n-max 3). On Q4_K_M that is below the break-even point — the verify costs more than speculation saves:

Q4_K_M, n-max 3     no-MTP    +MTP     acceptance
code                 206.6    256.4      0.702
math                 206.8    286.3      0.820
structured           206.6    239.1      0.631
creative prose       206.6    159.4      0.310   <- 23% SLOWER

On Q8_0 prose still nets positive (195.8 vs 149.6) because the baseline is slower to begin with. If your workload is mostly long-form creative writing on Q4_K_M, run without --spec-type.

Prompt processing

The tables above are decode. Prompt processing was not measured for the initial release; it is measured here because MTP's cost lands there. Q6_K, ctx 16384, fully GPU-resident:

prompt tokens	no-MTP pp tok/s	+MTP pp tok/s	no-MTP tg tok/s	+MTP tg tok/s
2,548	8,059	6,793	171.8	231.9
10,951	7,982	7,227	168.3	272.0

MTP costs 10–28% of prompt-processing throughput and pays it back in decode. That is the whole of its inherent cost when the model fits in VRAM. A prompt-processing drop measured in multiples is the memory-overflow case above, not this.

Methodology caveat — read before quoting these

These are single-stream (C=1) numbers, which our house rule normally bars from a model card, because speculative-decoding wins are known to compress or invert under concurrent load (our dFlash finding: a +43% single-stream win became 3× slower than MTP at C=32). We publish C=1 here because llama.cpp/GGUF deployment is overwhelmingly single-user and local, which makes C=1 the honest representative regime for this artifact — but do not carry these numbers over to a batched server. No concurrency sweep was run for this release.

Scorecard

Measured on the NVFP4 build of these same weights (protoLabsAI/Ornith-1.5-9B-NVFP4) — the quantization differs from the GGUF rungs here, so treat these as characterising the model, not any specific rung. Judge-free except claw (independent cloud judge, 0 fallbacks):

axis            score   kind                detail
--------------  -----   ------------------  ------
function_call   0.963   schema-checked      52/54 · best on our internal board
claw            0.675   agentic/LLM-judged  10 tasks · robustness 1.00 · safety-clean
reasoning_hard  0.611   solver-verified     5/9 full-pass
livecodebench   0.115   exec-graded         hard-only, 30 problems, thinking-off

Tool calling is this model's strength — 0.963 is the highest function_call score across ~26 scorecards we have run, spanning 9B to 397B including dedicated coder models.

Code generation is its weakness, and it is the model rather than any quantization. 12 of 30 problems earned partial credit, none passed every test, and 13 of 30 consumed the entire 32,768-token budget deliberating without emitting working code. Users report the same independently across the Ornith-1.5 family. Enabling thinking does not rescue it: paired on identical problems (on the 35B sibling) thinking-on scored worse than thinking-off, 0.129 vs 0.329, while exhausting the budget on 6 of 7.

If you are picking a rung for agentic/tool work, any of these are a good choice. If you are picking one for code generation, this family is not the right model at any precision.

"Lossless" — read this

MTP speculative decoding is distribution-lossless: every drafted token is verified against the target, so the output distribution is unchanged. It is not bitwise-identical to plain decode at greedy/temp 0 — the batched verify computes target logits in a different floating-point reduction order than sequential decode, which can flip a greedy argmax and fork the text. Both outputs are equally valid; this is expected llama.cpp behavior, not a defect of these weights.

Troubleshooting: wrong number of tensors expected 442 got 427

The gap is the 15 mtp.* head tensors. This happens if you convert the base ornith-ai/Ornith-1.5-9B directly without grafting a head first: the base keeps mtp_num_hidden_layers: 1 in config.json but ships none of the mtp.* weights, so the converter declares a blk.32 MTP layer and leaves those 15 tensors empty.

Fix: graft the head into the trunk before converting, then convert with no --mtp flag. (Only 4 of the 15 land as blk.32.nextn.*; the other 11 become ordinary blk.32.*, so grep nextn shows 4 but the head is complete.) Or run the stock base GGUF with --model-draft mtp-head/mtp-Ornith-1.5-9B-head-Q8_0.gguf.

MTP-baked GGUFs need recent runtimes. Old Ollama (≤~0.30) fails with layer 32 missing attn_qkv; update and re-pull.

How these were built

# 1. graft Qwen3.5-9B's 15 mtp.* tensors into the Ornith-1.5 trunk
python graft.py --donor Qwen/Qwen3.5-9B --target ornith-ai/Ornith-1.5-9B \
                --out ornith-1.5-9b-mtp-graft --dtype bfloat16
# 2. corpus = Ornith-1.5's OWN generations (3942 samples, no-think, T=0.7)
python gen_corpus.py --url <served-1.5> --model ornith15 --out corpus.jsonl
# 3. distill: freeze base, train ONLY the 15 mtp.* tensors, KL objective
python distill.py --config configs/ornith-1.5-9b.yaml     # 492 steps, loss 0.889 -> 0.357
# 4. convert (remaps mtp.* -> blk.32.nextn.* automatically) + quantize
python convert_hf_to_gguf.py ornith-1.5-9b-mtp --outfile ...-BF16.gguf --outtype bf16
llama-quantize ...-BF16.gguf ...-Q4_K_M.gguf Q4_K_M

Recipe: experiments/mtp/ — the scripts retarget to any Qwen3.5-family fine-tune by swapping a config.

The i-quant rungs, and what the MTP head does at low bit depth

The IQ rungs are i-quants (importance-matrix calibrated) with the MTP head pinned to Q8_0. That pin is load-bearing for a non-obvious reason: a plain forward pass never activates the nextn head, so the importance matrix contains no data at all for those 15 tensors. Left unpinned they would be i-quantized blind — on the one tensor group where that costs the most, since a degraded draft loses acceptance on every token. (output.weight and token_embd.weight are likewise absent from the imatrix and fall back to llama.cpp's defaults.)

Calibration corpus: ~2 MB, 70% Ornith-1.5-9B's own generations (agentic/business, coding, general chat — the same corpus the MTP head was distilled against) and 30% literary prose. The prose share is deliberate: instruct output is register-narrow and creative writing is the first thing to go at 3 bits.

MTP does not invert at low bit depth. The Q8_0 → Q4_K_M decay (1.57× → 1.06× on the ladder mix) looks like a trend heading for a regression at 3 and 2 bits. It is not one — acceptance holds in a 0.51–0.60 band across the entire ladder and every rung is net-positive with the head on.

Coherence at low bit depth

Needle recall + degeneration detectors at 4K / 16K / 32K context, plus a verifiable word problem:

rung	4K	16K	32K	word problem (answer 17:05)
NVFP4	clean	clean	clean	correct, with a distance check
IQ4_XS	clean	clean	clean	correct, with a distance check
IQ3_M	clean	clean	clean	correct, with a distance check
IQ2_M	clean	clean	clean	correct, with a distance check

Needle exact at every depth on every rung. IQ2_M solves the two-train problem correctly and verifies its own answer at 2.7 bpw.

⚠️ That table is not enough, and an earlier version of this card over-claimed from it

Those are short outputs. Degeneration on this family scales with generation length, not context depth — so a needle probe can come back perfectly clean on a rung that falls apart when asked for 1500 tokens of prose. This card previously called IQ2_M "genuinely usable" and "still coherent" on the strength of the table above. That was wrong.

Re-measured with long generations (1500 tokens, 8 prompts x 8 seeds = 64 per cell, three overlapping degeneration detectors):

rung	Ornith-recommended sampling	llama.cpp defaults	Fisher p
IQ4_XS	0 / 64	0 / 64	1.0
IQ3_M	0 / 64	5 / 64 (7.8%)	0.058
IQ2_M	3 / 64 (4.7%)	18 / 64 (28%)	5.5e-4

A wider sweep — every sampling arm, context depths to 31k, thinking on and off, 316 generations in all — splits the same way:

• every rung from IQ4_XS up: 0 failures out of 234
• IQ2_M: 14 out of 82

Fisher p = 2.6e-9. Q8_0, Q6_K, Q4_K_M and IQ4_XS never degenerated once.

What IQ2_M emits when it goes: summer summer summer … x114 · __class__ x66 · from typing import List, Optional, TypeVar, Generic, Iterator x12. Not a marginal metric call.

Guidance: IQ4_XS is safe at any sampler. IQ3_M is fine with the recommended sampler and marginal without it. IQ2_M should be treated as a size-constrained curiosity, not a general-purpose rung — the recommended sampler cuts its failure rate 6x but does not clear it.

Harness, raw JSONL and the full write-up: experiments/quantize/looping/.

A different failure that also gets called "looping"

On hard coding problems this model will argue with itself — "Hmm. Wait no. Let me reconsider… Actually, the—" — until it consumes the entire token budget. We reproduced one at 32,768 tokens with thinking off, and its repetition score was near zero: it never repeats, it just never stops.

We tried to fix this with sampling and it does not work. A graded LiveCodeBench re-run on these weights — 30 hard problems, 3 trials per arm — comparing our eval's temp 0.2 against Ornith's documented temp 0.6 for precise coding:

sampling	LCB mean (3 trials)	hit the 32k cap	mean tokens
temp 0.2	0.128 / 0.155 / 0.147 → 0.143	12.3 / 30	14,400
temp 0.6	0.125 / 0.168 / 0.095 → 0.129	14.3 / 30	17,100

Paired per-problem across all 30 tasks, p = 0.69 — no effect, and temp 0.6 was if anything slightly worse. An earlier version of this card recommended temp 0.6 for coding here on the strength of an n=8 probe on the Q6_K GGUF; that signal did not replicate at proper power and the recommendation is withdrawn.

So: sampling fixes the repetition failure (see the rung table above) but not this one. Every capped problem still emitted an extractable code block (no_code_in_budget = 0), so the low score is wrong answers, not missing ones. If your looping is specifically on code generation, no sampler setting will help — this family is strong at tool calling and weak at code.

Want the vLLM build? protoLabsAI/Ornith-1.5-9B-NVFP4 — W4A4 NVFP4 for vLLM on Blackwell, same distilled MTP head, vision verified against the bf16 source.

Provenance & license

• Base: ornith-ai/Ornith-1.5-9B (MIT) — a dense Qwen3.5-9B-architecture hybrid (linear + full attention) VL fine-tune, trained with end-to-end RL self-improvement.
• MTP head: grafted from Qwen/Qwen3.5-9B (Apache-2.0), then KL-distilled against Ornith-1.5-9B's own hidden states.
• These GGUFs derive from both; MIT. Built by protoLabs.studio.