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 | |
| from pathlib import Path | |
| import platform | |
| import shutil | |
| import subprocess | |
| import wave | |
| try: | |
| import winsound | |
| except ImportError: # pragma: no cover - only exercised on non-Windows hosts | |
| winsound = None | |
| import numpy as np | |
| import soundfile as sf | |
| import torch | |
| import torchaudio | |
| def select_best_device(explicit: str | None = None) -> str: | |
| if explicit: | |
| return explicit | |
| if torch.cuda.is_available(): | |
| return "cuda" | |
| if hasattr(torch.backends, "mps") and torch.backends.mps.is_available(): | |
| return "mps" | |
| return "cpu" | |
| def select_runtime_dtype(device: str, preferred: torch.dtype | None = None) -> torch.dtype: | |
| if preferred is not None: | |
| return preferred | |
| if device == "cuda": | |
| return torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16 | |
| return torch.float32 | |
| def ensure_parent_dir(path: str | Path) -> Path: | |
| resolved = Path(path) | |
| resolved.parent.mkdir(parents=True, exist_ok=True) | |
| return resolved | |
| def save_waveform(path: str | Path, waveform: torch.Tensor, sample_rate: int) -> Path: | |
| output_path = ensure_parent_dir(path) | |
| audio = waveform.detach().cpu() | |
| if audio.dim() == 1: | |
| audio = audio.unsqueeze(0) | |
| try: | |
| torchaudio.save(str(output_path), audio, sample_rate) | |
| except Exception: | |
| audio = audio.clamp(-1.0, 1.0) | |
| pcm16 = (audio.numpy() * 32767.0).astype(np.int16) | |
| with wave.open(str(output_path), "wb") as handle: | |
| handle.setnchannels(int(pcm16.shape[0])) | |
| handle.setsampwidth(2) | |
| handle.setframerate(int(sample_rate)) | |
| handle.writeframes(pcm16.T.tobytes()) | |
| return output_path | |
| def load_waveform(path: str | Path) -> tuple[torch.Tensor, int]: | |
| try: | |
| waveform, sample_rate = torchaudio.load(str(path)) | |
| return waveform, sample_rate | |
| except Exception: | |
| audio, sample_rate = sf.read(str(path), always_2d=True) | |
| waveform = torch.from_numpy(audio.T).to(dtype=torch.float32) | |
| return waveform, int(sample_rate) | |
| def detect_platform() -> str: | |
| return platform.system().lower() | |
| def native_playback_command(audio_path: str | Path) -> list[str] | None: | |
| resolved = str(Path(audio_path)) | |
| system = detect_platform() | |
| if system == "windows": | |
| return None | |
| if system == "darwin" and shutil.which("afplay"): | |
| return ["afplay", resolved] | |
| if system == "linux": | |
| for cmd in ("aplay", "paplay", "ffplay", "xdg-open"): | |
| if shutil.which(cmd): | |
| if cmd == "ffplay": | |
| return [cmd, "-nodisp", "-autoexit", resolved] | |
| return [cmd, resolved] | |
| return None | |
| def play_audio_file(audio_path: str | Path, *, block: bool = True) -> bool: | |
| resolved = Path(audio_path) | |
| if not resolved.exists(): | |
| return False | |
| system = detect_platform() | |
| if system == "windows": | |
| if winsound is None: | |
| return False | |
| flags = winsound.SND_FILENAME | |
| if not block: | |
| flags |= winsound.SND_ASYNC | |
| winsound.PlaySound(str(resolved), flags) | |
| return True | |
| command = native_playback_command(resolved) | |
| if command is None: | |
| return False | |
| if block: | |
| subprocess.run(command, check=False) | |
| else: | |
| subprocess.Popen(command) | |
| return True | |