Instructions to use Subject-Emu-5259/NeuralAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use Subject-Emu-5259/NeuralAI with PEFT:
```
Base model is not found.
```

How to use Subject-Emu-5259/NeuralAI with Transformers:

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

pipe = pipeline("text-generation", model="Subject-Emu-5259/NeuralAI", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Subject-Emu-5259/NeuralAI")
model = AutoModelForCausalLM.from_pretrained("Subject-Emu-5259/NeuralAI", device_map="auto")
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 Subject-Emu-5259/NeuralAI with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Subject-Emu-5259/NeuralAI"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Subject-Emu-5259/NeuralAI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Subject-Emu-5259/NeuralAI

SGLang

How to use Subject-Emu-5259/NeuralAI 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 "Subject-Emu-5259/NeuralAI" \
    --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": "Subject-Emu-5259/NeuralAI",
		"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 "Subject-Emu-5259/NeuralAI" \
        --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": "Subject-Emu-5259/NeuralAI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Subject-Emu-5259/NeuralAI with Docker Model Runner:
```
docker model run hf.co/Subject-Emu-5259/NeuralAI
```

NeuralAI / docs /VIBE_STACK.md

Subject-Emu-5259

Push NeuralAI project files - training data, scripts, services, knowledge base

38b4eff verified 9 days ago

preview code

Raw

History Blame

1.36 kB

	# NeuralAI Vibe Stack: Generative Multi-Modal Intelligence

	The Vibe Stack is the integration of high-velocity reasoning (NeuralAI LLM) and visual synthesis (NeuralAI Diffusion). This combination allows the Founder to move from concept to asset in a single flow.

	## 🎨 Generative Pillars

	### 1. Custom UI Mockups
	- Prompt: "Generate a wireframe for a Memphis-themed fintech app."
	- Logic: NeuralAI Diffusion denoises noise into structured wireframes, providing immediate visual feedback for application design.

	### 2. Branding Assets
	- Prompt: "Create a logo for Harris Holdings with a Pegasus and a Memphis sunset vibe."
	- Logic: High-fidelity asset generation for branding, social media, and product identity.

	### 3. Visual Logic Maps
	- Prompt: "Map out the architecture of the NeuralAI core service."
	- Logic: Turning complex backend logic into easy-to-understand visual diagrams.

	## 🚀 Technical Architecture
	- Engine: Latent Diffusion UNet (Beta)
	- Pipeline: Prompt $\rightarrow$ CLIP Encoding $\rightarrow$ Diffusion Denoising $\rightarrow$ VAE Decoding
	- Hardware: CPU Optimized (Inference Phase)

	## 🗺️ Scaling Roadmap
	- [x] Toy Proof of Concept (MNIST Denoising)
	- [x] Backend Integration (API Layer)
	- [ ] VAE Implementation for High-Resolution Outputs
	- [ ] Fine-tuning on Harris Holdings Branding Data