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 Settings
- 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
| import os | |
| import subprocess | |
| import json | |
| from pathlib import Path | |
| def validate_wrench_suite(): | |
| print("=== GLOBAL WRENCH RUNTIME VALIDATOR ===") | |
| # Dynamic path detection for project root | |
| tools_dir = Path(__file__).resolve().parent | |
| wrench_dir = tools_dir / "Wrench" | |
| if not wrench_dir.exists(): | |
| print(f"Error: Wrench directory not found at {wrench_dir}") | |
| return | |
| tools = [f for f in os.listdir(wrench_dir) if f.endswith(".ts") and not f.endswith(".test.ts") and f not in ["tools.ts", "tool-registry.ts", "tool-names.ts", "tool-error.ts", "constants.ts", "modifiable-tool.ts", "mcp-tool.ts"]] | |
| results = {} | |
| for tool_file in tools: | |
| tool_name = tool_file.replace(".ts", "") | |
| print(f"Checking {tool_name}...", end=" ", flush=True) | |
| # Test bootstrap by trying to import it in a small TS harness | |
| # cmd = f"npx tsx -e \"import './Wrench/{tool_file}'; console.log('LOAD_OK')\"" # Cross-platform tweak | |
| cmd = f"npx tsx -e \"import './Wrench/{tool_file}'; console.log('LOAD_OK')\"" | |
| try: | |
| res = subprocess.run(cmd, cwd=str(tools_dir), capture_output=True, text=True, timeout=20, shell=True) | |
| if "LOAD_OK" in res.stdout: | |
| print("PASSED") | |
| results[tool_name] = "PASSED" | |
| else: | |
| print("FAILED") | |
| # print(res.stderr) | |
| results[tool_name] = "FAILED: " + res.stderr.split("\n")[0] | |
| except Exception as e: | |
| print(f"ERROR: {str(e)}") | |
| results[tool_name] = "ERROR" | |
| print("\n" + "="*40) | |
| print("SUMMARY") | |
| print("="*40) | |
| for t, r in results.items(): | |
| print(f"{t:20}: {r}") | |
| if __name__ == "__main__": | |
| validate_wrench_suite() | |