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
| """ | |
| Command Line Interface for AGI Memory System | |
| """ | |
| import argparse | |
| import sys | |
| from .core import AGIMemorySystem, AGIMemoryConfig | |
| def main(): | |
| parser = argparse.ArgumentParser(description="AGI Memory System CLI") | |
| parser.add_argument("--model", type=str, default="Qwen/Qwen3-0.6B", help="Model name") | |
| parser.add_argument("--device", type=str, default="cuda", help="Device (cuda/cpu)") | |
| parser.add_argument("--capacity", type=int, default=10000, help="Memory capacity") | |
| args = parser.parse_args() | |
| print("Initializing AGI Memory System...") | |
| config = AGIMemoryConfig( | |
| shared_model_name=args.model, | |
| device=args.device, | |
| memory_graph_capacity=args.capacity | |
| ) | |
| memory = AGIMemorySystem(config) | |
| print("AGI Memory System initialized successfully!") | |
| print(f"Memory stats: {memory.get_stats()}") | |
| return 0 | |
| if __name__ == "__main__": | |
| sys.exit(main()) | |