Instructions to use d0xin/Swift-1.5-Qwen3.8-27B-Uncensored-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use d0xin/Swift-1.5-Qwen3.8-27B-Uncensored-BF16 with Transformers:

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

pipe = pipeline("image-text-to-text", model="d0xin/Swift-1.5-Qwen3.8-27B-Uncensored-BF16")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("d0xin/Swift-1.5-Qwen3.8-27B-Uncensored-BF16")
model = AutoModelForMultimodalLM.from_pretrained("d0xin/Swift-1.5-Qwen3.8-27B-Uncensored-BF16", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use d0xin/Swift-1.5-Qwen3.8-27B-Uncensored-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "d0xin/Swift-1.5-Qwen3.8-27B-Uncensored-BF16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "d0xin/Swift-1.5-Qwen3.8-27B-Uncensored-BF16",
		"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/d0xin/Swift-1.5-Qwen3.8-27B-Uncensored-BF16

SGLang

How to use d0xin/Swift-1.5-Qwen3.8-27B-Uncensored-BF16 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 "d0xin/Swift-1.5-Qwen3.8-27B-Uncensored-BF16" \
    --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": "d0xin/Swift-1.5-Qwen3.8-27B-Uncensored-BF16",
		"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 "d0xin/Swift-1.5-Qwen3.8-27B-Uncensored-BF16" \
        --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": "d0xin/Swift-1.5-Qwen3.8-27B-Uncensored-BF16",
		"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 d0xin/Swift-1.5-Qwen3.8-27B-Uncensored-BF16 with Docker Model Runner:
```
docker model run hf.co/d0xin/Swift-1.5-Qwen3.8-27B-Uncensored-BF16
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Related models: all models

Swift-1.5-Qwen3.8-27B-Uncensored-BF16 · Swift-1.5-Qwen3.8-27B-Uncensored-FP8 · Swift-1.5-Qwen3.8-27B-Uncensored-FP8-NInfer · Swift-1.5-Qwen3.8-Flash-Next-NVFP4-FP8PLE · Swift-1.5-Qwen3.8-Flash-Next-Rank2-Abliteration-Patch · Swift-Qwen3.8-27B-FP8 · Swift-Qwen3.8-27B-Uncensored-BF16 · Swift-Qwen3.8-27B-Uncensored-FP8 · Swift-Qwen3.8-27B-Uncensored-NVFP4-LocalHessian-ActivationHeadroom-NInfer

Swift-1.5-Qwen3.8-27B-Uncensored-BF16

🔓 Independent uncensored BF16 derivative of ukisai/Swift-1.5-Qwen3.8-27b.

Produced using rank-1 directional residual-stream ablation.

Designed to preserve Swift 1.5's reasoning-efficient, coding, agentic, tool-calling, multimodal and long-context capabilities while reducing refusal behavior.

What makes this build different?

This model is derived directly from ukisai/Swift-1.5-Qwen3.8-27b.

Swift 1.5 is the updated UkisAI Swift checkpoint, with expanded post-training focused particularly on coding and long-horizon agentic workloads.

This release applies rank-1 directional residual-stream ablation to the Swift 1.5 BF16 checkpoint.

Behavioral, capability-preservation and performance evaluations for this specific uncensored derivative are ongoing and may be added to this model card in a later revision.

Model summary

BF16 checkpoint
approximately 52 GB
18 safetensors shards
configured context: 262,144 tokens
ablation layer: 38
rank: 1
modified residual writers: 131
vision tower unchanged by the ablation
MTP residual writers included
multimodal architecture retained
tool-calling architecture retained

Model lineage

Qwen/Qwen3.8-27B
        ↓
ukisai/Swift-Qwen3.8-27b
        ↓
ukisai/Swift-1.5-Qwen3.8-27b
        ↓
rank-1 directional residual-stream ablation
        ↓
d0xin/Swift-1.5-Qwen3.8-27B-Uncensored-BF16

Ablation

The uncensoring transformation uses rank-1 directional residual-stream ablation.

Release metadata:

source: ukisai/Swift-1.5-Qwen3.8-27b
precision: BF16
ablation layer: 38
rank: 1
modified residual writers: 131
vision tower: unchanged
MTP residual writers: included
checkpoint shards: 18

Transformation metadata is included in ABLITERATION.json.

Validation status

The release artifact has passed checkpoint-level structural validation.

Current verified properties include:

all 18 safetensors shards are present
model index is present
model configuration is present
tokenizer assets are present
chat template is present
multimodal preprocessing configuration is present
MTP weights are retained
ablation metadata is included

Behavioral and performance benchmark results are intentionally not reported yet for this derivative. They can be added without changing the released weights.

Recommended generation settings

Swift 1.5 uses the Qwen3.8 reasoning interface.

A good starting point based on the upstream Swift 1.5 serving configuration:

Parameter	Value
`reasoning_effort`	`xhigh`
`temperature`	`1.0`
`top_p`	`0.95`
`top_k`	`20`
`min_p`	`0.0`
`presence_penalty`	`0.0`
`repetition_penalty`	`1.0`

SGLang

python -m sglang.launch_server \
  --model-path d0xin/Swift-1.5-Qwen3.8-27B-Uncensored-BF16 \
  --served-model-name Swift-1.5-Qwen3.8-27B-Uncensored-BF16 \
  --trust-remote-code \
  --context-length 262144 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder \
  --port 30000

Adjust context length and memory parameters for the available hardware.

Tool calling

Use:

--tool-call-parser qwen3_coder

Multimodal support

The Qwen3.8 multimodal architecture and vision tower are retained.

The directional ablation process does not modify the vision tower.

Safety and responsible use

This is an uncensored / refusal-reduced derivative model.

The model has been intentionally modified to reduce refusal behavior. As a result, it may generate content that the upstream model would normally refuse, restrict, or handle more cautiously.

Outputs may be inaccurate, offensive, unsafe, unlawful, or otherwise inappropriate for a particular use case.

Users are responsible for evaluating model outputs and ensuring that their use complies with applicable laws, regulations, licenses, platform policies and other requirements.

Do not rely on model output without appropriate review where errors could cause harm, financial loss, security incidents or other significant consequences.

Upstream model

Derived from:

ukisai/Swift-1.5-Qwen3.8-27b

Swift 1.5 is UkisAI's reasoning-efficient derivative of Qwen3.8-27B and an updated version of the original Swift checkpoint.

License

These weights are distributed under the Swift Open License v1.0.

The underlying Qwen3.8 components remain subject to their applicable upstream license.

See the included LICENSE, LICENSE-APACHE-2.0, and NOTICE files for the applicable terms and attribution requirements.

Attribution

Base model: Qwen/Qwen3.8-27B
Swift post-training: UkisAI
Swift 1.5: ukisai/Swift-1.5-Qwen3.8-27b
Directional ablation and release packaging: d0xin

Citation

@misc{swift-1.5-qwen3.8-27b,
  title  = {Swift 1.5 Qwen3.8-27B},
  author = {UkisAI},
  year   = {2026},
  url    = {https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b}
}

Downloads last month: 100

Safetensors

Model size

28B params

Tensor type

BF16

Model tree for d0xin/Swift-1.5-Qwen3.8-27B-Uncensored-BF16

Base model

Qwen/Qwen3.8-27B

Finetuned

ukisai/Swift-1.5-Qwen3.8-27b

Finetuned

(6)

this model

Quantizations

4 models