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.

You need to agree to share your Hugging Face information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

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. On GPQA-Diamond it uses 25.2% fewer thinking tokens (33.1% fewer on the median) and scores 1.52 pp higher than the base. Across our benchmarks it uses 6–25% fewer thinking tokens on the mean and 12–34% fewer on the median, while staying within 2.3 pp of the base on AIME, IFBench and LiveCodeBench.

This is an experimental Swift release. See Research status and limitations.

Training approach

We built Swift by identifying reasoning-marker tokens that, in our analysis, trigger overthinking in the model's reasoning rollouts. We then fine-tuned the model by penalizing usage of those tokens while it reasons.

Swift produces shorter reasoning traces while keeping accuracy close to the base model. In our testing, Swift 1.5 also hits the output cap less often than the base on every benchmark below.

The vision encoder, the multi-token prediction (MTP) layer, the token embeddings and the output head are unchanged from Ornith-1.5-9B.

Evaluation

All scores compare Ornith-1.5-9B with Swift 1.5 Ornith 9B, both in BF16 with thinking on. Both models used the same harness, sampling, seeds and output cap on every row. Token columns report thinking tokens per attempt; the visible answer is not counted.

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%

Swift 1.5 is ahead of base on GPQA-Diamond (+1.52 pp). It trails base on AIME 2024 (−0.67 pp), AIME 2026 (−2.00 pp), IFBench (−2.13 pp) and LiveCodeBench v6 (−2.29 pp).

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

Research status and limitations

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

We have not evaluated it on agentic, tool-use or vision benchmarks, and benchmark scores may not translate into reliable general-purpose behavior. These observations concern the specific model, runtimes and tests we used. This release shares a research result and its current limitations, rather than presenting a production-ready model.

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. It needs the same runtimes as Ornith-1.5-9B: Transformers ≥ 5.8.1, vLLM ≥ 0.19.1 or SGLang ≥ 0.5.9.

The model thinks before it answers. With the qwen3 reasoning parser below, the server returns the thinking in reasoning_content and the answer in content. We evaluated with temperature=1.0, top_p=0.95, top_k=20, min_p=0.0.

GGUF files for llama.cpp are in ukisai/Swift-1.5-Ornith-9B-GGUF.

vLLM

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

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

Both commands start an OpenAI-compatible server on port 8000. Lower the context length if your GPU has less memory. For contexts beyond 262,144 tokens (YaRN), see the Ornith-1.5-9B model card.

Transformers

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",
)

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}
}