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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	---
	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)
	```python
	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
	```bash
	# 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
	```bash
	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

	```bibtex
	@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}
	}
	```

	---

	Built with ❤️ for sovereign AI infrastructure
	[Discord](https://discord.gg/clawd) · [GitHub](https://github.com/my-ai-stack/Stack-3.0) · [Website](https://www.stack-ai.me)