Instructions to use utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF",
	filename="Ornith-1.0-35B-NVFP4-noMTP-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-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 utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF:BF16

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 utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF:BF16

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 utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF:BF16

Use Docker

docker model run hf.co/utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF:BF16

LM Studio
Jan

vLLM

How to use utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-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": "utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF:BF16

Ollama
How to use utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF with Ollama:
```
ollama run hf.co/utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF:BF16
```

Unsloth Studio

How to use utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-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 utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-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 utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF to start chatting

How to use utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF:BF16

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": "utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-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 utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF:BF16

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 utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF:BF16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF:BF16

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 "utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF:BF16" \
  --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 utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF with Docker Model Runner:
```
docker model run hf.co/utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF:BF16
```

Lemonade

How to use utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull utautako/Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF:BF16

Run and chat with the model

lemonade run user.Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF-BF16

List all available models

lemonade list

Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF / README.md

utautako

Trim benchmark card back to original concision (numbers only)

b368a51 verified 5 days ago

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metadata

license: mit
pipeline_tag: image-text-to-text
library_name: llama.cpp
base_model:
  - sakamakismile/Ornith-1.0-35B-NVFP4
  - deepreinforce-ai/Ornith-1.0-35B
base_model_relation: quantized
language:
  - en
tags:
  - gguf
  - llama-cpp
  - qwen
  - qwen3_5_moe
  - qwen35moe
  - nvfp4
  - compressed-tensors
  - vision
  - vlm
  - multimodal
  - mixture-of-experts
  - agentic-coding
  - quantized
  - rtx-5090
  - blackwell

Ornith-1.0-35B-NVFP4-noMTP-BF16-GGUF

GGUF conversion of sakamakismile/Ornith-1.0-35B-NVFP4, preserving the compressed-tensors NVFP4 weights, with a BF16 vision projector (mmproj) and BF16 (lossless) embedding & output layers. Text-only checkpoint (no MTP head). Benchmarked on an RTX 5090 (Blackwell) with llama-benchy.

Highlights

NVFP4 preserved — 430 NVFP4 tensors (MoE experts, attention, linear-attention/SSM) kept in their native 4-bit format, not re-quantized.
Lossless embeddings & output — token_embd and lm_head are kept at BF16 (no Q4_K/Q6_K), so vocabulary and output-projection precision is unchanged from the source.
Vision / mmproj — a separate BF16 vision projector enables image-text-to-text.
No MTP — the source checkpoint ships no multi-token-prediction head, so this is a clean autoregressive (AR) model (block_count = 40).
Blackwell native FP4 — prefill/decoding use native FP4 tensor-core kernels on RTX 50-series (sm_120).

Files

File	Size	Description
`Ornith-1.0-35B-NVFP4-noMTP-BF16.gguf`	19.60 GiB	Main GGUF — NVFP4 body preserved, BF16 `token_embd`/`lm_head`
`mmproj-Ornith-1.0-35B-BF16.gguf`	861 MiB	Vision projector (mmproj) for image input

Usage (llama.cpp)

# Text-only
llama-server -m Ornith-1.0-35B-NVFP4-noMTP-BF16.gguf --host 0.0.0.0 --port 8080 --jinja

# With vision (image-text-to-text)
llama-server -m Ornith-1.0-35B-NVFP4-noMTP-BF16.gguf \
  --mmproj mmproj-Ornith-1.0-35B-BF16.gguf \
  --host 0.0.0.0 --port 8080 --jinja

For a Blackwell (RTX 50xx) build with native FP4 acceleration, compile llama.cpp with CUDA 12.8+ and -DCMAKE_CUDA_ARCHITECTURES=120. -ngl 999 offloads all layers; -c 262144 uses the full context.

This is a thinking model. Reasoning is emitted inside <think>…</think>. To disable thinking for a turn, pass "chat_template_kwargs": {"enable_thinking": false} in the request body.

Vision

The model is multimodal (image-text-to-text). Image input requires the separate mmproj file via --mmproj. Verified on llama.cpp with a synthetic image (the model correctly described the shape and color). Loading the mmproj has negligible impact on text-generation throughput (see benchmarks).

Benchmarks

RTX 5090 (32 GB), llama.cpp b9812 (0e53b82a9, MSVC 19.50, sm_120a, BLACKWELL_NATIVE_FP4=1), llama-benchy (pp = 512/4096, tg = 512, runs = 3, --latency-mode generation), tokenizer deepreinforce-ai/Ornith-1.0-35B, ctx = 262144, KV cache q8_0. Autoregressive (no spec decoding).

Mode	pp	Prefill (pp) tok/s	Generation (tg) tok/s	Peak tg
Text	512	6539.84 ± 84.57	193.58 ± 3.20	194.00
Text	4096	7754.81 ± 288.02	193.75 ± 1.92	194.33
Vision (+mmproj)	512	6076.30 ± 335.59	188.55 ± 1.97	189.00
Vision (+mmproj)	4096	7674.93 ± 86.51	190.58 ± 0.24	191.00

Coherence test PASSED in all runs; generation latency ≈ 112–125 ms.

Runtime configuration

Currently loaded with llama-server on RTX 5090:

Parameter	Value
Context (`c`)	196 608
Max tokens	32 768
Speculative decoding	none
Vision projector	`mmproj-Ornith-1.0-35B-BF16.gguf`
KV cache type	q8_0
VRAM usage	22.68 GiB

Quantization / conversion notes

Converted with llama.cpp's convert_hf_to_gguf.py directly from the sakamakismile NVFP4 checkpoint (compressed-tensors, format: nvfp4-pack-quantized), which is auto-detected and re-packed into GGML_TYPE_NVFP4 — no de-quantization to full precision.

--outtype bf16 (not Q4_K). With bf16, the NVFP4 body is preserved and the large 2-D weights (token_embd, lm_head) stay at BF16; only small 1-D tensors (norms, biases, router gate, scales) are F32. There is no wasteful F32 blow-up of the big weights, so a follow-up llama-quantize pass is not required to reach a reasonable size (19.60 GiB).
--no-mtp. The checkpoint's config.json declares mtp_num_hidden_layers: 1 but ships no MTP tensors; --no-mtp keeps block_count = 40 and avoids requesting an absent MTP head.
Tensor make-up: NVFP4 × 430, F32 × 1161 (scales / norms / router gate), BF16 × 2 (token_embd, lm_head).

Speed/quality trade-off of BF16 output: keeping lm_head at BF16 (~1.0 GiB) costs roughly 14 % of generation throughput versus a fully quantized build (this model ≈ 193 tok/s vs Q4_K_M ≈ 223 tok/s on the same hardware), because the output projection over the ~248k-token vocabulary is read every decoding step. This build prioritizes lossless embeddings/output; for speed, Q6_K quantization of the output layer via llama-quantize is required.

Model details

Original (quantized) model: sakamakismile/Ornith-1.0-35B-NVFP4
Base model: deepreinforce-ai/Ornith-1.0-35B
Architecture: qwen35moe (Qwen3.5-MoE family; hybrid full + gated-linear attention), block_count = 40, no MTP
MoE: 256 experts, 8 active; embedding dim 2048; context length 262144
Quantization: NVFP4 (430 tensors) preserved; token_embd/lm_head BF16; scales/norms F32
Conversion: llama.cpp convert_hf_to_gguf.py --outtype bf16 --no-mtp (main) and --mmproj --outtype bf16 (vision)
File sizes: main 19.60 GiB, mmproj 861 MiB

License and attribution

Released under the MIT license, following the upstream models. This is an unofficial GGUF conversion of sakamakismile/Ornith-1.0-35B-NVFP4 (base model deepreinforce-ai/Ornith-1.0-35B; NVFP4 quantization by sakamakismile). It is not affiliated with or endorsed by the original authors. Please consult the original model cards for intended use and limitations.