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
File size: 3,282 Bytes
101858b ad2ce18 101858b ad2ce18 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | from __future__ import annotations
import shutil
import subprocess
from dataclasses import dataclass
from pathlib import Path
from PIL import Image
@dataclass
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
|