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 / reports /release_checklist.md

Dexy2

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

0810902 verified 10 days ago

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Release Checklist — Piko-9b 1.0.0

Run 2026-07-29. Every line is either PASS with evidence, FAIL with the reason, or NOT RUN with why. Nothing is marked done on the strength of an assumption.

Model integrity

Check	Status	Evidence
`config.json` parses	PASS	`scripts/audit_repository.py`
Tokenizer loads	PASS	`AutoTokenizer` — `TokenizersBackend`
Processor loads	PASS	`Qwen3VLProcessor`, requires `torchvision`
Model loads without `trust_remote_code`	PASS	`AutoModelForMultimodalLM`, no `auto_map`, no `.py` in repo
Parameter count verified	PASS	9,653,104,368 from safetensors headers
Shard integrity	PASS	11 shards, 775 tensors, all BF16, 0 unexplained keys
Weights unchanged from previous release	PASS	Byte-identical; this release changes documentation only

Provenance

Check	Status	Evidence
Base model identified	PASS	`deepreinforce-ai/Ornith-1.0-9B` + `Qwen/Qwen3.5-9B`
Lineage verified at tensor level	PASS	427/427 language + 348/348 vision tensors matched bitwise
Adapter presence tested	PASS	Both probed adapters not merged; `\|cand−lang\| = 0` exactly
Ornith's own base model	NOT VERIFIED	Card declares none; structural identity to Qwen3.5-9B is an inference
Published checkpoint byte-compared to Hub	NOT RUN	Verified by file name and byte size only; 21 GB re-download not performed

Documentation honesty

Check	Status	Evidence
No fabricated numbers	PASS	`scripts/validate_model_card.py` cross-checks every table percentage
Unrun benchmarks labelled	PASS	9 standard benchmarks marked Not run
Base-model scores not presented as Piko's	PASS	Misattributed scores removed; explicit warning added
Unsupported capabilities not advertised	PASS	Video/audio marked unsupported; identity caveat stated
Failures recorded, not omitted	PASS	5 suite failures adjudicated in the card; 32K OOM recorded
Model card metadata validates	PASS	0 errors, 0 warnings
Relative links resolve	PASS	Checked by `validate_model_card.py`

Hygiene

Check	Status	Evidence
No secrets	PASS	Token/key/private-key scan over all text files: 0 hits
No absolute local paths in shipped files	PASS	Result files normalised to repo ids; remaining mentions are deliberate documentation
`config.json` path leak fixed	PASS (proposed)	Correction specified in audit §8; not yet applied to the Hub
Regression test for path leak	PASS	`test_config_contains_no_absolute_paths`
`.gitignore` excludes weights	PASS

Code quality

Check	Status	Evidence
Syntax check, all Python	PASS	`compileall` clean
Lint	PASS	`ruff check` — all checks passed
Format	PASS	`ruff format --check` — 27 files formatted
Fast tests	PASS	10 passed, 14 deselected, no weights or GPU needed
Slow tests	NOT RUN	14 collected; require `PIKO_MODEL_PATH`. Behaviour they assert was verified directly by `scripts/validate_inference.py`

Functional validation

Check	Status	Evidence
Text generation	PASS	`reports/inference_validation.json` — 12/12
Image / OCR	PASS	Read "NORTHGATE HARDWARE" and 22.75 correctly
Document JSON extraction	PASS	10/10 on custom suite
Chart reading	PASS	Read all four bar values correctly
Multi-turn	PASS
Batch inference	PASS
Greedy determinism	PASS	Identical across repeats
Long context	PASS	Needle found at 14,429 tokens; suite passes at 2K/8K/32K
Streaming	PASS (code)	Implemented in `examples/inference_cli.py`; not separately measured
bfloat16 unquantized	NOT RUN	Needs ~22 GB; test GPU has 15.92 GB
float16	NOT RUN	Supported by flag; not exercised
8-bit	NOT RUN
CPU loading	NOT RUN	~38.6 GB float32 and no fast path; not viable
`device_map="auto"`	FAIL — by design	Produces `!!!!!` degenerate output; documented as unsupported
Video input	NOT RUN	No fixture built

Evaluation

Check	Status	Evidence
Smoke evaluation	PASS	8/8 required
Custom suite, Piko-9b	PASS	65/70
Custom suite, base model	PASS	63/70, identical settings
Like-for-like comparison	PASS	Settings matched; tool would have refused otherwise
Statistical honesty	PASS	Wilson intervals; every category reads No significant difference
Standard public benchmarks	NOT RUN	1–3 h per model each, ×2 for baseline; scripts provided

Performance

Check	Status	Evidence
Cold load	PASS	101–119 s NVMe
Peak VRAM	PASS	7.37 GB resident, 13.4 GB at 8K×4
Host RAM	PASS	1.28 GB RSS
Throughput, 3 contexts × 3 batches	PASS	9/9 configurations
Image preprocessing	PASS	13 ms median
32,768-token single forward	FAIL — recorded	OOM on 15.92 GB; 32K retrieval via `generate()` works

Quantization

Check	Status	Evidence
4-bit NF4	PASS	All results measured this way; vision path intact
8-bit	NOT RUN
GGUF / AWQ / GPTQ	NOT PRODUCED	Scripts provided, unexecuted, documented as unvalidated
Quantized artefact published	NO	Nothing to validate, nothing claimed

Blocking items before publishing

Apply the config.json correction on the Hub — remove the two absolute paths from piko_composition and set transformers_version to 5.5.0. This is the only change that touches the model repository itself, and it is live right now.
Decide what the repository is for. The evidence says Piko-9b performs the same as Qwen/Qwen3.5-9B within measurement error. That is publishable, but only with the honesty this release adopts. Do not restore the old benchmark table.

Not blocking, worth doing

Run one standard benchmark pair (GSM8K 200 items is the cheapest useful one).
Test the six unmerged Piko adapters against the composition — they may improve it, and nobody has checked.
Build a video fixture and either support or explicitly disable the video path.
Test image inputs outside rendered documents.