Instructions to use Dexy2/Piko-9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dexy2/Piko-9b with Transformers:

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

pipe = pipeline("image-text-to-text", model="Dexy2/Piko-9b")
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("Dexy2/Piko-9b")
model = AutoModelForMultimodalLM.from_pretrained("Dexy2/Piko-9b", 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 Dexy2/Piko-9b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dexy2/Piko-9b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dexy2/Piko-9b",
		"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/Dexy2/Piko-9b

SGLang

How to use Dexy2/Piko-9b 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 "Dexy2/Piko-9b" \
    --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": "Dexy2/Piko-9b",
		"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 "Dexy2/Piko-9b" \
        --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": "Dexy2/Piko-9b",
		"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 Dexy2/Piko-9b with Docker Model Runner:
```
docker model run hf.co/Dexy2/Piko-9b
```

Piko-9b / ruff.toml

Dexy2

Rewrite model card around verified evidence; correct misattributed benchmarks and config path leak

0810902 verified 1 day ago

Raw

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	line-length = 100
	target-version = "py310"

	[lint]
	select = ["E", "F", "W", "I", "B", "SIM", "UP", "PL", "ISC"]

	ignore = [
	# safe_open() handles from safetensors expose .keys() as their API; they are
	# not dicts, so `key in handle` does not work.
	"SIM118",
	# subprocess.run without check=True is deliberate throughout: this repository
	# records failures as data rather than raising on them.
	"PLW1510",
	"PLR0912", # branch count — the scoring dispatch is a flat, readable table
	"PLR0915", # statement count — likewise for the evaluation drivers
	"PLR2004", # magic values are fine in measurement thresholds
	# Imports of torch/transformers are deliberately lazy so that --help and
	# --dry-run work in an environment without them installed.
	"PLC0415",
	# The scoring dispatch in run_custom_eval.py is a deliberately flat table of
	# `if kind == ...: return` branches — easier to audit than a handler registry,
	# and auditability is the point of that suite.
	"PLR0911",
	# A bare try/except/pass reads more plainly than contextlib.suppress in the
	# number-parsing helper.
	"SIM105",
	# inference_cli.py intentionally rewrites the streamed chunk to strip the
	# reasoning span mid-stream.
	"PLW2901",
	]

	[lint.per-file-ignores]
	"tests/*" = ["PLR2004"]

	[format]
	quote-style = "double"