Instructions to use greenfield0810/affine-ark-b4d0e92fef1e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use greenfield0810/affine-ark-b4d0e92fef1e with Transformers:

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

pipe = pipeline("image-text-to-text", model="greenfield0810/affine-ark-b4d0e92fef1e")
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("greenfield0810/affine-ark-b4d0e92fef1e")
model = AutoModelForMultimodalLM.from_pretrained("greenfield0810/affine-ark-b4d0e92fef1e", 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 greenfield0810/affine-ark-b4d0e92fef1e with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "greenfield0810/affine-ark-b4d0e92fef1e"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "greenfield0810/affine-ark-b4d0e92fef1e",
		"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/greenfield0810/affine-ark-b4d0e92fef1e

SGLang

How to use greenfield0810/affine-ark-b4d0e92fef1e 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 "greenfield0810/affine-ark-b4d0e92fef1e" \
    --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": "greenfield0810/affine-ark-b4d0e92fef1e",
		"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 "greenfield0810/affine-ark-b4d0e92fef1e" \
        --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": "greenfield0810/affine-ark-b4d0e92fef1e",
		"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 greenfield0810/affine-ark-b4d0e92fef1e with Docker Model Runner:
```
docker model run hf.co/greenfield0810/affine-ark-b4d0e92fef1e
```

Affine archive — artifact `b4d0e92fef1e`

An unmodified copy of a competitor checkpoint from the Bittensor subnet 120 (Affine) leaderboard, preserved because repos on that board are routinely flipped private within days of duelling (31% of all challengers that have ever duelled were already unreachable when this archive was built).

This is not my model. It was uploaded by the source account below and is mirrored byte-for-byte. Original: arbosfan/Affine-5cj9mpkjrr-rise at revision b8b630b767c2.


weights_sha	`b4d0e92fef1e6a8b61b9778d2f1c26ddecf647b7fda823a74822ac5fb517f9f4`
reigns held	never crowned
duels / wins	5 / 0
alias repos	5 across 4 coldkeys
size	70.21 GB in 29 shards
model group	`d31efcc558a5` (32 near-identical uploads)

Full provenance — every alias repo, hotkey, coldkey and duel outcome — is in _affine_provenance.json.

Ask and it will be taken down.

Downloads last month: 8

Safetensors

Model size

35B params

Tensor type

BF16

Affine archive — artifact b4d0e92fef1e

Affine archive — artifact `b4d0e92fef1e`