Instructions to use jdagostino/Splice-QFN-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jdagostino/Splice-QFN-BF16 with Transformers:

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

pipe = pipeline("image-text-to-text", model="jdagostino/Splice-QFN-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)

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

processor = AutoProcessor.from_pretrained("jdagostino/Splice-QFN-BF16")
model = AutoModelForMultimodalLM.from_pretrained("jdagostino/Splice-QFN-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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jdagostino/Splice-QFN-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jdagostino/Splice-QFN-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": "jdagostino/Splice-QFN-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/jdagostino/Splice-QFN-BF16

SGLang

How to use jdagostino/Splice-QFN-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 "jdagostino/Splice-QFN-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": "jdagostino/Splice-QFN-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 "jdagostino/Splice-QFN-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": "jdagostino/Splice-QFN-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 jdagostino/Splice-QFN-BF16 with Docker Model Runner:
```
docker model run hf.co/jdagostino/Splice-QFN-BF16
```

Splice-QFN BF16

tl;dr: This is a merge of the Swift 1.5 and OrcaRouter-Uncensored finetunes of Qwen3.8-Flash-Next; it does a fairly decent job at combining the shorter chain-of-thought from Swift and the reduced refusal from the OrcaRouter finetune.

This is the unquantized BF16 version of the weights, I have never run this; I needed to quantize it down to 4 bits for it to actually fit on my hardware, but I'm uploading it as a backup. The version I've actually tested is Splice-QFN-W4A16.

I had gork do the merge and it wrote a long and poorly-written system card here that I deleted. Short version is that it went through every tensor and did this:

output = swift + 1.0 * (orcarouter - qwen3.8-flash-next)

The n-gram table is not included here and didn't change in the merge so download it elsewhere. If you're following that leapdragon franken-vllm you can grab it form the same place it suggests: primitive-ai/Qwen3.8-Flash-Next-PLE-quant, put it in the folder ples_int4/.

Quantization

this is the full bf16 quant. again, I have never run this directly, use the w4a16 quant if you want something I've actually tested.

License

my attorney, who is also a large language model, has instructed me to state the following:

The Swift contribution in these weights is under the Swift Open License v1.0, included here as LICENSE. That license's redistributor notice covers a merge and a quant: keep that license, keep the attribution, and also comply with the Qwen Community License 1.0 for the base model. The Swift commercial threshold (gross revenue of one million USD, above which their enterprise license is required) still applies to the Swift part of this checkpoint. Nothing here grants use of the Swift, UkisAI, Qwen, or OrcaRouter names as if those projects shipped Splice.

Downloads last month: 24

Safetensors

Model size

180B params

Tensor type

BF16

I64

Model tree for jdagostino/Splice-QFN-BF16

Qwen/Qwen3.8-Flash-Next

orcarouter/Qwen3.8-Flash-Next-Uncensored

ukisai/Swift1.5-Qwen3.8-Flash-Next

Merge model

this model