Instructions to use julianmb/Ornith-1.5-35B-A3B-ROCmFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use julianmb/Ornith-1.5-35B-A3B-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 julianmb/Ornith-1.5-35B-A3B-ROCmFP4-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf julianmb/Ornith-1.5-35B-A3B-ROCmFP4-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf julianmb/Ornith-1.5-35B-A3B-ROCmFP4-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf julianmb/Ornith-1.5-35B-A3B-ROCmFP4-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 julianmb/Ornith-1.5-35B-A3B-ROCmFP4-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf julianmb/Ornith-1.5-35B-A3B-ROCmFP4-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 julianmb/Ornith-1.5-35B-A3B-ROCmFP4-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf julianmb/Ornith-1.5-35B-A3B-ROCmFP4-GGUF:BF16

Use Docker

docker model run hf.co/julianmb/Ornith-1.5-35B-A3B-ROCmFP4-GGUF:BF16

LM Studio
Jan
Ollama
How to use julianmb/Ornith-1.5-35B-A3B-ROCmFP4-GGUF with Ollama:
```
ollama run hf.co/julianmb/Ornith-1.5-35B-A3B-ROCmFP4-GGUF:BF16
```

Unsloth Studio

How to use julianmb/Ornith-1.5-35B-A3B-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 julianmb/Ornith-1.5-35B-A3B-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 julianmb/Ornith-1.5-35B-A3B-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 julianmb/Ornith-1.5-35B-A3B-ROCmFP4-GGUF to start chatting

How to use julianmb/Ornith-1.5-35B-A3B-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 julianmb/Ornith-1.5-35B-A3B-ROCmFP4-GGUF:BF16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "julianmb/Ornith-1.5-35B-A3B-ROCmFP4-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use julianmb/Ornith-1.5-35B-A3B-ROCmFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/julianmb/Ornith-1.5-35B-A3B-ROCmFP4-GGUF:BF16
```

Lemonade

How to use julianmb/Ornith-1.5-35B-A3B-ROCmFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull julianmb/Ornith-1.5-35B-A3B-ROCmFP4-GGUF:BF16

Run and chat with the model

lemonade run user.Ornith-1.5-35B-A3B-ROCmFP4-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use julianmb/Ornith-1.5-35B-A3B-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 julianmb/Ornith-1.5-35B-A3B-ROCmFP4-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 julianmb/Ornith-1.5-35B-A3B-ROCmFP4-GGUF:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use julianmb/Ornith-1.5-35B-A3B-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 julianmb/Ornith-1.5-35B-A3B-ROCmFP4-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 "julianmb/Ornith-1.5-35B-A3B-ROCmFP4-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"

Ornith-1.5-35B-A3B ROCmFP4

ROCmFP4 quantization of Ornith 1.5 35B-A3B for AMD Strix Halo (gfx1151) and RDNA 3.5 GPUs, produced with the Q4_0_ROCMFP4_STRIX_LEAN preset (FP16 embedding/norm preservation, nextn MTP head kept at q8_0).

Quantization Details

Property	Value
Quant format	`Q4_0_ROCMFP4_STRIX_LEAN` (ROCmFP4)
Bits per weight	4.29
File size	18.16 GiB
SHA256	`b42fb74cd32ce3ab5fc0b10214ae7b4030d852f49b749dcdd0e5ad6b35e1510f`
Vision projector	`mmproj-Ornith-1.5-35B-BF16.gguf` (0.84 GiB)
Projector SHA256	`d9ce31026d1cb1f3f8d5152e2e2a014d9d2b302b6c93a7dc07bb0a0487f52837`
Max Context	262,144 tokens (256K)
Source	Ornith-1.5-35B-A3B-GGUF Q8_0 (allow-requantize)
Notes	MTP head tensors preserved at q8_0

Measured Performance (AMD Ryzen AI Max+ 395, Radeon 8060S, Mesa RADV Wave64)

Configuration	Decode
ROCmFP4 bare decode	76.9 tok/s
Q4_K_M baseline	71.5–71.7 tok/s
ROCmFP4 MTP (K4 p0.0)	35–50 tok/s (net loss — keep MTP disabled)

ROCmFP4 is ~7.5% faster and 16.7% smaller than stock Q4_K_M.

Serving

HaloFPX (Text + Vision)

halofpx pull downloads and verifies both the ROCmFP4 weights and BF16 vision projector:

halofpx pull ornith-1.5-35b
halofpx serve
halofpx load ornith-1.5-35b

halofpx list reports model-weight and vision-projector readiness separately.

Direct llama-server Vision

llama-server \
  -m Ornith-1.5-35B-A3B-ROCmFP4.gguf \
  -mm mmproj-Ornith-1.5-35B-BF16.gguf \
  -ngl 99 -c 131072 -fa on --no-mmap

Full 256K Max Context (Strix Halo 128GB UMA):

llama-server \
  -m Ornith-1.5-35B-A3B-ROCmFP4.gguf \
  -ngl 99 -c 262144 -fa on --no-mmap

Multi-Slot / Conservative Memory (131K Context):

llama-server \
  -m Ornith-1.5-35B-A3B-ROCmFP4.gguf \
  -ngl 99 -c 131072 -fa on --no-mmap

Note: speculative decoding (MTP, --spec-type draft-mtp) is not recommended on this model (hybrid linear-attention MoE: ~16% draft acceptance, net throughput loss). Use bare decode (default --spec-type none).

Downloads last month: 1,155

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Model tree for julianmb/Ornith-1.5-35B-A3B-ROCmFP4-GGUF

Base model

ornith-ai/Ornith-1.5-35B-A3B-GGUF

Quantized

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