---

license: mit

base_model: deepreinforce-ai/Ornith-1.0-9B

base_model_relation: quantized

tags:

- gguf

- llama.cpp

- speculative-decoding

- mtp

- multi-token-prediction

- qwen3.5

pipeline_tag: text-generation

---

Ornith-1.0-9B MTP — GGUF (llama.cpp speculative decoding)

GGUF builds of deepreinforce-ai/Ornith-1.0-9B

with the KL-distilled MTP draft head from

protoLabsAI/Ornith-1.0-9B-MTP

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.

Two things to know before you pick a file:

• Ampere and older: use Q4_K_M. It is smaller and faster than everything else here.
• Blackwell (RTX 50xx / PRO 6000): use NVFP4. MTP compounds with NVFP4's tensor-core GEMMs

where it only partially helps K-quants — so NVFP4 is the fastest rung on this hardware by

~28%, despite being a hair larger than Q4_K_M. The measured mechanism is below.

> Want the base with no MTP head? deepreinforce-ai/Ornith-1.0-9B-GGUF.

Files

| File | Size | Form | Use |

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

| Ornith-1.0-9B-MTP-NVFP4.gguf | 6.6 GB | bundled | Blackwell: fastest rung (306 tok/s +MTP) |

| Ornith-1.0-9B-MTP-Q8_0.gguf | 9.8 GB | bundled | reference quality / largest relative MTP gain |

| Ornith-1.0-9B-MTP-Q6_K.gguf | 7.6 GB | bundled | near-lossless quant |

| Ornith-1.0-9B-MTP-Q5_K_M.gguf | 6.6 GB | bundled | balanced quality |

| Ornith-1.0-9B-MTP-Q4_K_M.gguf | 5.8 GB | bundled | Ampere: fastest rung |

| Ornith-1.0-9B-MTP-IQ4_XS.gguf | 5.5 GB | bundled (imatrix) | low VRAM, near-Q4 quality |

| Ornith-1.0-9B-MTP-IQ3_M.gguf | 4.7 GB | bundled (imatrix) | lower VRAM |

| Ornith-1.0-9B-MTP-IQ2_M.gguf | 3.9 GB | bundled (imatrix) | very low VRAM (~5 GB to serve) |

| Ornith-1.0-9B-MTP-BF16.gguf | 18.4 GB | bundled (master) | re-quantize from this |

| mtp-head/mtp-Ornith-1.0-9B-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 IQ rungs are i-quants (importance-matrix

calibrated) with the MTP head pinned to Q8_0 so acceptance holds on the low-bit trunk

(measured ~0.81–0.84 on IQ2_M–IQ4_XS, on par with the k-quants). Serve them exactly like the

k-quants.

The standalone head is not a model — loading mtp-head/… directly will crash. It exists only

to pair with a base Ornith-9B GGUF via --model-draft.

Requires llama.cpp ≥ b9616 (Qwen3.5 qwen35 arch + --spec-type draft-mtp). The NVFP4

rung additionally needs a build with NVFP4 support (GGML_TYPE_NVFP4, type 40) — both landed

in llama.cpp spring 2026. LM Studio (recent) and Ollama (≥ ~0.31) inherit MTP through llama.cpp;

older Ollama fails with layer 32 missing attn_qkv → update and re-pull.

Run

Bundled (recommended) — the head travels in the file:

llama-server --model Ornith-1.0-9B-MTP-Q4_K_M.gguf \
  --n-gpu-layers 99 --ctx-size 8192 --flash-attn on --jinja \
  --spec-type draft-mtp --spec-draft-n-max 3

One-command pull:

llama-server -hf protoLabsAI/Ornith-1.0-9B-MTP-GGUF:NVFP4  --spec-type draft-mtp -ngl 99  # Blackwell
llama-server -hf protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M --spec-type draft-mtp -ngl 99  # everything else

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

llama-server --model ornith-1.0-9b-Q4_K_M.gguf \
  --model-draft mtp-head/mtp-Ornith-1.0-9B-head-Q8_0.gguf \
  --spec-type draft-mtp --spec-draft-n-max 3 \
  --n-gpu-layers 99 --ctx-size 8192 --flash-attn on --jinja

--spec-draft-n-max is the draft depth: 2 maximizes acceptance, 3 maximizes throughput,

4 starts to regress. Tune per workload.

The NVFP4 × MTP finding (why Blackwell is different)

NVFP4 weights sit on the tensor-core FP4 GEMM path; K-quants dequantize to a compute path each

step. MTP's cost is a per-step parallel verify of the drafted tokens — and that verify is

nearly free on NVFP4's GEMMs but costs ~+28% on the K-quant dequant path. So MTP's speedup is

effectively multiplicative with NVFP4 and only partial with K-quants.

Measured, acceptance-controlled (-n 200 greedy, 6 diverse prompts, ranges shown; Ampere

single-run indicative):

file size Ampere A6000 Blackwell (sm120)

no-MTP +MTP no-MTP +MTP

--------- ------ ------ ------ ------ ----------------

Q4_K_M 5.8 GB 104.6 153.4 205.1 239 (216–252)

NVFP4 6.6 GB 70.7 84.8 201.5 306 (287–330)

On Blackwell the worst NVFP4 prompt (287) beats the best Q4_K_M prompt (252); draft

acceptance is near-equal on both files at the same prompt/box, so the gap is verify cost, not

acceptance. On Ampere the FP4 path has no tensor-core backing, so Q4_K_M wins outright — use it.

Code prompts run hottest (330), creative prose lowest (287): MTP acceptance tracks predictability.

The FP8 × spec-decode compounding is documented upstream (TensorRT-LLM); the FP4-vs-K-quant

verify-cost split is, as far as we've found, new data.

Benchmarks (RTX A6000, ctx 8192, flash-attn, greedy; 6-prompt code+general mix)

n-max sweep, Q8_0

| config | decode tok/s | acceptance | speedup |

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

| base (no MTP) | 71.0 | — | 1.00× |

| MTP n-max 2 | 118.3 | 0.766 | 1.67× |

| MTP n-max 3 | 122.6 | 0.651 | 1.73× |

| MTP n-max 4 | 120.8 | 0.565 | 1.70× |

Per-token acceptance at n-max 2 (0.766) matches the vLLM reference for this head (0.762).

Across quants, n-max 3

| quant | base tok/s | +MTP tok/s | speedup | acceptance |

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

| Q4_K_M | 105.4 | 145.3 | 1.38× | 0.659 |

| Q8_0 | 71.0 | 122.6 | 1.73× | 0.651 |

Acceptance is quant-stable (~0.65 at n-max 3 even with a Q4 trunk). Q4_K_M is fastest in absolute

terms on Ampere; the relative MTP gain grows with precision (Q8's bandwidth-bound baseline has

more to gain from the parallel verify).

Full rows behind every number: protoLabsAI/lab-benchmarks

· charts at protolabs.studio/lab.

"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

(or got 426 on smaller quants — the gap is the 15 mtp.* head tensors.)

This happens if you convert the base deepreinforce-ai/Ornith-1.0-9B directly without grafting

the 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 while leaving those 15 tensors empty

→ llama.cpp expects 442 and finds 427.

Fix: graft the head into the trunk before converting, then convert with no --mtp flag. (Only 4

of the 15 head tensors are named blk.32.nextn.; the other 11 land as ordinary blk.32. tensors,

so grep nextn shows 4 but the head is complete.) Or skip grafting entirely and run the base GGUF

with --model-draft mtp-head/mtp-Ornith-1.0-9B-head-Q8_0.gguf — functionally identical.

How these were built

# 1. graft the mtp.* head into the base trunk (15 tensors, 1 nextn layer)
python graft.py --donor protoLabsAI/Ornith-1.0-9B-MTP \
                --target deepreinforce-ai/Ornith-1.0-9B --out ./ornith-9b-mtp-kl
# 2. convert (remaps mtp.* -> blk.32.nextn.* automatically)
python convert_hf_to_gguf.py ./ornith-9b-mtp-kl --outfile out/...-BF16.gguf --outtype bf16
# 3. quantize (NVFP4 rung converted from the gate-verified vLLM NVFP4 quant, same scales)
llama-quantize out/...-BF16.gguf out/...-Q4_K_M.gguf Q4_K_M

The graft.py recipe and the KL-distillation details live in the head repo

protoLabsAI/Ornith-1.0-9B-MTP. The NVFP4

rung is converted from protoLabsAI/Ornith-1.0-9B-NVFP4

— full quality/coherence receipts on that card.

Want a different size or format?

Open a Community discussion — requests usually ship within 48h. That's how most of the quants here

got made.

Provenance & license

• Base: deepreinforce-ai/Ornith-1.0-9B (MIT) — a Qwen3.5-9B hybrid (linear + full attention) fine-tune.
• MTP head: protoLabsAI/Ornith-1.0-9B-MTP (MIT) — KL-distilled against Ornith's own hidden states.
• These GGUFs derive from both; MIT. Built by protoLabs.studio.