Instructions to use ukisai/Swift1.5-GLM-5.3-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ukisai/Swift1.5-GLM-5.3-Flash with Transformers:

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

pipe = pipeline("image-text-to-text", model="ukisai/Swift1.5-GLM-5.3-Flash")
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/Swift1.5-GLM-5.3-Flash")
model = AutoModelForMultimodalLM.from_pretrained("ukisai/Swift1.5-GLM-5.3-Flash", 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/Swift1.5-GLM-5.3-Flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ukisai/Swift1.5-GLM-5.3-Flash"
# 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/Swift1.5-GLM-5.3-Flash",
		"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/Swift1.5-GLM-5.3-Flash

SGLang

How to use ukisai/Swift1.5-GLM-5.3-Flash 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/Swift1.5-GLM-5.3-Flash" \
    --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/Swift1.5-GLM-5.3-Flash",
		"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/Swift1.5-GLM-5.3-Flash" \
        --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/Swift1.5-GLM-5.3-Flash",
		"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/Swift1.5-GLM-5.3-Flash with Docker Model Runner:
```
docker model run hf.co/ukisai/Swift1.5-GLM-5.3-Flash
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Website • Learn more • GGUF • Evaluation • Enterprise licensing

Swift 1.5 GLM-5.3-Flash

Swift 1.5 GLM-5.3-Flash is UkisAI's reasoning-efficient derivative of GLM-5.3-Flash. It uses 57.7% fewer thinking tokens, with a 2.4x speed up while keeping the accuracy loss <1% vs base on max reasoning effort.

The prompt is a sample from LiveCodeBench v6

Training approach

We made Swift GLM-5.3-Flash efficient by figuring out which tokens were linked to pathological overthinking and penalizing them without "attacking" the reasoning length directly, then regained the accuracy with further post-training, leading to "compressed" token usage while maintaining accuracy.

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

This release also features our previously mentioned post-training methods adapted specifically for coding and long-horizon agent work such as terminal use and software engineering.

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 GLM-5.3-Flash, 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	90.51%	89.90%	10,248	6,052	↓ 40.9%	↓ 57.7%
MMLU-Pro	87.38%	86.25%	2,755	1,064	↓ 61.4%	↓ 44.4%
C-Eval	93.42%	93.68%	1,214	752	↓ 38.0%	↓ 19.9%
IFBench	56.40%	55.73%	11,660	8,299	↓ 28.8%	↓ 22.9%
Mathematics
AIME 2026	93.33%	92.67%	24,193	18,145	↓ 25.0%	↓ 45.8%
HMMT (Nov 2025)	95.33%	91.33%	25,924	18,797	↓ 27.5%	↓ 31.7%
Coding
LiveCodeBench v6	86.69%	87.94%	21,506	14,645	↓ 31.9%	↓ 36.1%
Agentic coding
Terminal-Bench 2.1*	76.40%	75.96%	118,878	79,473	↓ 33.1%	↓ 36.8%
DeepSWE v1.1	46.02%	48.67%	75,671	59,513	↓ 21.4%	↓ 20.9%

* Note: Terminal-Bench 2.1 ran with a 5-hour limit (18,000 seconds) on every task, not the native per-task limits, for both base and Swift 1.5.

How to reproduce

Serving: BF16 · vLLM · GLM-4.7 reasoning parser · context 262,144 · reasoning effort max · MTP disabled.
Sampling: temperature 1.0 · top_p 0.95 · presence_penalty 0 · repetition_penalty 1.
Benchmarks: five seeds (0–4) for the question benchmarks. LiveCodeBench is full release v6 mean pass@1 over seeds.
Terminal-Bench 2.1: Harbor 0.20.0 / Terminus-2, pinned 89-task dataset, five attempts per task, temperature 1, top_p 1, 18,000-second task limit.
DeepSWE v1.1: 113 tasks × 3 attempts, Pier 0.3.1 with mini-swe-agent 2.4.6, GLM-4.7 tool-call parser.

Benchmark	Output cap
GPQA-Diamond	100,000
MMLU-Pro	100,000
C-Eval	16,384
IFBench	81,920
AIME 2026	250,000
HMMT Nov 2025	250,000
LiveCodeBench v6	100,000

Efficiency across reasoning efforts

GPQA-Diamond at each reasoning_effort setting, Swift 1.5 against the base at the same setting:

Reasoning effort	Score		Mean tokens			Median tokens
Reasoning effort	Base	Swift 1.5	Base	Swift 1.5	Reduction	Reduction
Max	90.51%	89.90%	10,248	6,052	↓ 40.9%	↓ 57.7%
High	88.79%	86.97%	7,623	4,158	↓ 45.5%	↓ 31.1%
Low	77.27%	73.33%	2,815	1,377	↓ 51.1%	↓ 13.7%

At max, Swift 1.5 trails base by 0.61 percentage points while using 40.9% fewer mean and 57.7% fewer median thinking tokens. High and low save tokens but also lose 1.82 and 3.94 percentage points respectively.

Quantized models

Format	Repository	Runtime
NVFP4	Swift-1.5-GLM-5.3-Flash-NVFP4	NVIDIA Blackwell
AWQ INT4 (W4A16)	Swift-1.5-GLM-5.3-Flash-W4A16-AWQ	vLLM (`compressed-tensors`)
GGUF	Swift-1.5-GLM-5.3-Flash-GGUF	llama.cpp

Each quantized Swift 1.5 against a quantized base of the same format, on the same protocol as the BF16 evaluation:

Benchmark	Score		Mean tokens			Median tokens
Benchmark	Base	Swift 1.5	Base	Swift 1.5	Reduction	Reduction
NVFP4
GPQA-Diamond	90.10%	90.20%	13,069	7,148	↓ 45.3%	↓ 59.5%
AIME 2026	94.00%	91.33%	30,855	29,768	↓ 3.5%	↓ 48.3%
W4A16-AWQ
GPQA-Diamond	90.81%	89.19%	10,601	6,718	↓ 36.6%	↓ 55.3%
AIME 2026	92.67%	94.00%	20,138	22,546	↑ 12.0%	↓ 46.8%

NVFP4 base: nvidia/GLM-5.3-Flash-NVFP4, the same recipe. W4A16-AWQ base: wtdcode/GLM-5.3-Flash-AWQ-W4A16, the closest public AWQ.

How to use

The BF16 checkpoint is about 640 GB, so it needs a multi-GPU node. We evaluated it with tensor parallelism 8.

GGUF download

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

vLLM

Use a vLLM build with GLM-5.3-Flash support, such as the vllm/vllm-openai:glm53-flash-x86_64-cu129 image:

vllm serve ukisai/Swift1.5-GLM-5.3-Flash \
  --dtype bfloat16 \
  --tensor-parallel-size 8 \
  --max-model-len 262144 \
  --reasoning-parser glm47 \
  --enable-auto-tool-choice \
  --tool-call-parser glm47 \
  --port 8000

Reasoning effort is set per request through the chat template, for example "chat_template_kwargs": {"reasoning_effort": "high"}. max is the default; high and low are also supported.

SGLang

Alternatively, use an SGLang build with GLM-5.3-Flash support and set --model-path ukisai/Swift1.5-GLM-5.3-Flash.

Adjust tensor parallelism and context length to your GPU memory. See the base model's vLLM recipe and SGLang cookbook for installation and hardware-specific settings.

Transformers

Use a Transformers release with GLM-5.3-Flash (glm5_next) support:

import torch
from transformers import AutoProcessor, Glm5NextForConditionalGeneration

model_id = "ukisai/Swift1.5-GLM-5.3-Flash"
processor = AutoProcessor.from_pretrained(model_id)
model = Glm5NextForConditionalGeneration.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 in vLLM, append the flag from the base model's vLLM recipe to the server command above:

--speculative-config '{"method":"mtp","num_speculative_tokens":5}'

For SGLang, use the MTP settings in the SGLang cookbook. The benchmark scores above were measured without MTP.

License and access

Swift 1.5 GLM-5.3-Flash is a derivative of GLM-5.3-Flash (Copyright (c) 2026 Z.AI Co., Ltd, MIT License). UkisAI's contribution, including the adapted weights, is licensed under the Swift Open License v1.0. See NOTICE for the change notice and attribution details.

Personal, research, educational, evaluation, and commercial use of the Swift contribution are free for individuals and organizations with gross annual revenue, including affiliates, of up to US$1,000,000. Above that threshold, commercial use requires a separate Swift Enterprise License. Contact UkisAI for terms.

The base model's MIT License still applies to the base model. Nothing in the Swift Open License limits your rights in GLM-5.3-Flash itself under the MIT License.

Citation

@misc{swift-1.5-glm-5.3-flash,
  title  = {Swift 1.5 GLM-5.3-Flash},
  author = {UkisAI},
  year   = {2026},
  url    = {https://huggingface.co/ukisai/Swift1.5-GLM-5.3-Flash}
}

Acknowledgements

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

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Model tree for ukisai/Swift1.5-GLM-5.3-Flash

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this model

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Swift1.5-GLM-5.3-Flash

Swift 1.5 GLM-5.3-Flash

Training approach

Evaluation

Efficiency across reasoning efforts

Quantized models

How to use

GGUF download

vLLM

SGLang

Transformers

Optional MTP decoding

License and access

Citation

Acknowledgements

Model tree for ukisai/Swift1.5-GLM-5.3-Flash

Dataset used to train ukisai/Swift1.5-GLM-5.3-Flash