Instructions to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="protoLabsAI/Ornith-1.0-9B-MTP-GGUF",
	filename="Ornith-1.0-9B-MTP-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M

Use Docker

docker model run hf.co/protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "protoLabsAI/Ornith-1.0-9B-MTP-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "protoLabsAI/Ornith-1.0-9B-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M

Ollama
How to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with Ollama:
```
ollama run hf.co/protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M
```

Unsloth Studio

How to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for protoLabsAI/Ornith-1.0-9B-MTP-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for protoLabsAI/Ornith-1.0-9B-MTP-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for protoLabsAI/Ornith-1.0-9B-MTP-GGUF to start chatting

How to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M
```

Lemonade

How to use protoLabsAI/Ornith-1.0-9B-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull protoLabsAI/Ornith-1.0-9B-MTP-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ornith-1.0-9B-MTP-GGUF-Q4_K_M

List all available models

lemonade list

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.

Downloads last month: 41,452

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for protoLabsAI/Ornith-1.0-9B-MTP-GGUF

Base model

deepreinforce-ai/Ornith-1.0-9B

Quantized

(67)

this model

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

Files

Run

The NVFP4 × MTP finding (why Blackwell is different)

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

n-max sweep, Q8_0

Across quants, n-max 3

"Lossless" — read this

Troubleshooting: wrong number of tensors expected 442 got 427

How these were built

Want a different size or format?

Provenance & license

Model tree for protoLabsAI/Ornith-1.0-9B-MTP-GGUF

Troubleshooting: `wrong number of tensors expected 442 got 427`