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 hashlib | |
| import json | |
| from dataclasses import dataclass | |
| from pathlib import Path | |
| from typing import Any | |
| from PIL import Image | |
| from .storyboard import VideoStoryboard | |
| class KeyframeResult: | |
| frames: list[Image.Image] | |
| paths: list[Path] | |
| cache_hits: int | |
| metadata: list[dict[str, Any]] | |
| def cache_key(payload: dict[str, Any]) -> str: | |
| raw = json.dumps(payload, sort_keys=True, ensure_ascii=True) | |
| return hashlib.sha256(raw.encode("utf-8")).hexdigest()[:24] | |
| def _json_safe(value: Any) -> Any: | |
| if isinstance(value, (str, int, float, bool)) or value is None: | |
| return value | |
| if isinstance(value, Path): | |
| return str(value) | |
| if isinstance(value, (list, tuple)): | |
| return [_json_safe(item) for item in value] | |
| if isinstance(value, dict): | |
| return {str(key): _json_safe(item) for key, item in sorted(value.items(), key=lambda pair: str(pair[0]))} | |
| return repr(value) | |
| def generate_keyframes( | |
| router: Any, | |
| storyboard: VideoStoryboard, | |
| *, | |
| cache_dir: str | Path, | |
| width: int, | |
| height: int, | |
| image_steps: int, | |
| guidance_scale: float, | |
| seed: int, | |
| motion: str, | |
| version: str, | |
| use_cache: bool = True, | |
| **kwargs: Any, | |
| ) -> KeyframeResult: | |
| root = Path(cache_dir) | |
| root.mkdir(parents=True, exist_ok=True) | |
| generation_strategy = kwargs.pop("generation_strategy", "diffusion") | |
| frames: list[Image.Image] = [] | |
| paths: list[Path] = [] | |
| metadata: list[dict[str, Any]] = [] | |
| cache_hits = 0 | |
| for idx, prompt in enumerate(storyboard.keyframe_prompts): | |
| image_kwargs = { | |
| "use_memory": False, | |
| "reference_pass_steps": 0, | |
| "unload_after_call": False, | |
| **kwargs, | |
| } | |
| payload = { | |
| "prompt": prompt, | |
| "negative": storyboard.negative_prompt, | |
| "width": width, | |
| "height": height, | |
| "steps": image_steps, | |
| "guidance_scale": guidance_scale, | |
| "seed": seed + idx, | |
| "motion": motion, | |
| "videogen_version": version, | |
| "image_options": { | |
| "generation_strategy": generation_strategy, | |
| **{str(key): _json_safe(value) for key, value in sorted(image_kwargs.items(), key=lambda pair: str(pair[0]))}, | |
| }, | |
| } | |
| path = root / f"{cache_key(payload)}.png" | |
| if use_cache and path.exists(): | |
| frame = Image.open(path).convert("RGB") | |
| cache_hits += 1 | |
| route_meta = {"cache_hit": True, "saved_path": str(path)} | |
| else: | |
| result = router.generate_image( | |
| prompt=prompt, | |
| height=height, | |
| width=width, | |
| steps=image_steps, | |
| guidance_scale=guidance_scale, | |
| seed=seed + idx, | |
| generation_strategy=generation_strategy, | |
| **image_kwargs, | |
| ) | |
| frame = result.payload.images[0].convert("RGB") | |
| frame.save(path) | |
| route_meta = dict(getattr(result, "metadata", {})) | |
| route_meta["cache_hit"] = False | |
| route_meta["saved_path"] = str(path) | |
| frames.append(frame) | |
| paths.append(path) | |
| metadata.append(route_meta) | |
| return KeyframeResult(frames=frames, paths=paths, cache_hits=cache_hits, metadata=metadata) | |