Instructions to use etemiz/Llama-3.1-405B-Inst-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use etemiz/Llama-3.1-405B-Inst-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="etemiz/Llama-3.1-405B-Inst-GGUF", filename="llama-3.1-405b-IQ1_M-00001-of-00019.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use etemiz/Llama-3.1-405B-Inst-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf etemiz/Llama-3.1-405B-Inst-GGUF:IQ1_M # Run inference directly in the terminal: llama-cli -hf etemiz/Llama-3.1-405B-Inst-GGUF:IQ1_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf etemiz/Llama-3.1-405B-Inst-GGUF:IQ1_M # Run inference directly in the terminal: llama-cli -hf etemiz/Llama-3.1-405B-Inst-GGUF:IQ1_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 etemiz/Llama-3.1-405B-Inst-GGUF:IQ1_M # Run inference directly in the terminal: ./llama-cli -hf etemiz/Llama-3.1-405B-Inst-GGUF:IQ1_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 etemiz/Llama-3.1-405B-Inst-GGUF:IQ1_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf etemiz/Llama-3.1-405B-Inst-GGUF:IQ1_M
Use Docker
docker model run hf.co/etemiz/Llama-3.1-405B-Inst-GGUF:IQ1_M
- LM Studio
- Jan
- Ollama
How to use etemiz/Llama-3.1-405B-Inst-GGUF with Ollama:
ollama run hf.co/etemiz/Llama-3.1-405B-Inst-GGUF:IQ1_M
- Unsloth Studio
How to use etemiz/Llama-3.1-405B-Inst-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 etemiz/Llama-3.1-405B-Inst-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 etemiz/Llama-3.1-405B-Inst-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for etemiz/Llama-3.1-405B-Inst-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use etemiz/Llama-3.1-405B-Inst-GGUF with Docker Model Runner:
docker model run hf.co/etemiz/Llama-3.1-405B-Inst-GGUF:IQ1_M
- Lemonade
How to use etemiz/Llama-3.1-405B-Inst-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull etemiz/Llama-3.1-405B-Inst-GGUF:IQ1_M
Run and chat with the model
lemonade run user.Llama-3.1-405B-Inst-GGUF-IQ1_M
List all available models
lemonade list
Llama 3.1 405B Quants and llama.cpp versions that is used for quantization
- IQ1_S: 86.8 GB - b3459
- IQ1_M: 95.1 GB - b3459
- IQ2_XXS: 109.0 GB - b3459
- IQ3_XXS: 157.7 GB - b3484
Quantization from BF16 here: https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/
which is converted from Llama 3.1 405B: https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct
imatrix file https://huggingface.co/nisten/meta-405b-instruct-cpu-optimized-gguf/blob/main/405imatrix.dat
Lmk if you need bigger quants.
Sponsored by: https://pickabrain.ai
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