Text Generation
Transformers
Safetensors
GGUF
qwen2
code-generation
code-assistant
mixture-of-experts
multilingual
llama.cpp
ollama
conversational
Eval Results (legacy)
text-generation-inference
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
- Transformers
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", 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
- 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
- Pi
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
Walid Sobhi
Add benchmark results: results_mmlu_omni-nexus-alpha-q8_1777037079.json
3d69393 verified | { | |
| "mmlu_avg": 59.89175331149409, | |
| "correct": 8410, | |
| "total": 14042, | |
| "by_subject": { | |
| "abstract_algebra": { | |
| "correct": 33, | |
| "total": 100, | |
| "acc": 33.0 | |
| }, | |
| "anatomy": { | |
| "correct": 83, | |
| "total": 135, | |
| "acc": 61.48148148148148 | |
| }, | |
| "astronomy": { | |
| "correct": 108, | |
| "total": 152, | |
| "acc": 71.05263157894737 | |
| }, | |
| "business_ethics": { | |
| "correct": 68, | |
| "total": 100, | |
| "acc": 68.0 | |
| }, | |
| "clinical_knowledge": { | |
| "correct": 183, | |
| "total": 265, | |
| "acc": 69.05660377358491 | |
| }, | |
| "college_biology": { | |
| "correct": 103, | |
| "total": 144, | |
| "acc": 71.52777777777777 | |
| }, | |
| "college_chemistry": { | |
| "correct": 42, | |
| "total": 100, | |
| "acc": 42.0 | |
| }, | |
| "college_computer_science": { | |
| "correct": 55, | |
| "total": 100, | |
| "acc": 55.0 | |
| }, | |
| "college_mathematics": { | |
| "correct": 32, | |
| "total": 100, | |
| "acc": 32.0 | |
| }, | |
| "college_medicine": { | |
| "correct": 109, | |
| "total": 173, | |
| "acc": 63.005780346820806 | |
| }, | |
| "college_physics": { | |
| "correct": 51, | |
| "total": 102, | |
| "acc": 50.0 | |
| }, | |
| "computer_security": { | |
| "correct": 74, | |
| "total": 100, | |
| "acc": 74.0 | |
| }, | |
| "conceptual_physics": { | |
| "correct": 154, | |
| "total": 235, | |
| "acc": 65.53191489361703 | |
| }, | |
| "econometrics": { | |
| "correct": 36, | |
| "total": 114, | |
| "acc": 31.57894736842105 | |
| }, | |
| "electrical_engineering": { | |
| "correct": 85, | |
| "total": 145, | |
| "acc": 58.62068965517241 | |
| }, | |
| "elementary_mathematics": { | |
| "correct": 115, | |
| "total": 378, | |
| "acc": 30.423280423280424 | |
| }, | |
| "formal_logic": { | |
| "correct": 63, | |
| "total": 126, | |
| "acc": 50.0 | |
| }, | |
| "global_facts": { | |
| "correct": 32, | |
| "total": 100, | |
| "acc": 32.0 | |
| }, | |
| "high_school_biology": { | |
| "correct": 249, | |
| "total": 310, | |
| "acc": 80.3225806451613 | |
| }, | |
| "high_school_chemistry": { | |
| "correct": 118, | |
| "total": 203, | |
| "acc": 58.12807881773399 | |
| }, | |
| "high_school_computer_science": { | |
| "correct": 75, | |
| "total": 100, | |
| "acc": 75.0 | |
| }, | |
| "high_school_european_history": { | |
| "correct": 128, | |
| "total": 165, | |
| "acc": 77.57575757575758 | |
| }, | |
| "high_school_geography": { | |
| "correct": 158, | |
| "total": 198, | |
| "acc": 79.79797979797979 | |
| }, | |
| "high_school_government_and_politics": { | |
| "correct": 161, | |
| "total": 193, | |
| "acc": 83.41968911917098 | |
| }, | |
| "high_school_macroeconomics": { | |
| "correct": 258, | |
| "total": 390, | |
| "acc": 66.15384615384616 | |
| }, | |
| "high_school_mathematics": { | |
| "correct": 26, | |
| "total": 270, | |
| "acc": 9.62962962962963 | |
| }, | |
| "high_school_microeconomics": { | |
| "correct": 175, | |
| "total": 238, | |
| "acc": 73.52941176470588 | |
| }, | |
| "high_school_physics": { | |
| "correct": 71, | |
| "total": 151, | |
| "acc": 47.019867549668874 | |
| }, | |
| "high_school_psychology": { | |
| "correct": 458, | |
| "total": 545, | |
| "acc": 84.03669724770643 | |
| }, | |
| "high_school_statistics": { | |
| "correct": 110, | |
| "total": 216, | |
| "acc": 50.925925925925924 | |
| }, | |
| "high_school_us_history": { | |
| "correct": 167, | |
| "total": 204, | |
| "acc": 81.86274509803921 | |
| }, | |
| "high_school_world_history": { | |
| "correct": 185, | |
| "total": 237, | |
| "acc": 78.05907172995781 | |
| }, | |
| "human_aging": { | |
| "correct": 154, | |
| "total": 223, | |
| "acc": 69.05829596412556 | |
| }, | |
| "human_sexuality": { | |
| "correct": 94, | |
| "total": 131, | |
| "acc": 71.7557251908397 | |
| }, | |
| "international_law": { | |
| "correct": 82, | |
| "total": 121, | |
| "acc": 67.76859504132231 | |
| }, | |
| "jurisprudence": { | |
| "correct": 79, | |
| "total": 108, | |
| "acc": 73.14814814814815 | |
| }, | |
| "logical_fallacies": { | |
| "correct": 125, | |
| "total": 163, | |
| "acc": 76.68711656441718 | |
| }, | |
| "machine_learning": { | |
| "correct": 55, | |
| "total": 112, | |
| "acc": 49.107142857142854 | |
| }, | |
| "management": { | |
| "correct": 81, | |
| "total": 103, | |
| "acc": 78.64077669902913 | |
| }, | |
| "marketing": { | |
| "correct": 203, | |
| "total": 234, | |
| "acc": 86.75213675213675 | |
| }, | |
| "medical_genetics": { | |
| "correct": 75, | |
| "total": 100, | |
| "acc": 75.0 | |
| }, | |
| "miscellaneous": { | |
| "correct": 616, | |
| "total": 783, | |
| "acc": 78.67177522349937 | |
| }, | |
| "moral_disputes": { | |
| "correct": 223, | |
| "total": 346, | |
| "acc": 64.45086705202313 | |
| }, | |
| "moral_scenarios": { | |
| "correct": 217, | |
| "total": 895, | |
| "acc": 24.24581005586592 | |
| }, | |
| "nutrition": { | |
| "correct": 200, | |
| "total": 306, | |
| "acc": 65.359477124183 | |
| }, | |
| "philosophy": { | |
| "correct": 217, | |
| "total": 311, | |
| "acc": 69.7749196141479 | |
| }, | |
| "prehistory": { | |
| "correct": 230, | |
| "total": 324, | |
| "acc": 70.98765432098766 | |
| }, | |
| "professional_accounting": { | |
| "correct": 111, | |
| "total": 282, | |
| "acc": 39.361702127659576 | |
| }, | |
| "professional_law": { | |
| "correct": 598, | |
| "total": 1534, | |
| "acc": 38.983050847457626 | |
| }, | |
| "professional_medicine": { | |
| "correct": 196, | |
| "total": 272, | |
| "acc": 72.05882352941177 | |
| }, | |
| "professional_psychology": { | |
| "correct": 388, | |
| "total": 612, | |
| "acc": 63.39869281045752 | |
| }, | |
| "public_relations": { | |
| "correct": 69, | |
| "total": 110, | |
| "acc": 62.72727272727273 | |
| }, | |
| "security_studies": { | |
| "correct": 162, | |
| "total": 245, | |
| "acc": 66.12244897959184 | |
| }, | |
| "sociology": { | |
| "correct": 168, | |
| "total": 201, | |
| "acc": 83.58208955223881 | |
| }, | |
| "us_foreign_policy": { | |
| "correct": 76, | |
| "total": 100, | |
| "acc": 76.0 | |
| }, | |
| "virology": { | |
| "correct": 84, | |
| "total": 166, | |
| "acc": 50.602409638554214 | |
| }, | |
| "world_religions": { | |
| "correct": 142, | |
| "total": 171, | |
| "acc": 83.04093567251462 | |
| } | |
| } | |
| } |