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
| """ | |
| Memory Optimization Module Test | |
| Quick test to verify all components work correctly. | |
| """ | |
| import torch | |
| import sys | |
| import os | |
| # Add parent directory to path | |
| sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) | |
| from memory_optimization import ( | |
| UnifiedMemoryManager, | |
| MemoryOptimizationConfig, | |
| get_shared_model, | |
| get_tensor, | |
| return_tensor, | |
| get_memory_stats, | |
| clear_memory | |
| ) | |
| def test_memory_optimization(): | |
| """Test memory optimization module""" | |
| print("=" * 70) | |
| print("Testing Memory Optimization Module") | |
| print("=" * 70) | |
| # Initialize config | |
| config = MemoryOptimizationConfig( | |
| use_shared_model=True, | |
| device="cuda" if torch.cuda.is_available() else "cpu" | |
| ) | |
| print(f"\n[CONFIG] Device: {config.device}") | |
| print(f"[CONFIG] Shared Model: {config.use_shared_model}") | |
| # Initialize manager | |
| manager = UnifiedMemoryManager(config) | |
| print("\n[OK] UnifiedMemoryManager initialized") | |
| # Test tensor pooling | |
| print("\n[TEST] Tensor Pooling...") | |
| tensor1 = manager.get_tensor((10, 1024), dtype=torch.float32) | |
| print(f" [OK] Created tensor: {tensor1.shape}, device: {tensor1.device}") | |
| manager.return_tensor(tensor1) | |
| print(" [OK] Returned tensor to pool") | |
| tensor2 = manager.get_tensor((10, 1024), dtype=torch.float32) | |
| print(f" [OK] Retrieved tensor from pool: {tensor2.shape}") | |
| # Test shared model (if available) | |
| print("\n[TEST] Shared Model...") | |
| try: | |
| # This will use shared Qwen model if available | |
| model = manager.get_shared_model("Qwen/Qwen3-0.6B", "transformer") | |
| print(f" [OK] Got shared model: {type(model).__name__}") | |
| except Exception as e: | |
| print(f" [WARN] Could not get shared model: {e}") | |
| # Test memory stats | |
| print("\n[TEST] Memory Stats...") | |
| stats = manager.get_memory_stats() | |
| print(f" [OK] Got memory stats: {len(stats)} categories") | |
| # Test cleanup | |
| print("\n[TEST] Memory Cleanup...") | |
| manager.clear_all_memory() | |
| print(" [OK] Memory cleared") | |
| print("\n" + "=" * 70) | |
| print("[SUCCESS] All tests passed!") | |
| print("=" * 70) | |
| if __name__ == "__main__": | |
| test_memory_optimization() | |