Instructions to use pugant/Ornith-1.5-35B-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-1.5-35B-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-1.5-35B-ROCmFP4-STRIX_LEAN:BF16
# Run inference directly in the terminal:
llama cli -hf pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16
# Run inference directly in the terminal:
llama cli -hf pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN: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 pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16
# Run inference directly in the terminal:
./llama-cli -hf pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN: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 pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16

Use Docker

docker model run hf.co/pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16

LM Studio
Jan

vLLM

How to use pugant/Ornith-1.5-35B-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-1.5-35B-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-1.5-35B-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-1.5-35B-ROCmFP4-STRIX_LEAN:BF16

Ollama
How to use pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN with Ollama:
```
ollama run hf.co/pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16
```

Unsloth Studio

How to use pugant/Ornith-1.5-35B-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-1.5-35B-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-1.5-35B-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-1.5-35B-ROCmFP4-STRIX_LEAN to start chatting

How to use pugant/Ornith-1.5-35B-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-1.5-35B-ROCmFP4-STRIX_LEAN: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": "pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN with Docker Model Runner:
```
docker model run hf.co/pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16
```

Lemonade

How to use pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16

Run and chat with the model

lemonade run user.Ornith-1.5-35B-ROCmFP4-STRIX_LEAN-BF16

List all available models

lemonade list

Hermes Agent

How to use pugant/Ornith-1.5-35B-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-1.5-35B-ROCmFP4-STRIX_LEAN: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 pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use pugant/Ornith-1.5-35B-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-1.5-35B-ROCmFP4-STRIX_LEAN: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 "pugant/Ornith-1.5-35B-ROCmFP4-STRIX_LEAN: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"

Ornith-1.5-35B-ROCmFP4-STRIX_LEAN / README.md

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	---
	library_name: llama.cpp
	license: mit
	base_model: ornith-ai/Ornith-1.5-35B
	tags:
	- rocmfpx
	- gfx1151
	- strix-halo
	- qwen35moe
	- moe
	- rocm
	- amdgpu
	- ROCmFP4
	pipeline_tag: image-text-to-text
	language:
	- en
	- multilingual
	version: '1.5'
	date: 2026-08-20
	quantized_by: pugant
	---

	# Ornith-1.5-35B — ROCmFP4-STRIX_LEAN (Strix Halo / gfx1151)

	> Version 1.5 — 2026-08-20

	## TL;DR

	`Ornith-1.5-35B` (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 ~17.7 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](#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`, Vulkan (RADV) build of the fork, GPU-exclusive window (production service stopped). Note: the [Ornith-1.0 release](https://huggingface.co/pugant/Ornith-1.0-35B-ROCmFP4-STRIX_LEAN) numbers below were measured on the ROCm (HIP) backend — different backend, not directly comparable.

	\| Model \| Quant \| Size \| tg128 (tok/s) \| pp512 (tok/s) \|
	\|---\|---\|---:\|---:\|---:\|
	\| Ornith-1.5-35B (this) \| ROCmFP4-STRIX_LEAN \| 17.73 GiB \| 82.45 ± 1.37 \| 1161.00 ± 22.76 \|
	\| Ornith-1.0-35B (ROCm) \| ROCmFP4-STRIX_LEAN \| 17.32 GiB \| 66.68 \| 1486 \|
	\| grug-35b-v2 (ROCm) \| ROCmFP4-STRIX_LEAN \| 17.31 GiB \| 70.92 \| 1418 \|
	\| Qwen3.6-35B-A3B base (Vulkan ref) \| ROCmFP4-STRIX_LEAN \| 17.73 GiB \| 81.57 \| 1164.67 \|

	Sanity check: Ornith-1.5 tracks its base-architecture sibling Qwen3.6-35B-A3B (same quant, same backend) within 1% — 82.45 vs 81.57 tg128. On this Vulkan build Ornith-1.5 decodes +23.7% faster than Ornith-1.0 on ROCm at the same footprint class.

	### Speculative decoding (MTP)

	The source model ships a 1-layer MTP head (`nextn_predict_layers=1`, tensors `blk.40.nextn.`), included in this GGUF and activated at runtime only if you opt in* with the fork's `--spec-type draft-mtp` flags.

	Measured on the same GPU-exclusive window, same server/prompt methodology (2 prompts × 2 runs, ctx 16k, Vulkan build of the fork):

	\| Config \| prose (tok/s) \| deterministic (tok/s) \|
	\|---\|---:\|---:\|
	\| plain (no spec) \| 78.0 \| 77.2 \|
	\| MTP n-max 2 \| 59.1 \| 75.4 \|
	\| MTP n-max 3 \| 44.6 \| 63.5 \|
	\| MTP n-max 5 \| 36.9 \| 47.3 \|

	Verdict: speculative decoding does not pay off on Ornith-1.5 — plain inference wins at every n-max (−24% prose at the best MTP setting). The measured draft acceptance explains why: position-1 acceptance is high (0.99 on deterministic tasks) but position-2 collapses to ~0.07, so the mean accepted length (1.4–1.7) never covers the draft+verify cost — the nextn layer is a full MoE layer. For comparison, on the same stack the Qwen3.6-35B-A3B base model accepts (0.87, 0.77, 0.64) and gains +37% with MTP n-max 3: the 1.5 fine-tune degraded the MTP head beyond the first drafted token. If you still want to experiment, use `--spec-draft-n-max 2`; above that it is pure overhead. MTP stays opt-in: with no spec flags the model runs plain inference at the headline speeds above.

	### 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):

	- Token embeddings (`token_embd.weight`) → `Q5_K` (preserve vocab fidelity)
	- Attention K/V (`blk..attn_qkv.weight`, `blk..attn_v.weight`) → `q4_0_rocmfp4` (high-precision path for attention state)
	- Expert FFN (`blk..ffn__exps.weight`) → `q4_0_rocmfp4_fast` (max speed path; the bulk of MoE weights)
	- MTP head (`blk.40.nextn.*`) kept in the file (BF16/F32 as in source)
	- Zero quantization fallbacks across all 753 tensors

	Reference fork: [`charlie12345/ROCmFPX`](https://github.com/charlie12345/ROCmFPX) (MIT).

	## imatrix methodology

	Precomputed by bartowski on 573 chunks (`calibration-v6` dataset), redistributed here as `imatrix-Ornith-1.5-35B-bartowski.gguf` with explicit attribution. The original is at [`bartowski/Ornith-1.5-35B-A3B-GGUF`](https://huggingface.co/bartowski/Ornith-1.5-35B-A3B-GGUF) (MIT). Different from the Ornith-1.0 release, which used the unsloth imatrix computed on 1.0 weights.

	## Files

	\| File \| Size \| Description \|
	\|---\|---:\|---\|
	\| `Ornith-1.5-35B-ROCmFP4-STRIX_LEAN.gguf` \| ~17.73 GiB \| Main model (type 106), MTP head included \|
	\| `mmproj-Ornith-1.5-35B-BF16.gguf` \| ~860 MB \| Vision projector (BF16, from the official repo) \|
	\| `imatrix-Ornith-1.5-35B-bartowski.gguf` \| ~183 MB \| Importance matrix (precomputed by bartowski; for re-quantization) \|

	## Usage

	```bash
	# 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-1.5-35B-ROCmFP4-STRIX_LEAN.gguf \
	--mmproj /models/mmproj-Ornith-1.5-35B-BF16.gguf \
	-ngl 999 -fa on --jinja -c 32768 --host 0.0.0.0 --port 1234
	```

	Notes:
	- MTP is opt-in at runtime. Add `--spec-type draft-mtp --spec-draft-ngl all --spec-draft-p-min 0.0 --spec-draft-p-split 0.10 --spec-draft-n-max <N>` to activate speculative decoding (see the MTP section above for measured n-max guidance). Plain inference (no spec flags) is the default and what the headline benchmark table reports.
	- The `--mmproj` flag is required for the vision tower (multimodal). Without it, text-only still works.

	## How to replicate

	1. Build the `docker-llm-service-convert` image from `kyuz0/amd-strix-halo-toolboxes` + `charlie12345/ROCmFPX` (must contain `MODEL_ARCH.QWEN35MOE`).
	2. Download the BF16 GGUF from [`ornith-ai/Ornith-1.5-35B-A3B-GGUF`](https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF).
	3. Quantize with the bartowski imatrix: `llama-quantize --imatrix imatrix-Ornith-1.5-35B-bartowski.gguf <bf16>.gguf <out>.gguf Q4_0_ROCMFP4_STRIX_LEAN 16`.

	## Attribution & model tree

	```
	Qwen3.5-VL-MoE (base architecture)
	└── ornith-ai/Ornith-1.5-35B (A3B) (MIT)
	└── this GGUF (ROCmFP4-STRIX_LEAN)
	```

	- Base model: [`ornith-ai/Ornith-1.5-35B`](https://huggingface.co/ornith-ai/Ornith-1.5-35B) (MIT)
	- BF16 GGUF + mmproj source: [`ornith-ai/Ornith-1.5-35B-A3B-GGUF`](https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF) (MIT)
	- imatrix: [`bartowski/Ornith-1.5-35B-A3B-GGUF`](https://huggingface.co/bartowski/Ornith-1.5-35B-A3B-GGUF) (MIT)
	- Quantization fork: [`charlie12345/ROCmFPX`](https://github.com/charlie12345/ROCmFPX) (MIT)
	- Container runtime: [`kyuz0/amd-strix-halo-toolboxes`](https://github.com/kyuz0/amd-strix-halo-toolboxes)

	## License

	MIT (inherited from `ornith-ai/Ornith-1.5-35B` and its GGUF release). Derivative work: original model and its license are preserved. See [`LICENSE`](./LICENSE) and [`NOTICE`](./NOTICE).

	## Acknowledgements

	Built on the shoulders of giants:

	- [kyuz0/amd-strix-halo-toolboxes](https://github.com/kyuz0/amd-strix-halo-toolboxes) — Strix Halo container runtime
	- [charlie12345/ROCmFPX](https://github.com/charlie12345/ROCmFPX) — llama.cpp fork with ROCmFP4 presets (type 106)
	- [bartowski](https://huggingface.co/bartowski) — precomputed imatrix for Ornith-1.5-35B
	- [ornith-ai / DeepReinforce Team](https://huggingface.co/ornith-ai) — Ornith-1.5-35B
	- [llama.cpp](https://github.com/ggerganov/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 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 (feel free to share yours).
	- MTP head present in weights; speculative decoding is opt-in at runtime (plain inference by default). No separate DFlash/drafter head exists for Ornith-1.5 as of release date.

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

	## Citation

	```bibtex
	@misc{ornith152026,
	title = {Ornith-1.5-35B},
	author = {DeepReinforce Team},
	year = {2026},
	url = {https://huggingface.co/ornith-ai/Ornith-1.5-35B}
	}
	```

	## Disclaimer

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

	---

	## Software

	- Runtime: [`charlie12345/ROCmFPX`](https://github.com/charlie12345/ROCmFPX) (fork of llama.cpp) —
	our experimental build with per-request drafter routing, reasoning budget and
	spec-boundary cache salvage: **full source included in the lab repo under
	[`rocmfpx/`](https://github.com/pugant/strix-halo-llamacpp-lab/tree/main/rocmfpx)**
	- Lab repo (patches, benchmarks, full replication guide):
	[`pugant/strix-halo-llamacpp-lab`](https://github.com/pugant/strix-halo-llamacpp-lab)

	> Everything here is experimental and provided as-is, at your own risk.