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 argparse | |
| import json | |
| from pathlib import Path | |
| def build_parser() -> argparse.ArgumentParser: | |
| parser = argparse.ArgumentParser(description="Phill Swarm Audio CLI") | |
| parser.add_argument("--root-dir", type=str, default=str(Path(__file__).resolve().parent)) | |
| subparsers = parser.add_subparsers(dest="command", required=True) | |
| subparsers.add_parser("indicators", help="Show Audio runtime and terminal indicators") | |
| synth = subparsers.add_parser("synthesize", help="Generate speech to a wav file") | |
| synth.add_argument("--text", required=True) | |
| synth.add_argument("--output", required=True) | |
| synth.add_argument("--language", default=None) | |
| synth.add_argument("--ref-audio", default=None) | |
| synth.add_argument("--ref-text", default=None) | |
| synth.add_argument("--instruct", default=None) | |
| synth.add_argument("--duration", type=float, default=None) | |
| synth.add_argument("--speed", type=float, default=None) | |
| synth.add_argument("--num-step", type=int, default=8) | |
| synth.add_argument("--guidance-scale", type=float, default=1.5) | |
| synth.add_argument("--denoise", action="store_true") | |
| speak = subparsers.add_parser("speak", help="Generate speech and try native OS playback") | |
| speak.add_argument("--text", required=True) | |
| speak.add_argument("--output", required=True) | |
| speak.add_argument("--language", default=None) | |
| speak.add_argument("--ref-audio", default=None) | |
| speak.add_argument("--ref-text", default=None) | |
| speak.add_argument("--instruct", default=None) | |
| speak.add_argument("--duration", type=float, default=None) | |
| speak.add_argument("--speed", type=float, default=None) | |
| speak.add_argument("--num-step", type=int, default=8) | |
| speak.add_argument("--guidance-scale", type=float, default=1.5) | |
| speak.add_argument("--denoise", action="store_true") | |
| speak.add_argument("--non-blocking", action="store_true") | |
| transcribe = subparsers.add_parser("transcribe", help="Transcribe an audio file") | |
| transcribe.add_argument("--input", required=True) | |
| return parser | |
| def main() -> None: | |
| args = build_parser().parse_args() | |
| if args.command == "synthesize": | |
| from Audio.Pipeline import load_audio_pipeline | |
| pipeline = load_audio_pipeline(args.root_dir) | |
| output = pipeline.synthesize_to_file( | |
| text=args.text, | |
| output_path=args.output, | |
| language=args.language, | |
| ref_audio=args.ref_audio, | |
| ref_text=args.ref_text, | |
| instruct=args.instruct, | |
| duration=args.duration, | |
| speed=args.speed, | |
| num_step=args.num_step, | |
| guidance_scale=args.guidance_scale, | |
| denoise=args.denoise, | |
| ) | |
| print(output) | |
| return | |
| if args.command == "speak": | |
| from Audio.Pipeline import load_audio_pipeline | |
| pipeline = load_audio_pipeline(args.root_dir) | |
| output, played = pipeline.speak( | |
| text=args.text, | |
| output_path=args.output, | |
| language=args.language, | |
| ref_audio=args.ref_audio, | |
| ref_text=args.ref_text, | |
| instruct=args.instruct, | |
| duration=args.duration, | |
| speed=args.speed, | |
| num_step=args.num_step, | |
| guidance_scale=args.guidance_scale, | |
| denoise=args.denoise, | |
| block=not args.non_blocking, | |
| ) | |
| print({"output": str(output), "played": played}) | |
| return | |
| if args.command == "transcribe": | |
| from Audio.Pipeline import load_audio_pipeline | |
| pipeline = load_audio_pipeline(args.root_dir) | |
| print(pipeline.transcribe_file(args.input)) | |
| return | |
| if args.command == "indicators": | |
| from Audio.indicators import build_audio_indicator_payload | |
| print(json.dumps(build_audio_indicator_payload(args.root_dir), indent=2)) | |
| if __name__ == "__main__": | |
| main() | |