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 / docs /installation.md

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Installation

Requirements

Component	Minimum	Notes
Python	3.10 – 3.12	3.13+ works if wheels exist for your torch build; 3.15 currently has no `torchvision` wheel
`transformers`	5.5	`AutoModelForMultimodalLM` does not exist in 4.x
`torch`	2.6	CUDA build; validated on 2.10.0+cu128
`torchvision`	any matching build	Mandatory — `AutoProcessor` fails to construct without it
`accelerate`	0.30	device placement
`bitsandbytes`	0.43	only for 4-bit / 8-bit
`pillow`	10.0	image input
GPU	8 GB (4-bit) / 22 GB (bf16)	CUDA required; see hardware.md

trust_remote_code is not required. The repository ships no Python files.

Quick install

python -m venv .venv
source .venv/bin/activate        # Windows: .venv\Scripts\activate

pip install torch torchvision --index-url https://download.pytorch.org/whl/cu128
pip install -r requirements.txt

For evaluation and development:

pip install -r requirements-dev.txt

Verifying the install

python - <<'PY'
import torch, transformers, torchvision
assert tuple(int(x) for x in transformers.__version__.split(".")[:2]) >= (5, 5), transformers.__version__
print("torch", torch.__version__, "cuda", torch.cuda.is_available())
print("transformers", transformers.__version__)
print("torchvision", torchvision.__version__)
print("gpu", torch.cuda.get_device_name(0) if torch.cuda.is_available() else "NONE")
PY

All four lines must print, and cuda must be True. CPU-only inference is not a supported configuration for this model — see hardware.md.

Getting the weights

From the Hub

from transformers import AutoModelForMultimodalLM
model = AutoModelForMultimodalLM.from_pretrained("Dexy2/Piko-9b")

Pin a revision for reproducible work:

model = AutoModelForMultimodalLM.from_pretrained("Dexy2/Piko-9b", revision="<commit-sha>")

The repository is public, so no authentication is needed. If you are behind a proxy or working with a private mirror:

hf auth login

Downloading ahead of time

hf download Dexy2/Piko-9b --local-dir ./piko-9b

≈ 21 GB across 11 safetensors shards, plus a 20 MB tokenizer.

From a local directory

Every script in this repository accepts a path anywhere a repo id is accepted:

python examples/inference_transformers.py --model ./piko-9b --prompt "Hello"

Load the checkpoint from internal NVMe. Loading 21 GB from an external USB disk is I/O bound and takes 10–20 minutes per load; from NVMe it takes seconds.

CUDA compatibility

torch build	Driver	Status
`2.10.0+cu128`	≥ 525	Validated for every result in this repository
`cu121` / `cu124` builds	≥ 525	Expected to work; not tested here
ROCm	—	Not tested
CPU-only	—	Loads, but see hardware.md before trying

Blackwell cards (RTX 50-series) need a cu128 or newer build.

Optional: linear-attention kernels

pip install flash-linear-attention causal-conv1d

24 of the 32 layers are linear-attention. Without these kernels transformers logs "The fast path is not available" and falls back to pure PyTorch — correct, but slower. Every measurement in this repository was taken without these kernels, so treat published throughput as a floor.

Reproducible environment

pip install -r requirements-lock.txt   # exact versions used for the published results

If that file is absent, the environment behind every measured number is recorded in the environment block of each JSON file under evaluation/results/ and benchmarks/results/.