Instructions to use pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN 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 pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN:F16
# Run inference directly in the terminal:
llama cli -hf pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN:F16
# Run inference directly in the terminal:
llama cli -hf pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN:F16

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 pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN:F16
# Run inference directly in the terminal:
./llama-cli -hf pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN:F16

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 pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN:F16

Use Docker

docker model run hf.co/pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN:F16

LM Studio
Jan

vLLM

How to use pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN",
		"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/pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN:F16

Ollama
How to use pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN with Ollama:
```
ollama run hf.co/pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN:F16
```

Unsloth Studio

How to use pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN 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 pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN 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 pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN to start chatting

How to use pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN:F16

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": "pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN with Docker Model Runner:
```
docker model run hf.co/pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN:F16
```

Lemonade

How to use pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN:F16

Run and chat with the model

lemonade run user.Ornith-1.0-35B-ROCmFP4-STRIX_LEAN-F16

List all available models

lemonade list

Hermes Agent

How to use pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN:F16

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 pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN:F16

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 "pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN:F16" \
  --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"

Ornith-1.0-35B-ROCmFP4-STRIX_LEAN / README.md

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metadata

library_name: llama.cpp
license: mit
base_model: ornith-ai/Ornith-1.0-35B
tags:
  - rocmfpx
  - gfx1151
  - strix-halo
  - qwen35moe
  - moe
  - rocm
  - amdgpu
  - ROCmFP4
pipeline_tag: image-text-to-text
language:
  - en
  - multilingual
version: '1.0'
date: 2026-08-11T00:00:00.000Z
quantized_by: pugant

Ornith-1.0-35B — ROCmFP4-STRIX_LEAN (Strix Halo / gfx1151)

Version 1.0 — 2026-08-11

TL;DR

Ornith-1.0-35B (35B params, 3B active per token, Qwen3.5-VL-MoE family) quantized to Q4_0_ROCMFP4_STRIX_LEAN (type 106 preset, ~4.29 BPW). Tuned for AMD Strix Halo (gfx1151 / RDNA 3.5) with the charlie12345/ROCmFPX fork of llama.cpp. Runs the full vision + text multimodal model in ~17.3 GiB.

⚠️ Critical warnings — read before downloading

Requires charlie12345/ROCmFPX fork of llama.cpp (built via the kyuz0/amd-strix-halo-toolboxes container). The type 106 (Q4_0_ROCMFP4_STRIX_LEAN) tensor format is INVALID in stock llama.cpp — it will refuse to load. See Usage below.
Profiled for gfx1151 only (Strix Halo / Ryzen AI Max+ 395, RDNA 3.5). Not tested on other GPUs.
FP4 here is software on RDNA 3.5 (no FP4 silicon units): the win is bandwidth / memory, not raw compute throughput. The Strix Halo ceiling on this MoE is bandwidth-bound, which is exactly where FP4 helps.

Benchmarks

Tested on Strix Halo (AMD Ryzen AI Max+ 395, 128 GB LPDDR5X). Methodology: llama-bench -ngl 999 -fa on -p 512 -n 128 -mmap 0. All benchmarks for this model were run in ROCm containers (HIP backend). Since then we also benchmarked ROCmFPX quants on the Vulkan (RADV) build of the fork — see the comparison table in Qwen3.6-35B-A3B-MTP-Q6_0_ROCMFPX; those numbers are on a different backend and not directly comparable.

Model	Quant	Size	tg128 (tok/s)	pp512 (tok/s)
Ornith-1.0-35B	ROCmFP4-STRIX_LEAN	17.32 GiB	66.68	1486
grug-35b-v2 (sibling)	ROCmFP4-STRIX_LEAN	17.31 GiB	70.92	1418
Qwen3.6-35B-A3B (production ref)	ROCmFP4-STRIX_LEAN	17.31 GiB	63	—

vs production reference: +5.9% tok/s vs Qwen3.6-35B-A3B (66.68 vs 63), at the same 17.3 GiB footprint. See the sibling grug quant for a +12% variant (same arch family, grug fine-tune).

System configuration at bench time

Declared for reproducibility:

Bare metal host: Bosgame BeyondMax Series (bosgame-m5), Ubuntu 24.04.4 LTS, kernel 7.0.0-28-generic
CPU power profile: balanced (powerprofilesctl get) — default, NOT forced to performance. Representative of an out-of-the-box setup.
CPU scaling driver: amd-pstate-epp, scaling_governor performance (amd-pstate-epp default), EPP performance
IOMMU / iGPU power: auto (no manual tuning)

Note: tok/s above were measured on a non-tuned system (power profile balanced). Users who set powerprofilesctl set performance may see slightly higher numbers.

Quantization details

Preset Q4_0_ROCMFP4_STRIX_LEAN (GGUF file_type 106, ~4.29 bits/weight):

Attention K/V (blk.*.attn_qkv.weight, blk.*.attn_v.weight) → q4_0_rocmfp4 (high-precision path for attention state)
Token embeddings (token_embd.weight) → Q5_K (preserve vocab fidelity)
Expert FFN (blk.*.ffn_*_exps.weight) → q4_0_rocmfp4_fast (max speed path; the bulk of MoE weights)
Other tensors → F32 / Q4_0_ROCMFP4_FAST as appropriate

Reference fork: charlie12345/ROCmFPX commit 00d5452.

imatrix methodology

Precomputed by unsloth (46 chunks), redistributed here as imatrix.dat with explicit attribution. The original is at unsloth/Ornith-1.0-35B-GGUF (MIT).

Files

File	Size	Description
`Ornith-1.0-35B-ROCmFP4-STRIX_LEAN.gguf`	~17.32 GiB	Main model (type 106)
`mmproj-F16.gguf`	~857 MB	Vision projector (F16)
`imatrix.dat`	~183 MB	Importance matrix (precomputed by unsloth; for re-quantization)

Usage

# Requires the kyuz0 Strix Halo toolbox (which builds charlie12345/ROCmFPX)
docker run --rm -p 1234:1234 --device /dev/kfd --device /dev/dri \
  -v /path/to/models:/models rocmfpx-llm-service \
  llama-server \
    -m /models/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN.gguf \
    --mmproj /models/mmproj-F16.gguf \
    -ngl 999 -fa on --jinja -c 32768 --host 0.0.0.0 --port 1234

Notes:

MTP not enabled at runtime. The source model includes mtp_num_hidden_layers=1 (MTP weights are present as blk.40.*), but this quant is aligned with the plain-inference MoE pipeline (no --spec-type draft-mtp). MTP weights remain in the file (~1–2 GiB extra) should a future runtime activate them.
The --mmproj flag is required for the vision tower (multimodal). Without it, text-only still works.

How to replicate

Textual pipeline only (no published scripts):

Build the docker-llm-service-convert image from kyuz0/amd-strix-halo-toolboxes + charlie12345/ROCmFPX (commit 00d5452 or later main HEAD — must contain MODEL_ARCH.QWEN35MOE).
Download the BF16 GGUF (2 shards) from unsloth/Ornith-1.0-35B-GGUF.
Quantize with the included imatrix.dat: llama-quantize --imatrix imatrix.dat <bf16>.gguf <out>.gguf Q4_0_ROCMFP4_STRIX_LEAN 16.

Attribution & model tree

Qwen3.5-VL-MoE (base architecture)
    └── ornith-ai/Ornith-1.0-35B (MIT)
            └── this GGUF (ROCmFP4-STRIX_LEAN)

Base model: ornith-ai/Ornith-1.0-35B (MIT) — alias of deepreinforce-ai/Ornith-1.0-35B
BF16 source + imatrix: unsloth/Ornith-1.0-35B-GGUF (MIT)
Quantization fork: charlie12345/ROCmFPX (MIT)
Container runtime: kyuz0/amd-strix-halo-toolboxes

License

MIT (inherited from ornith-ai/Ornith-1.0-35B and unsloth/Ornith-1.0-35B-GGUF). Derivative work: original model and its license are preserved. See LICENSE and NOTICE.

Acknowledgements

Built on the shoulders of giants:

kyuz0/amd-strix-halo-toolboxes — Strix Halo container runtime
charlie12345/ROCmFPX — llama.cpp fork with ROCmFP4 presets (type 106)
unsloth — BF16 GGUF + precomputed imatrix for Ornith-1.0-35B
ornith-ai / DeepReinforce Team — Ornith-1.0-35B
llama.cpp community + Kawrakow (imatrix methodology)
Hardware: Bosgame BeyondMax Series (Strix Halo bare metal host)

Limitations & community feedback

Speed benchmark only. No perplexity / MMLU / quality eval is included in this release. The MoE structure is preserved bit-for-bit from the BF16 source except for the quantized tensor formats above; quality is expected to track standard Q4_K_M-class with the ROCmFP4 attention/K-V choices, but this is not measured here.
Profiled for gfx1151 only. Not tested on other GPUs (no Navi 3 / Navi 4 / data-center MI series numbers — feel free to share yours).
MTP present in weights but not activated at runtime (plain inference).

We invite the community — especially fellow Strix Halo owners — to test and share quality results. Open a Discussion on this repo.

Citation

@misc{ornith102026,
  title  = {Ornith-1.0-35B},
  author = {DeepReinforce Team},
  year   = {2026},
  url    = {https://deep-reinforce.com/ornith_1_0.html}
}

Disclaimer

No affiliation with AMD, Qwen, DeepReinforce, unsloth, kyuz0, or charlie12345. Provided as-is, without warranty. Users must comply with the base model license (MIT).