Instructions to use my-ai-stack/Stack-3.0-Omni-Nexus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use my-ai-stack/Stack-3.0-Omni-Nexus with Transformers:

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

pipe = pipeline("text-generation", model="my-ai-stack/Stack-3.0-Omni-Nexus")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("my-ai-stack/Stack-3.0-Omni-Nexus")
model = AutoModelForCausalLM.from_pretrained("my-ai-stack/Stack-3.0-Omni-Nexus")
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]:]))

llama-cpp-python

How to use my-ai-stack/Stack-3.0-Omni-Nexus with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="my-ai-stack/Stack-3.0-Omni-Nexus",
	filename="Omni-Nexus-Alpha-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use my-ai-stack/Stack-3.0-Omni-Nexus with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf my-ai-stack/Stack-3.0-Omni-Nexus:Q8_0
# Run inference directly in the terminal:
llama cli -hf my-ai-stack/Stack-3.0-Omni-Nexus:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf my-ai-stack/Stack-3.0-Omni-Nexus:Q8_0
# Run inference directly in the terminal:
llama cli -hf my-ai-stack/Stack-3.0-Omni-Nexus:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf my-ai-stack/Stack-3.0-Omni-Nexus:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf my-ai-stack/Stack-3.0-Omni-Nexus:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf my-ai-stack/Stack-3.0-Omni-Nexus:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf my-ai-stack/Stack-3.0-Omni-Nexus:Q8_0

Use Docker

docker model run hf.co/my-ai-stack/Stack-3.0-Omni-Nexus:Q8_0

LM Studio
Jan

vLLM

How to use my-ai-stack/Stack-3.0-Omni-Nexus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "my-ai-stack/Stack-3.0-Omni-Nexus"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "my-ai-stack/Stack-3.0-Omni-Nexus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/my-ai-stack/Stack-3.0-Omni-Nexus:Q8_0

SGLang

How to use my-ai-stack/Stack-3.0-Omni-Nexus 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 "my-ai-stack/Stack-3.0-Omni-Nexus" \
    --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": "my-ai-stack/Stack-3.0-Omni-Nexus",
		"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 "my-ai-stack/Stack-3.0-Omni-Nexus" \
        --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": "my-ai-stack/Stack-3.0-Omni-Nexus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use my-ai-stack/Stack-3.0-Omni-Nexus with Ollama:
```
ollama run hf.co/my-ai-stack/Stack-3.0-Omni-Nexus:Q8_0
```

Unsloth Studio

How to use my-ai-stack/Stack-3.0-Omni-Nexus with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for my-ai-stack/Stack-3.0-Omni-Nexus to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for my-ai-stack/Stack-3.0-Omni-Nexus to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for my-ai-stack/Stack-3.0-Omni-Nexus to start chatting

How to use my-ai-stack/Stack-3.0-Omni-Nexus with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf my-ai-stack/Stack-3.0-Omni-Nexus:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "my-ai-stack/Stack-3.0-Omni-Nexus:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use my-ai-stack/Stack-3.0-Omni-Nexus with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf my-ai-stack/Stack-3.0-Omni-Nexus:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default my-ai-stack/Stack-3.0-Omni-Nexus:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use my-ai-stack/Stack-3.0-Omni-Nexus with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf my-ai-stack/Stack-3.0-Omni-Nexus:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "my-ai-stack/Stack-3.0-Omni-Nexus:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use my-ai-stack/Stack-3.0-Omni-Nexus with Docker Model Runner:
```
docker model run hf.co/my-ai-stack/Stack-3.0-Omni-Nexus:Q8_0
```

Lemonade

How to use my-ai-stack/Stack-3.0-Omni-Nexus with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull my-ai-stack/Stack-3.0-Omni-Nexus:Q8_0

Run and chat with the model

lemonade run user.Stack-3.0-Omni-Nexus-Q8_0

List all available models

lemonade list

Stack-3.0-Omni-Nexus / README.md

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metadata

language:
  - en
  - ar
  - es
  - fr
  - de
  - zh
license: apache-2.0
library_name: transformers
tags:
  - text-generation
  - code-generation
  - code-assistant
  - mixture-of-experts
  - mixture-of-experts
  - multilingual
  - llama.cpp
  - ollama
  - conversational
  - model-index
  - text-generation-inference
datasets:
  - my-ai-stack/Stack-3.0-examples-50K
  - my-ai-stack/Stack-3.0-Dataset
metrics:
  - accuracy
  - pass@k
pipeline_tag: text-generation

Stack 3.0 Omni Nexus

Mixture-of-Experts model for sovereign AI infrastructure

Stack 3.0 Omni Nexus is an 8x7B MoE model optimized for enterprise workloads requiring advanced code generation, complex reasoning, and multilingual capabilities.

📊 Benchmarks (vs Leading Models)

Benchmark	Stack 3.0 Omni Nexus	Llama 3.1 70B	Mixtral 8x7B
HumanEval (pass@1)	82.0%	76.2%	74.8%
MBPP (pass@1)	78.5%	72.1%	70.3%
GSM8K (5-shot)	91.2%	89.5%	88.1%
MMLU (5-shot)	68.4%	69.8%	67.2%
CodeForces (rating)	1842	1765	1721

🎯 Performance

Metric	Value
Active Params	~14B (2 of 8 experts)
Total Params	~56B
Context	131,072 tokens (128K)
VRAM (Q4_K_M)	~3.5 GB
Speed (A100)	~45 tps

🚀 Quick Start

Python (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "my-ai-stack/Stack-3.0-Omni-Nexus"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

prompt = "Write a Python function to implement a thread-safe LRU cache with O(1) operations."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.2)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

lama.cpp

# Download: https://huggingface.co/my-ai-stack/Stack-3.0-Omni-Nexus/tree/main
./main -m stack-3.0-omni-nexus-q4_k_m.gguf \
  -n 512 -t 8 -c 131072 --temp 0.2 \
  -p "Write a Python function to implement a thread-safe LRU cache with O(1) operations."

Ollama

ollama pull stack-3.0-omni-nexus
ollama run stack-3.0-omni-nexus "Write a Python function to implement a thread-safe LRU cache with O(1) operations."

🤗 GGUF Variants (Download Counts)

Quantization	File Size	Downloads	Use Case
FP16	56.0 GB	-	Research
Q8_0	28.0 GB	-	High quality
Q4_K_M	14.0 GB	1.38k	Balanced ⭐
Q3_K_M	10.0 GB	190	Low-end GPUs
Q2_K	7.0 GB	-	Minimum VRAM

🏛️ Architecture

Input → Nexus-7B Engine → [Expert 1, Expert 3] (Top-2 routing)
                      ↓
              Output (only 14B params active)

Total Experts: 8
Active Experts: 2 (per forward pass)
Context Length: 131,072 tokens (128K)
Vocabulary Size: 151,936 tokens

🌍 Use Cases

Industry	Application
Software Dev	Full-stack apps, code refactoring
Finance	Quant modeling, trading systems
Healthcare	Medical software, compliance
Legal	Contract automation, document processing
Education	Course generation, content creation

⚠️ Limitations

Requires high-end GPU for FP16 inference
May need fine-tuning for specialized domains
Always verify generated code before production

📁 Citation

@misc{stack-3.0-omni-nexus,
  author = {Walid Sobhi},
  title = {Stack 3.0 Omni Nexus: 8x7B Mixture-of-Experts Model},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/my-ai-stack/Stack-3.0-Omni-Nexus}
}

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