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 / benchmarks /results /memory_4bit.json

Dexy2

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

0810902 verified 2 days ago

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	{
	"model": "Dexy2/Piko-9b",
	"label": "piko-9b",
	"environment": {
	"timestamp": "2026-07-29T14:39:26-0400",
	"python": "3.12.3",
	"platform": "Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.39",
	"torch": "2.10.0+cu128",
	"transformers": "5.5.0",
	"gpu": "NVIDIA GeForce RTX 5070 Ti",
	"vram_total_gb": 15.92,
	"dtype": "bfloat16",
	"quantization": "4bit"
	},
	"load": {
	"cold_load_seconds": 101.4,
	"weights_vram_gb": 7.342,
	"peak_during_load_gb": 7.371,
	"host_rss_before_gb": 0.81,
	"host_rss_after_gb": 1.283,
	"reported_parameters": 5724972272,
	"reported_vision_parameters": 230421232,
	"note": "quantized parameters report packed element counts, not logical parameters"
	},
	"contexts": [
	{
	"context_tokens": 512,
	"prefill_seconds": 0.805,
	"baseline_vram_gb": 7.342,
	"peak_vram_gb": 7.631,
	"activation_and_cache_gb": 0.289
	},
	{
	"context_tokens": 2048,
	"prefill_seconds": 0.402,
	"baseline_vram_gb": 7.35,
	"peak_vram_gb": 8.4,
	"activation_and_cache_gb": 1.05
	},
	{
	"context_tokens": 8192,
	"prefill_seconds": 1.59,
	"baseline_vram_gb": 7.35,
	"peak_vram_gb": 11.476,
	"activation_and_cache_gb": 4.126
	}
	],
	"failures": [
	{
	"context_tokens": 32768,
	"error": "RuntimeError: CUDA driver error: device not ready"
	}
	],
	"max_context_measured": 8192,
	"model_source_note": "run from a local copy of this checkpoint on internal NVMe; the path has been normalised to the canonical repository id"
	}