Instructions to use DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF", filename="nvidia.Llama-3_1-Nemotron-Ultra-253B-v1.IQ1_M-00001-of-00005.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 DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF:Q4_K_M
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 DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF:Q4_K_M
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 DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF:Q4_K_M
- Ollama
How to use DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF with Ollama:
ollama run hf.co/DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF:Q4_K_M
- Unsloth Studio
How to use DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF 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 DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF 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 DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF with Docker Model Runner:
docker model run hf.co/DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF:Q4_K_M
- Lemonade
How to use DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull DevQuasar/nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.nvidia.Llama-3_1-Nemotron-Ultra-253B-v1-GGUF-Q4_K_M
List all available models
lemonade list
Big thanks to ymcki for updating the llama.cpp code to support the 'dummy' layers. Use the llama.cpp branch from this PR: https://github.com/ggml-org/llama.cpp/pull/12843 if it hasn't been merged yet.
Note the imatrix data used for the IQ quants has been produced from the Q4 quant!
'Make knowledge free for everyone'
Quantized version of: nvidia/Llama-3_1-Nemotron-Ultra-253B-v1
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