Instructions to use ayjays132/PHILLNET-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ayjays132/PHILLNET-1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ayjays132/PHILLNET-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ayjays132/PHILLNET-1")
model = AutoModelForCausalLM.from_pretrained("ayjays132/PHILLNET-1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ayjays132/PHILLNET-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ayjays132/PHILLNET-1"
# 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-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ayjays132/PHILLNET-1

SGLang

How to use ayjays132/PHILLNET-1 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-1" \
    --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-1",
		"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-1" \
        --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-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ayjays132/PHILLNET-1 with Docker Model Runner:
```
docker model run hf.co/ayjays132/PHILLNET-1
```

ayjays132 commited on Apr 5, 2025

Commit

8fefcae

verified ·

1 Parent(s): e5af5ea

Update README.md

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Files changed (1) hide show

README.md +6 -6

README.md CHANGED Viewed

@@ -205,7 +205,7 @@ h1, h2, h3 {
       <p>
         The specialized MoE layer fuses memories, plans, and meaning to direct routing decisions with unparalleled context-awareness.
       </p>
-      <img src="https://huggingface.co/ayjays132/PHILLNET-1/blob/main/Images/Model_Overview.png?download=true" alt="Model Overview" style="width:100%; border-radius: 15px;">
     </div>
   </div>
@@ -220,7 +220,7 @@ h1, h2, h3 {
         <li><strong>MoE Tailoring:</strong> Uses 16 experts with top-4 routing, supported by gating noise and load balancing losses.</li>
         <li>Enhances token-level routing with abstract conceptual guidance.</li>
       </ul>
-      <img src="https://huggingface.co/ayjays132/phillnet/resolve/main/Integration.png?download=true" alt="Concept Integration" style="width:100%; border-radius: 15px;">
     </div>
   </div>
@@ -236,7 +236,7 @@ h1, h2, h3 {
         <li>Mechanisms such as fitness scoring, habitat specialization, memory-based adaptation, and ecosystem dynamics drive continuous neuroevolution.</li>
         <li>The network reshapes its neuron topology based on complexity metrics and performance trends – effectively rewriting its own body as it learns.</li>
       </ul>
-      <img src="https://huggingface.co/ayjays132/phillnet/resolve/main/Hyperameters.png?download=true" alt="Hyperparameters Overview" style="width:100%; border-radius: 15px;">
     </div>
   </div>
@@ -311,7 +311,7 @@ h1, h2, h3 {
   <div class="section">
     <h2 class="section-title">🔗 Seamless Integration with Hugging Face</h2>
     <div class="section-content">
-      <img src="https://huggingface.co/ayjays132/phillnet/resolve/main/Phillnet.png?download=true" alt="PhillNet 1 Model" style="width:100%; border-radius: 15px;">
       <p>
         Load PhillNet 1 easily with the following script:
       </p>
@@ -351,14 +351,14 @@ print("Generated Response:", generated_response)
       <p>
         Welcome to a new era of AI—where every parameter evolves, every neuron thinks, and every token is a step toward true general intelligence.
       </p>
-      <img src="https://huggingface.co/ayjays132/phillnet/resolve/main/Integration.png?download=true" alt="Integration Overview" style="width:100%; border-radius: 15px;">
     </div>
   </div>
   <div class="section">
     <h2 class="section-title">📜 Usage and License</h2>
     <div class="section-content">
-      <img src="https://huggingface.co/ayjays132/phillnet/resolve/main/usage.png?download=true" alt="Usage Example" style="width:100%; border-radius: 15px;">
       <p>
         If you use PhillNet 1, please provide credit to the original author, Phillip Holland, and review the LICENSE.md for usage guidelines. Your acknowledgement fosters ethical and responsible AI development.
       </p>

       <p>
         The specialized MoE layer fuses memories, plans, and meaning to direct routing decisions with unparalleled context-awareness.
       </p>
+      <img src="https://huggingface.co/ayjays132/PHILLNET-1/resolve/main/Images/Model_Overview.png?download=true" alt="Model Overview" style="width:100%; border-radius: 15px;">
     </div>
   </div>
         <li><strong>MoE Tailoring:</strong> Uses 16 experts with top-4 routing, supported by gating noise and load balancing losses.</li>
         <li>Enhances token-level routing with abstract conceptual guidance.</li>
       </ul>
+      <img src="https://huggingface.co/ayjays132/PHILLNET-1/resolve/main/Images/Integration.png?download=true" alt="Concept Integration" style="width:100%; border-radius: 15px;">
     </div>
   </div>
         <li>Mechanisms such as fitness scoring, habitat specialization, memory-based adaptation, and ecosystem dynamics drive continuous neuroevolution.</li>
         <li>The network reshapes its neuron topology based on complexity metrics and performance trends – effectively rewriting its own body as it learns.</li>
       </ul>
+      <img src="https://huggingface.co/ayjays132/PHILLNET-1/resolve/main/Images/Hyperameters.png?download=true" alt="Hyperparameters Overview" style="width:100%; border-radius: 15px;">
     </div>
   </div>
   <div class="section">
     <h2 class="section-title">🔗 Seamless Integration with Hugging Face</h2>
     <div class="section-content">
+      <img src="https://huggingface.co/ayjays132/PHILLNET-1/resolve/main/Images/Phillnet.png?download=true" alt="PhillNet 1 Model" style="width:100%; border-radius: 15px;">
       <p>
         Load PhillNet 1 easily with the following script:
       </p>
       <p>
         Welcome to a new era of AI—where every parameter evolves, every neuron thinks, and every token is a step toward true general intelligence.
       </p>
+      <img src="https://huggingface.co/ayjays132/PHILLNET-1/resolve/main/Images/Integration.png?download=true" alt="Integration Overview" style="width:100%; border-radius: 15px;">
     </div>
   </div>
   <div class="section">
     <h2 class="section-title">📜 Usage and License</h2>
     <div class="section-content">
+      <img src="https://huggingface.co/ayjays132/PHILLNET-1/resolve/main/Images/usage.png?download=true" alt="Usage Example" style="width:100%; border-radius: 15px;">
       <p>
         If you use PhillNet 1, please provide credit to the original author, Phillip Holland, and review the LICENSE.md for usage guidelines. Your acknowledgement fosters ethical and responsible AI development.
       </p>