Instructions to use ukisai/Swift-1.5-Ornith-9B-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 ukisai/Swift-1.5-Ornith-9B-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 ukisai/Swift-1.5-Ornith-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ukisai/Swift-1.5-Ornith-9B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ukisai/Swift-1.5-Ornith-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ukisai/Swift-1.5-Ornith-9B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf ukisai/Swift-1.5-Ornith-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf ukisai/Swift-1.5-Ornith-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ukisai/Swift-1.5-Ornith-9B-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use ukisai/Swift-1.5-Ornith-9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ukisai/Swift-1.5-Ornith-9B-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": "ukisai/Swift-1.5-Ornith-9B-GGUF",
		"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/ukisai/Swift-1.5-Ornith-9B-GGUF:Q4_K_M

Ollama
How to use ukisai/Swift-1.5-Ornith-9B-GGUF with Ollama:
```
ollama run hf.co/ukisai/Swift-1.5-Ornith-9B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use ukisai/Swift-1.5-Ornith-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ukisai/Swift-1.5-Ornith-9B-GGUF:Q4_K_M

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": "ukisai/Swift-1.5-Ornith-9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ukisai/Swift-1.5-Ornith-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/ukisai/Swift-1.5-Ornith-9B-GGUF:Q4_K_M
```

Lemonade

How to use ukisai/Swift-1.5-Ornith-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ukisai/Swift-1.5-Ornith-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Swift-1.5-Ornith-9B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ukisai/Swift-1.5-Ornith-9B-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 ukisai/Swift-1.5-Ornith-9B-GGUF:Q4_K_M

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 ukisai/Swift-1.5-Ornith-9B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ukisai/Swift-1.5-Ornith-9B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ukisai/Swift-1.5-Ornith-9B-GGUF:Q4_K_M

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 "ukisai/Swift-1.5-Ornith-9B-GGUF:Q4_K_M" \
  --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"

Website • Learn more • Safetensors • GGUF files • Evaluation • License

Swift 1.5 Ornith 9B

GGUF quantizations. Derived directly from Swift 1.5 Ornith 9B with llama.cpp. Run a chosen quantization tier with a current llama.cpp-compatible runtime such as llama-server.

Swift 1.5 Ornith 9B is UkisAI's reasoning-efficient derivative of Ornith-1.5-9B. It uses 33.1% fewer thinking tokens while scoring 1.52 pp higher than the base on GPQA-Diamond.

Training approach

We made Swift Ornith 9B efficient by figuring out which tokens were linked to pathological overthinking and penalizing them without "attacking" the reasoning length directly, leading to "compressed" token usage.

Swift 1.5 produces shorter reasoning traces. In our testing, we also observe fewer overthinking errors.

Our training data is viewable here: https://huggingface.co/datasets/ukisai/Qwen3.8-27B-multi-turn-agent-sft albeit it is not used out of the box, but rather re-sampled, turned into proper RL environments etc.

Evaluation

The external results below compare Ornith-1.5-9B, the foundation base model, and Swift 1.5. Both models use the same saved evaluation protocols, and all scores are reported as final aggregate percentages.

Benchmark	Score		Mean tokens			Median tokens
Benchmark	Base	Swift 1.5	Base	Swift 1.5	Reduction	Reduction
General reasoning
GPQA-Diamond	83.00%	84.51%	15,760	11,782	↓ 25.2%	↓ 33.1%
IFBench	42.60%	40.47%	13,409	10,790	↓ 19.5%	↓ 24.7%
Mathematics
AIME 2026	92.67%	90.67%	37,872	35,460	↓ 6.4%	↓ 29.2%
AIME 2024	94.00%	93.33%	20,776	17,266	↓ 16.9%	↓ 12.2%
Coding
LiveCodeBench v6	59.20%	56.91%	41,057	34,343	↓ 16.4%	↓ 33.5%

Note: This is an experimental release. Swift 1.5 Ornith 9B thinks less than the base on every benchmark above but scores 0.7–2.3 pp lower on AIME, IFBench and LiveCodeBench. GPQA-Diamond, the benchmark we used to select this checkpoint, is the only one where it scores higher.

How to reproduce

Serving: BF16 · tensor parallelism 1 · context 262,144 · thinking on.
Sampling: temperature 1.0 · top_p 0.95 · top_k 20 · min_p 0 · presence_penalty 0.
Scoring: every attempt counts. Request errors, answers cut off at the output cap and empty answers count as wrong.
GPQA-Diamond: 198 questions × 3 seeds. The answer letter comes from the final answer, not from the thinking.
IFBench: 300 prompts × 5 seeds, official prompt-level loose score.
AIME 2026: 30 problems × 5 seeds, reasoning effort xhigh, scorer from UkisAI Benchmark_configs.
AIME 2024: 30 problems from MathArena × 5 seeds.
LiveCodeBench v6: the 175 problems that release v6 added, official pass@1 averaged over 5 seeds, 10-second limit per test.

Benchmark	Output cap	Cap hits, base	Cap hits, Swift 1.5
GPQA-Diamond (594 attempts)	81,920	48	24
IFBench (1,500 attempts)	81,920	86	69
AIME 2026 (150 attempts)	250,000	2	0
AIME 2024 (150 attempts)	250,000	1	0
LiveCodeBench v6 (875 attempts)	100,000	225	149

GGUF quantizations

File	Size
Swift-1.5-Ornith-9B-BF16.gguf	18.4 GB
Swift-1.5-Ornith-9B-Q8_0.gguf	9.8 GB
Swift-1.5-Ornith-9B-Q6_K.gguf	7.6 GB
Swift-1.5-Ornith-9B-Q5_K_M.gguf	6.6 GB
Swift-1.5-Ornith-9B-Q4_K_M.gguf	5.8 GB
mmproj-Swift-1.5-Ornith-9B-F16.gguf	0.9 GB

Every file includes the MTP head; the vision encoder is in the separate mmproj file. The benchmark scores above were measured on the BF16 safetensors checkpoint, not on these files.

Use a current llama.cpp build with Ornith-1.5-9B (qwen35) support:

llama-server -hf ukisai/Swift-1.5-Ornith-9B-GGUF:Q4_K_M --jinja
# or, after downloading:
llama-server -m Swift-1.5-Ornith-9B-Q4_K_M.gguf \
  --mmproj mmproj-Swift-1.5-Ornith-9B-F16.gguf --jinja -c 32768 \
  --temp 1.0 --top-p 0.95 --top-k 20 --min-p 0

The model supports up to 262,144 tokens of context; set -c to what fits your memory.

Recipe

All files were built with llama.cpp b11510 from the published BF16 safetensors, without an importance matrix. Every file passed a smoke test before upload.

License and access

Swift 1.5 Ornith 9B is a derivative of Ornith-1.5-9B (Ornith Team, MIT License). UkisAI's contribution, including the adapted weights, is licensed under the Apache License 2.0. See NOTICE for the change notice and attribution details.

The base model's MIT License still applies to the base model. This is an independent UkisAI release, not an official Ornith release or endorsement.

Citation

@misc{swift-1.5-ornith-9b,
  title  = {Swift 1.5 Ornith 9B},
  author = {UkisAI},
  year   = {2026},
  url    = {https://huggingface.co/ukisai/Swift-1.5-Ornith-9B}
}

Acknowledgements

We acknowledge Amazon Web Services for providing compute credits and infrastructure support for Swift's development, training, and evaluation.

Downloads last month: 3,148

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for ukisai/Swift-1.5-Ornith-9B-GGUF

Base model

ornith-ai/Ornith-1.5-9B

Finetuned

ukisai/Swift-1.5-Ornith-9B

Quantized

(3)

this model

Dataset used to train ukisai/Swift-1.5-Ornith-9B-GGUF

Collection including ukisai/Swift-1.5-Ornith-9B-GGUF

Swift 1.5 Ornith 9B

Collection

Swift 1.5 Ornith 9B: standalone BF16 weights and GGUF files for llama.cpp. • 2 items • Updated Oct 9 • 3