Affine-Q3.5ft-5DHWeG9naPinHGTZp43akB6LE3ztq7xA6uWg84synAHZqEFR

Instructions to use SuperDan/Affine-Q3.5ft-5DHWeG9naPinHGTZp43akB6LE3ztq7xA6uWg84synAHZqEFR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SuperDan/Affine-Q3.5ft-5DHWeG9naPinHGTZp43akB6LE3ztq7xA6uWg84synAHZqEFR with Transformers:

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

pipe = pipeline("image-text-to-text", model="SuperDan/Affine-Q3.5ft-5DHWeG9naPinHGTZp43akB6LE3ztq7xA6uWg84synAHZqEFR")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("SuperDan/Affine-Q3.5ft-5DHWeG9naPinHGTZp43akB6LE3ztq7xA6uWg84synAHZqEFR")
model = AutoModelForImageTextToText.from_pretrained("SuperDan/Affine-Q3.5ft-5DHWeG9naPinHGTZp43akB6LE3ztq7xA6uWg84synAHZqEFR")
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

vLLM

How to use SuperDan/Affine-Q3.5ft-5DHWeG9naPinHGTZp43akB6LE3ztq7xA6uWg84synAHZqEFR with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SuperDan/Affine-Q3.5ft-5DHWeG9naPinHGTZp43akB6LE3ztq7xA6uWg84synAHZqEFR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SuperDan/Affine-Q3.5ft-5DHWeG9naPinHGTZp43akB6LE3ztq7xA6uWg84synAHZqEFR",
		"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/SuperDan/Affine-Q3.5ft-5DHWeG9naPinHGTZp43akB6LE3ztq7xA6uWg84synAHZqEFR

SGLang

How to use SuperDan/Affine-Q3.5ft-5DHWeG9naPinHGTZp43akB6LE3ztq7xA6uWg84synAHZqEFR 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 "SuperDan/Affine-Q3.5ft-5DHWeG9naPinHGTZp43akB6LE3ztq7xA6uWg84synAHZqEFR" \
    --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": "SuperDan/Affine-Q3.5ft-5DHWeG9naPinHGTZp43akB6LE3ztq7xA6uWg84synAHZqEFR",
		"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 "SuperDan/Affine-Q3.5ft-5DHWeG9naPinHGTZp43akB6LE3ztq7xA6uWg84synAHZqEFR" \
        --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": "SuperDan/Affine-Q3.5ft-5DHWeG9naPinHGTZp43akB6LE3ztq7xA6uWg84synAHZqEFR",
		"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 SuperDan/Affine-Q3.5ft-5DHWeG9naPinHGTZp43akB6LE3ztq7xA6uWg84synAHZqEFR with Docker Model Runner:
```
docker model run hf.co/SuperDan/Affine-Q3.5ft-5DHWeG9naPinHGTZp43akB6LE3ztq7xA6uWg84synAHZqEFR
```

Affine-Q3.5ft

"Just a little guy trying to do his best on some benchmarks."

Fine-tuned variant of Qwen3.5-27B. We took an already-scary model and made it slightly more unhinged in the direction of agentic reasoning and tool use. You're welcome.

What Is This

Qwen3.5-27B has a genuinely weird architecture — hybrid linear recurrent attention (GatedDeltaNet) fused with sparse self-attention. Training on it requires custom infrastructure that doesn't Just Work™ out of the box. We built the infrastructure. It works. The model is better now.

The fine-tuning applies reward-shaped optimization with a custom signal targeting structured reasoning, tool-call formatting, and multi-step task completion. The result is a model that is annoyingly good at following instructions and won't hallucinate a fake API endpoint at you (usually).

Why Does This Exist

We're competing on Bittensor Subnet 120 (Affine) — a subnet where validators send your model agentic coding tasks and score it on whether it actually solves them. It's basically a gladiator arena for language models, except nobody bleeds and the prize is TAO emissions.

Our model enters the arena. We believe in it. We have trained it. We have watched it learn. We are emotionally attached in a way that is probably not healthy.

Capabilities

Agentic multi-step reasoning
Structured tool calling (won't invent tools that don't exist — much)
Code generation and debugging
Following complex instructions without quietly giving up halfway through

Architecture Notes

The GatedDeltaNet linear attention layers in Qwen3.5 require specialized kernel support that most training frameworks quietly don't have. Running this model without the right stack means your GPU sits at 0% pretending to work while the CPU slowly suffers. We have the right stack. The GPU is very busy and very warm.

Training

Fine-tuned by AffineGo.
Base: Qwen/Qwen3.5-27B | Precision: BF16 | Fully merged weights

No LoRA adapters were harmed in the making of this model.

License

Apache 2.0. Go nuts.

Downloads last month: 4

Safetensors

Model size

28B params

Tensor type

BF16

F32

Model tree for SuperDan/Affine-Q3.5ft-5DHWeG9naPinHGTZp43akB6LE3ztq7xA6uWg84synAHZqEFR

Base model

Qwen/Qwen3.5-27B

Finetuned

(303)

this model