Text Generation
Transformers
Diffusers
Safetensors
English
gpt_oss
phillnet-2
gpt-oss
multimodal
image-generation
video-generation
speech
audio
custom-code
conversational
custom_code
Instructions to use ayjays132/Phillnet-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ayjays132/Phillnet-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ayjays132/Phillnet-2", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ayjays132/Phillnet-2", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("ayjays132/Phillnet-2", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ayjays132/Phillnet-2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ayjays132/Phillnet-2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ayjays132/Phillnet-2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ayjays132/Phillnet-2
- SGLang
How to use ayjays132/Phillnet-2 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 "ayjays132/Phillnet-2" \ --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": "ayjays132/Phillnet-2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "ayjays132/Phillnet-2" \ --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": "ayjays132/Phillnet-2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ayjays132/Phillnet-2 with Docker Model Runner:
docker model run hf.co/ayjays132/Phillnet-2
| from __future__ import annotations | |
| import shutil | |
| import subprocess | |
| from dataclasses import dataclass | |
| from pathlib import Path | |
| from PIL import Image | |
| class EncodeResult: | |
| path: Path | |
| encoder: str | |
| mp4: bool | |
| gif_path: Path | None = None | |
| audio_path: Path | None = None | |
| muxed_audio: bool = False | |
| def ffmpeg_available() -> bool: | |
| return shutil.which("ffmpeg") is not None | |
| def encode_video(frames: list[Image.Image], output_path: str | Path, *, fps: int, export_gif: bool = False) -> EncodeResult: | |
| path = Path(output_path) | |
| path.parent.mkdir(parents=True, exist_ok=True) | |
| fps = max(1, int(fps)) | |
| if path.suffix.lower() == ".gif": | |
| frames[0].save(path, save_all=True, append_images=frames[1:], duration=int(1000 / fps), loop=0) | |
| return EncodeResult(path, "pil_gif", False, path) | |
| try: | |
| import imageio.v3 as iio | |
| import numpy as np | |
| iio.imwrite(path, [np.asarray(frame.convert("RGB")) for frame in frames], fps=fps) | |
| gif_path = _write_gif(frames, path.with_suffix(".gif"), fps) if export_gif else None | |
| return EncodeResult(path, "imageio", path.suffix.lower() == ".mp4", gif_path) | |
| except Exception: | |
| pass | |
| ffmpeg = shutil.which("ffmpeg") | |
| if ffmpeg: | |
| tmp = path.parent / f"{path.stem}_frames" | |
| tmp.mkdir(parents=True, exist_ok=True) | |
| for idx, frame in enumerate(frames): | |
| frame.save(tmp / f"frame_{idx:05d}.png") | |
| subprocess.run( | |
| [ | |
| ffmpeg, | |
| "-y", | |
| "-framerate", | |
| str(fps), | |
| "-i", | |
| str(tmp / "frame_%05d.png"), | |
| "-pix_fmt", | |
| "yuv420p", | |
| str(path), | |
| ], | |
| check=True, | |
| stdout=subprocess.DEVNULL, | |
| stderr=subprocess.DEVNULL, | |
| ) | |
| gif_path = _write_gif(frames, path.with_suffix(".gif"), fps) if export_gif else None | |
| return EncodeResult(path, "ffmpeg", path.suffix.lower() == ".mp4", gif_path) | |
| fallback = path.with_suffix(".gif") | |
| _write_gif(frames, fallback, fps) | |
| return EncodeResult(fallback, "pil_gif_fallback", False, fallback) | |
| def _write_gif(frames: list[Image.Image], path: Path, fps: int) -> Path: | |
| frames[0].save(path, save_all=True, append_images=frames[1:], duration=int(1000 / fps), loop=0) | |
| return path | |
| def mux_audio(video_path: str | Path, audio_path: str | Path, output_path: str | Path | None = None) -> Path | None: | |
| ffmpeg = shutil.which("ffmpeg") | |
| if not ffmpeg: | |
| return None | |
| video = Path(video_path) | |
| audio = Path(audio_path) | |
| if not video.exists() or not audio.exists(): | |
| return None | |
| out = Path(output_path) if output_path is not None else video.with_name(f"{video.stem}_with_audio{video.suffix}") | |
| out.parent.mkdir(parents=True, exist_ok=True) | |
| command = [ | |
| ffmpeg, | |
| "-y", | |
| "-i", | |
| str(video), | |
| "-i", | |
| str(audio), | |
| "-map", | |
| "0:v:0", | |
| "-map", | |
| "1:a:0", | |
| "-c:v", | |
| "copy", | |
| "-c:a", | |
| "aac", | |
| "-shortest", | |
| str(out), | |
| ] | |
| subprocess.run(command, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) | |
| return out | |