Instructions to use ayjays132/Phillnet-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Phillnet-2 / ConsciousnessSystem /run_example.py

ayjays132

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	"""
	Quick script to run ConsciousnessSystem examples.
	"""

	import sys
	import os
	import io

	# Fix encoding for Windows
	if sys.platform == 'win32':
	try:
	sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace')
	sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8', errors='replace')
	except:
	pass

	# Add parent directory to path
	sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

	try:
	from ConsciousnessSystem.example_usage import (
	example_basic_consciousness,
	example_thought_analysis,
	example_memory_management,
	)
	except ImportError:
	# Fallback for direct execution
	sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
	from example_usage import (
	example_basic_consciousness,
	example_thought_analysis,
	example_memory_management,
	)

	if __name__ == "__main__":
	print("Starting ConsciousnessSystem Examples...")
	print("=" * 80)

	try:
	example_basic_consciousness()
	example_thought_analysis()
	example_memory_management()
	print("\nAll examples completed successfully!")
	except Exception as e:
	print(f"\nError: {e}")
	import traceback
	traceback.print_exc()