Text Generation
GGUF
rocmfp4
strix-halo
gfx1151
mtp
speculative-decoding
qwen35moe
code
agentic
conversational
Instructions to use singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use singulared/Ornith-1.0-35B-MTP-ROCmFP4-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 singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF # Run inference directly in the terminal: llama cli -hf singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF # Run inference directly in the terminal: llama cli -hf singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF
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 singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF # Run inference directly in the terminal: ./llama-cli -hf singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF
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 singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF
Use Docker
docker model run hf.co/singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF
- LM Studio
- Jan
- vLLM
How to use singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "singulared/Ornith-1.0-35B-MTP-ROCmFP4-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": "singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF
- Ollama
How to use singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF with Ollama:
ollama run hf.co/singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF
- Unsloth Studio
How to use singulared/Ornith-1.0-35B-MTP-ROCmFP4-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 singulared/Ornith-1.0-35B-MTP-ROCmFP4-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 singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF to start chatting
- Pi
How to use singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF
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": "singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use singulared/Ornith-1.0-35B-MTP-ROCmFP4-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 singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF
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 singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF
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 "singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF" \ --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 singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF with Docker Model Runner:
docker model run hf.co/singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF
- Lemonade
How to use singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull singulared/Ornith-1.0-35B-MTP-ROCmFP4-GGUF
Run and chat with the model
lemonade run user.Ornith-1.0-35B-MTP-ROCmFP4-GGUF-{{QUANT_TAG}}List all available models
lemonade list
File size: 3,979 Bytes
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license: apache-2.0
base_model:
- deepreinforce-ai/Ornith-1.0-35B
- Qwen/Qwen3.6-35B-A3B
base_model_relation: merge
pipeline_tag: text-generation
library_name: gguf
tags:
- gguf
- rocmfp4
- strix-halo
- gfx1151
- mtp
- speculative-decoding
- qwen35moe
- code
- agentic
---
# Ornith-1.0-35B-MTP β ROCmFP4 (Strix Halo)
> β οΈ **Requires the [ROCmFPX fork](https://github.com/charlie12345/ROCmFPX) of llama.cpp β this will NOT load on mainline llama.cpp.**
> ROCmFP4 is an experimental AMD FP4 quant format (ggml tensor types 100β107) that only exists in that fork.
> Targets **AMD Strix Halo / Radeon 8060S (gfx1151)**. For standard GGUF that runs anywhere, use the base repo:
> **β [singulared/Ornith-1.0-35B-MTP-GGUF](https://huggingface.co/singulared/Ornith-1.0-35B-MTP-GGUF)** (Q8_0 / Q4_K_M, mainline llama.cpp).
[Ornith-1.0-35B](https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B) (DeepReinforce, a `qwen35moe`
agentic coder) with an embedded **MTP (`nextn`) head grafted in**, quantized to **ROCmFP4-COHERENT**
for fast self-speculative decoding on Strix Halo.
## What it is / provenance
- **Body:** `Q4_0_ROCMFP4_COHERENT` (4.70 bpw β ROCmFP4 experts + `Q6_K` token embeddings),
quantized **directly from the BF16 original** (`deepreinforce-ai/Ornith-1.0-35B`) β a single clean
quantization pass, no intermediate Q8 (avoids double-quant).
- **MTP head:** `blk.40.nextn.*` grafted **raw** from [`Qwen/Qwen3.6-35B-A3B`](https://huggingface.co/Qwen/Qwen3.6-35B-A3B)'s
native head (same `qwen35moe` arch), after quantization β so `eh_proj` stays at its original **Q8_0**.
The head's `eh_proj` precision is the throughput lever, and grafting it raw keeps it high. Ornith is a
barely-shifted fine-tune of Qwen3.6-35B-A3B, so the head transfers cleanly (~88% draft acceptance).
## Performance (Radeon 8060S / **gfx1151**, ROCmFPX fork Β· 19.2 GiB)
**Recommended: Vulkan (`-dev Vulkan0`)** β best decode, which dominates a coder's token cost:
- **decode: 86.7 t/s** (MTP `n4`, `-ub 2048`) Β· 72 t/s without MTP Β· draft acceptance **0.88**
- **prefill: ~1050 t/s** (pp4096, `-ub 2048`)
### Backend / ROCm-version comparison (`-ub 2048`)
| backend | prefill (pp4096) | decode (MTP `n4`) |
|---|---:|---:|
| **Vulkan** | 1051 | **86.7** |
| ROCm/HIP @ ROCm 7.2 | 876 | 70.5 |
| ROCm/HIP @ ROCm 7.15.0a20260721 nightly *(compiled)* | **1155** | 68.1 |
- **Vulkan wins decode decisively** (86.7 vs 70.5) β **the serving default** (decode dominates a coder's token cost).
- **Compiling the fork against ROCm 7.15 gives +32% ROCm prefill** (876β1155, beating Vulkan's) β but it's
the *slowest* decode, so it only helps prefill-bound work. The gain is **compile-time** (running a
7.2-built binary on 7.15 libs does nothing). **β ROCm 7.15 is the current [TheRock](https://github.com/ROCm/TheRock)
nightly, not a stable release** β specifically `therock-dist-linux-gfx1151-7.15.0a20260721`
(build 2026-07-21), built against the nightly tarball, not a packaged ROCm. Newer nightlies may differ.
- Numbers are **gfx1151-specific**; other Strix Halo parts may differ.
## Usage (ROCmFPX fork)
```bash
llama-server -m ornith-1.0-35b-MTP-ROCmFP4-COHERENT.gguf \
-dev Vulkan0 -fa on -ngl 99 -c 131072 --jinja \
--spec-type draft-mtp --spec-draft-n-max 4 --spec-draft-p-min 0.6 --alias ornith
```
The MTP head is **embedded** β no separate draft model. `--spec-draft-n-max 4` is the MoE sweet spot
(deeper drafts get rejected). Build the fork per its Strix Halo quickstart.
## Licensing & attribution
Derivative of two permissively-licensed models; both credited, their licenses apply to their parts:
- **Ornith-1.0-35B** β Β© DeepReinforce β **MIT** β the base weights.
- **Qwen3.6-35B-A3B** β Β© Alibaba Cloud / Qwen β **Apache-2.0** β the grafted MTP head.
Quantization: ROCmFP4-COHERENT (ROCmFPX fork). Not affiliated with or endorsed by DeepReinforce, Qwen, or the ROCmFPX author.
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