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
| #!/usr/bin/env python3 | |
| """Strip machine-specific filesystem paths from anything destined for publication. | |
| The provenance information in these reports is worth publishing; the author's | |
| directory layout is not. This replaces concrete paths with neutral placeholders | |
| while leaving checkpoint *names* intact, since those are the actual provenance | |
| identifiers and are already public in config.json. | |
| python scripts/sanitize_paths.py --root . --check | |
| python scripts/sanitize_paths.py --root . --apply | |
| """ | |
| from __future__ import annotations | |
| import argparse | |
| import re | |
| import sys | |
| from pathlib import Path | |
| # Ordered: longest / most specific first, so a broad rule cannot eat a narrow one. | |
| REPLACEMENTS: list[tuple[re.Pattern[str], str]] = [ | |
| # Windows form, both single and JSON-escaped backslashes. | |
| ( | |
| re.compile(r"E:\\{1,2}New folder \(4\)\\{1,2}wraithfast-9b\\{1,2}wraith-finetune"), | |
| "<workspace>", | |
| ), | |
| (re.compile(r"E:\\{1,2}New folder \(4\)"), "<workspace>"), | |
| # POSIX / WSL form. | |
| (re.compile(r"/mnt/e/New folder \(4\)/wraithfast-9b/wraith-finetune"), "<workspace>"), | |
| (re.compile(r"/mnt/e/New folder \(4\)"), "<workspace>"), | |
| (re.compile(r"/mnt/c/piko9b-weights"), "<local-checkpoint>"), | |
| # Home directories and Windows user profiles, whoever they belong to. | |
| (re.compile(r"/home/[A-Za-z0-9_.-]+"), "<home>"), | |
| (re.compile(r"C:\\{1,2}Users\\{1,2}[A-Za-z0-9_.-]+"), "<home>"), | |
| ] | |
| # Leftover bare drive/folder references that survive the rules above. | |
| RESIDUAL = re.compile(r"New folder \(4\)|/mnt/[a-z]/|C:\\Users|/home/[a-z]") | |
| SUFFIXES = {".md", ".json", ".py", ".yaml", ".yml", ".txt", ".cff", ".toml", ".jinja"} | |
| SKIP_DIRS = {".git", "__pycache__", ".ruff_cache", ".pytest_cache", ".venv"} | |
| def iter_files(root: Path): | |
| for path in sorted(root.rglob("*")): | |
| if not path.is_file() or path.suffix not in SUFFIXES: | |
| continue | |
| if any(part in SKIP_DIRS for part in path.parts): | |
| continue | |
| # Never rewrite this script's own rules. | |
| if path.name == "sanitize_paths.py": | |
| continue | |
| yield path | |
| def sanitize(text: str) -> tuple[str, int]: | |
| total = 0 | |
| for pattern, replacement in REPLACEMENTS: | |
| text, count = pattern.subn(replacement, text) | |
| total += count | |
| return text, total | |
| def main() -> None: | |
| parser = argparse.ArgumentParser(description=__doc__) | |
| parser.add_argument("--root", type=Path, default=Path(".")) | |
| parser.add_argument("--apply", action="store_true", help="Rewrite files in place.") | |
| parser.add_argument("--check", action="store_true", help="Exit 1 if anything remains.") | |
| args = parser.parse_args() | |
| changed: list[tuple[Path, int]] = [] | |
| residual: list[str] = [] | |
| for path in iter_files(args.root): | |
| original = path.read_text(encoding="utf-8", errors="replace") | |
| cleaned, count = sanitize(original) | |
| if count: | |
| changed.append((path.relative_to(args.root), count)) | |
| if args.apply: | |
| path.write_text(cleaned, encoding="utf-8") | |
| final = cleaned if args.apply or count else original | |
| for line_number, line in enumerate(final.splitlines(), start=1): | |
| if RESIDUAL.search(line): | |
| residual.append( | |
| f"{path.relative_to(args.root)}:{line_number}: {line.strip()[:110]}" | |
| ) | |
| verb = "sanitised" if args.apply else "would sanitise" | |
| for path, count in changed: | |
| print(f" {verb} {path} ({count} occurrence(s))") | |
| print(f"\n{len(changed)} file(s) {verb}, {sum(c for _, c in changed)} replacement(s)") | |
| if residual: | |
| print(f"\n{len(residual)} residual reference(s) needing manual review:") | |
| for entry in residual[:40]: | |
| print(f" {entry}") | |
| if args.check and residual: | |
| sys.exit(1) | |
| if __name__ == "__main__": | |
| main() | |