Instructions to use ukisai/Swift-1.5-Ornith-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="ukisai/Swift-1.5-Ornith-9B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ukisai/Swift-1.5-Ornith-9B")
model = AutoModelForMultimodalLM.from_pretrained("ukisai/Swift-1.5-Ornith-9B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ukisai/Swift-1.5-Ornith-9B 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"
# 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",
		"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

SGLang

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

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ukisai/Swift-1.5-Ornith-9B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ukisai/Swift-1.5-Ornith-9B",
		"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 images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ukisai/Swift-1.5-Ornith-9B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ukisai/Swift-1.5-Ornith-9B",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use ukisai/Swift-1.5-Ornith-9B with Docker Model Runner:
```
docker model run hf.co/ukisai/Swift-1.5-Ornith-9B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Website • Learn more • GGUF • Evaluation • License

Swift 1.5 Ornith 9B

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

How to use

The BF16 checkpoint is about 19 GB and runs on a single GPU. For the full 262,144-token context, use an 80 GB GPU.

GGUF download

The GGUF version is available for compatible llama.cpp-based runtimes.

vLLM

Use vLLM 0.19.1 or later:

vllm serve ukisai/Swift-1.5-Ornith-9B \
  --dtype bfloat16 \
  --max-model-len 262144 \
  --enable-prefix-caching \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_xml \
  --port 8000

SGLang

Alternatively, use SGLang 0.5.9 or later:

python -m sglang.launch_server \
  --model-path ukisai/Swift-1.5-Ornith-9B \
  --dtype bfloat16 \
  --context-length 262144 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder \
  --port 8000

Adjust the context length to your GPU memory.

Transformers

Use Transformers 5.8.1 or later:

import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "ukisai/Swift-1.5-Ornith-9B"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

To extend the context beyond 262,144 tokens, follow the YaRN settings on the Ornith-1.5-9B model card.

Optional MTP decoding

The checkpoint includes the base model's one-layer MTP head. To enable self-speculative decoding, append the corresponding flags to the server command above:

# vLLM
--speculative-config '{"method":"mtp","num_speculative_tokens":3}'

# SGLang
--speculative-algorithm NEXTN --speculative-num-steps 3 \
  --speculative-eagle-topk 1 --speculative-num-draft-tokens 4

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}
}