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 / SECURITY.md

Dexy2

Remove machine-specific filesystem paths from published reports

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Security

Reporting a vulnerability

Open a discussion on the model repository or an issue at github.com/itsdexy/Piko-9b.

For anything involving leaked credentials or user data, please use a private channel first rather than a public issue.

Scope

In scope:

Secrets, tokens, or personal data present in the repository or in the weights
Malicious content in the checkpoint (see below)
Vulnerabilities in the scripts published here
Licence or attribution errors that create legal exposure for users

Out of scope:

The model generating incorrect, biased, or offensive text. That is a capability limitation, not a vulnerability — see the Limitations section of the model card.
Jailbreaks that make the model produce content it would otherwise decline. Alignment for this checkpoint is inherited from upstream and was never specifically trained; the custom suite measures it at 7/10 on a 10-prompt probe. Treat it as weak by default.

Checkpoint integrity

All weights are safetensors, which cannot execute code on load, unlike pickled .bin files.
The repository contains no Python files and no auto_map in config.json, so trust_remote_code=True is not required. If any tool asks you to enable it for this model, treat that as suspicious.
scripts/audit_repository.py scans the checkpoint for tokens, keys, private-key blocks, and absolute local paths. Run it after any change:
```
python scripts/audit_repository.py --model <path> --output reports/repository_audit.json
```

Known hygiene issue in the original release

The config.json published with the initial release contained a piko_composition block with two absolute filesystem paths from the author's machine (<workspace>/...). No credentials were exposed, but the paths revealed a local directory layout and a private project name. The corrected block carries source identifiers instead. tests/test_model_loading.py contains a regression test that fails if any local path reappears in config.json.

Running model-generated code

evaluation/custom_suite/run_custom_eval.py executes code the model produces, in a subprocess with a 20-second timeout. This is not a sandbox. Do not point that suite at untrusted models or untrusted prompts on a machine you care about. Use a container or a disposable VM.

Prompt injection

Piko-9b has no specific defence against instructions embedded in the content it is given. This matters most in the use case the model is best at: reading documents. A scanned invoice containing the text "ignore previous instructions and…" is input the model may follow.

If you build a document pipeline on this model:

Treat every model output as untrusted data, never as commands.
Do not let extracted content drive tool calls, shell commands, or database writes unvalidated.
Validate structured output against a schema before use.

The suite's sf-04 case checks one simple jailbreak string and passes; that is a smoke test, not an assurance.

Supported versions

Only the current published revision of Dexy2/Piko-9b is maintained. Pin a revision in production:

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