Image-Text-to-Text
Transformers
Safetensors
English
qwen3_5
piko
piko-9b
multimodal
vision-language
hybrid-attention
linear-attention
ocr
document-understanding
conversational
Instructions to use Dexy2/Piko-9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
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"""Validate the model card before publishing.
Checks the YAML front matter, verifies that every number in a results table is
backed by a file under evaluation/results or benchmarks/results, and refuses
placeholder values. The point is to make it hard to reintroduce the original
release's defect: benchmark numbers measured on a different checkpoint.
python scripts/validate_model_card.py --readme README.md
"""
from __future__ import annotations
import argparse
import json
import re
import sys
from pathlib import Path
from typing import Any
REQUIRED_METADATA = ["license", "language", "library_name", "pipeline_tag", "base_model", "tags"]
REQUIRED_SECTIONS = [
"Model summary",
"Relationship to the base model",
"Intended uses",
"Out-of-scope uses",
"Architecture",
"Training",
"Evaluation",
"Benchmark results",
"Hardware requirements",
"Quantization",
"Limitations",
"Reproducibility",
"Citation",
"License",
]
PLACEHOLDERS = [
"TODO",
"FIXME",
"XXX",
"[More Information Needed]",
"lorem ipsum",
"TBD",
]
# Anything that looks like a leaked local path.
PATH_PATTERNS = [
re.compile(r"/mnt/[a-z]/"),
re.compile(r"/home/[A-Za-z0-9_.-]+/"),
re.compile(r"[Cc]:[\\/]+Users[\\/]+"),
]
SECRET_PATTERNS = [
re.compile(r"\bhf_[A-Za-z0-9]{34,}\b"),
re.compile(r"\bsk-[A-Za-z0-9]{20,}\b"),
re.compile(r"\bAKIA[0-9A-Z]{16}\b"),
]
ALLOWED_STATUSES = {
"not run",
"unavailable",
"could not verify",
"not tested",
"n/a",
"—",
"-",
}
def parse_front_matter(text: str) -> tuple[dict[str, Any] | None, str]:
if not text.startswith("---"):
return None, text
end = text.find("\n---", 3)
if end == -1:
return None, text
block = text[3:end]
body = text[end + 4 :]
try:
import yaml
return yaml.safe_load(block), body
except ImportError:
# Fall back to a shallow key scan so the check still works without pyyaml.
keys = dict(re.findall(r"^([a-z_]+):", block, re.MULTILINE))
return {k: True for k in keys}, body
def collect_measured_numbers(results_dirs: list[Path]) -> set[str]:
"""Every percentage and ratio that appears in a committed result file."""
found: set[str] = set()
for directory in results_dirs:
for path in directory.rglob("*.json"):
try:
data = json.loads(path.read_text(encoding="utf-8"))
except (json.JSONDecodeError, OSError):
continue
_walk(data, found)
return found
def _walk(node: Any, found: set[str]) -> None:
if isinstance(node, dict):
for key, value in node.items():
if key in ("accuracy", "score") and isinstance(value, (int, float)):
found.add(f"{value * 100:.1f}")
found.add(f"{value * 100:.0f}")
if key in ("passed", "total") and isinstance(value, int):
found.add(str(value))
_walk(value, found)
elif isinstance(node, list):
for item in node:
_walk(item, found)
def check_tables(body: str, measured: set[str]) -> list[str]:
"""Every percentage in a results table must be measured or an allowed status."""
problems: list[str] = []
in_results = False
for line in body.splitlines():
lowered = line.lower()
if lowered.startswith("#"):
in_results = "benchmark" in lowered or "result" in lowered
continue
if not in_results or not line.strip().startswith("|"):
continue
cells = [c.strip() for c in line.strip().strip("|").split("|")]
for cell in cells:
stripped = cell.replace("**", "").strip()
if stripped.lower() in ALLOWED_STATUSES or not stripped:
continue
match = re.fullmatch(r"([+-]?\d+(?:\.\d+)?)\s*%", stripped)
if not match:
continue
value = match.group(1).lstrip("+-")
if value.startswith("0.") or value in ("0",):
continue
normalised = {f"{float(value):.1f}", f"{float(value):.0f}"}
if not (normalised & measured):
problems.append(
f" {stripped} appears in a results table but is not in any "
f"committed result file"
)
return problems
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--readme", type=Path, default=Path("README.md"))
parser.add_argument(
"--results",
type=Path,
nargs="*",
default=[Path("evaluation/results"), Path("benchmarks/results")],
)
parser.add_argument(
"--strict-numbers",
action="store_true",
help="Fail on unbacked table numbers instead of warning.",
)
args = parser.parse_args()
if not args.readme.is_file():
sys.exit(f"Model card not found: {args.readme}")
text = args.readme.read_text(encoding="utf-8")
metadata, body = parse_front_matter(text)
errors: list[str] = []
warnings: list[str] = []
if metadata is None:
errors.append("No YAML front matter found.")
else:
for key in REQUIRED_METADATA:
if key not in metadata:
errors.append(f"Front matter is missing required key: {key}")
license_is_apache = str(metadata.get("license", "")).lower() == "apache-2.0"
if license_is_apache and not Path("NOTICE").is_file():
errors.append(
"License is apache-2.0 but no NOTICE file exists. Upstream MIT "
"attribution must be preserved."
)
for section in REQUIRED_SECTIONS:
if section.lower() not in body.lower():
warnings.append(f"No section mentioning: {section}")
for placeholder in PLACEHOLDERS:
if placeholder.lower() in body.lower():
errors.append(f"Placeholder text present: {placeholder!r}")
for pattern in PATH_PATTERNS:
for hit in set(pattern.findall(text)):
errors.append(f"Local path leaked in model card: {hit!r}")
for pattern in SECRET_PATTERNS:
if pattern.search(text):
errors.append("Possible secret detected in model card.")
existing = [d for d in args.results if d.is_dir()]
if existing:
measured = collect_measured_numbers(existing)
table_problems = check_tables(body, measured)
(errors if args.strict_numbers else warnings).extend(table_problems)
else:
warnings.append("No result directories found; skipped number verification.")
for link in re.findall(r"\]\((?!https?://|#)([^)]+)\)", body):
target = (args.readme.parent / link.split("#")[0]).resolve()
if not target.exists():
errors.append(f"Broken relative link: {link}")
for warning in warnings:
print(f"WARN {warning}")
for error in errors:
print(f"ERROR {error}")
print(f"\n{len(errors)} error(s), {len(warnings)} warning(s) in {args.readme}")
sys.exit(1 if errors else 0)
if __name__ == "__main__":
main()
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