Instructions to use YTan2000/Ornith-1.0-35B-TQ3_4S with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use YTan2000/Ornith-1.0-35B-TQ3_4S 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 YTan2000/Ornith-1.0-35B-TQ3_4S:F16
# Run inference directly in the terminal:
llama cli -hf YTan2000/Ornith-1.0-35B-TQ3_4S:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf YTan2000/Ornith-1.0-35B-TQ3_4S:F16
# Run inference directly in the terminal:
llama cli -hf YTan2000/Ornith-1.0-35B-TQ3_4S: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 YTan2000/Ornith-1.0-35B-TQ3_4S:F16
# Run inference directly in the terminal:
./llama-cli -hf YTan2000/Ornith-1.0-35B-TQ3_4S: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 YTan2000/Ornith-1.0-35B-TQ3_4S:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf YTan2000/Ornith-1.0-35B-TQ3_4S:F16

Use Docker

docker model run hf.co/YTan2000/Ornith-1.0-35B-TQ3_4S:F16

LM Studio
Jan

vLLM

How to use YTan2000/Ornith-1.0-35B-TQ3_4S with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YTan2000/Ornith-1.0-35B-TQ3_4S"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YTan2000/Ornith-1.0-35B-TQ3_4S",
		"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/YTan2000/Ornith-1.0-35B-TQ3_4S:F16

Ollama
How to use YTan2000/Ornith-1.0-35B-TQ3_4S with Ollama:
```
ollama run hf.co/YTan2000/Ornith-1.0-35B-TQ3_4S:F16
```
Unsloth Desktop

How to use YTan2000/Ornith-1.0-35B-TQ3_4S with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YTan2000/Ornith-1.0-35B-TQ3_4S:F16

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": "YTan2000/Ornith-1.0-35B-TQ3_4S:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use YTan2000/Ornith-1.0-35B-TQ3_4S with Docker Model Runner:
```
docker model run hf.co/YTan2000/Ornith-1.0-35B-TQ3_4S:F16
```

Lemonade

How to use YTan2000/Ornith-1.0-35B-TQ3_4S with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull YTan2000/Ornith-1.0-35B-TQ3_4S:F16

Run and chat with the model

lemonade run user.Ornith-1.0-35B-TQ3_4S-F16

List all available models

lemonade list

Hermes Agent

How to use YTan2000/Ornith-1.0-35B-TQ3_4S with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YTan2000/Ornith-1.0-35B-TQ3_4S: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 YTan2000/Ornith-1.0-35B-TQ3_4S:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use YTan2000/Ornith-1.0-35B-TQ3_4S with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YTan2000/Ornith-1.0-35B-TQ3_4S: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 "YTan2000/Ornith-1.0-35B-TQ3_4S: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-TQ3_4S / README.md

YTan2000

Upload README.md with huggingface_hub

531b14a verified 3 months ago

3.54 kB

license: apache-2.0
language:
  - en
library_name: gguf
pipeline_tag: text-generation
model_name: Ornith-1.0-35B-TQ3_4S
tags:
  - gguf
  - llama.cpp
  - qwen
  - qwen3.6
  - ornith
  - turboquant
  - tq3_4s
base_model:
  - deepreinforce-ai/Ornith-1.0-35B
model-index:
  - name: Ornith-1.0-35B-TQ3_4S
    results: []

Ornith-1.0-35B-TQ3_4S

Ornith-1.0-35B-TQ3_4S is a compact TurboQuant GGUF build of deepreinforce-ai/Ornith-1.0-35B.

Required Runtime

This model uses the custom TQ3_4S tensor type and requires turbo-tan/llama.cpp-tq3.

Stock llama.cpp builds without TurboQuant support cannot load this model.

This is the standard 35B model, not an MTP release. Do not add draft-MTP speculative-decoding flags.

Files

Ornith-1.0-35B-TQ3_4S.gguf - main model, 13.30 GB (12.39 GiB)
thumbnail.png - model card banner
benchmark.png - benchmark comparison card
benchmark_notes.md - compact benchmark provenance and comparison

Build the Required Runtime

git clone https://github.com/turbo-tan/llama.cpp-tq3
cd llama.cpp-tq3

cmake -S . -B build \
  -DCMAKE_BUILD_TYPE=Release \
  -DGGML_CUDA=ON \
  -DGGML_CUDA_FA_ALL_QUANTS=OFF \
  -DGGML_CUDA_GRAPHS=ON

cmake --build build --target llama-server -j

For an RTX 3090, -DCMAKE_CUDA_ARCHITECTURES=86 may be added explicitly. Use the architecture matching your GPU on other systems.

Recommended Runtime

Validated on an NVIDIA GeForce RTX 3090 Founders Edition with 24 GiB VRAM:

./build/bin/llama-server \
  -m Ornith-1.0-35B-TQ3_4S.gguf \
  --alias Ornith-1.0-35B-TQ3_4S \
  --host 127.0.0.1 --port 8080 \
  -c 32768 -np 1 -ngl 99 -fa on \
  -ctk q8_0 -ctv tq3_0 \
  --reasoning off --jinja

Runtime notes:

-fa on enables flash attention at runtime.
The validated CUDA build uses GGML_CUDA_FA_ALL_QUANTS=OFF.
-ngl 99 fully offloads the model on supported GPUs. Avoid partial offload when comparing the published speed.
Reduce context from 32768 if the available VRAM is lower than 24 GiB.

Quick Smoke Test

curl -s http://127.0.0.1:8080/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"Ornith-1.0-35B-TQ3_4S","messages":[{"role":"user","content":"Write ONLY the word ok."}],"max_tokens":16,"temperature":0.0}'

Expected assistant content:

ok

Benchmark Summary

Local BenchLoop and Hard86 comparison on an RTX 3090 FE using the TurboTan runtime and the launch settings above:

Metric	Result
35B field overall	95.75
Hard86	81.4%
EasyCode	100.0%
Toolcall	88.3%
Data extract	86.5%
Instruct follow	65.5%
Reason math	73.3%
Generation speed	146.3 tok/s
Size	13.0 GB reported; 12.39 GiB file

The field score uses 0.85 * task_score + 0.15 * size_factor, with size normalized to the smallest displayed 35B model. Hard86 is weighted at 2x, EasyCode at 0.5x, and the remaining benchmark categories at 1x.

Validation Notes

Benchmark results are local measurements, not claims from the parent model repository.

License

Use is subject to the base model license and the licenses of turbo-tan/llama.cpp-tq3.