Instructions to use bartowski/Meta-Llama-3-70B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use bartowski/Meta-Llama-3-70B-Instruct-GGUF 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 bartowski/Meta-Llama-3-70B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf bartowski/Meta-Llama-3-70B-Instruct-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf bartowski/Meta-Llama-3-70B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf bartowski/Meta-Llama-3-70B-Instruct-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 bartowski/Meta-Llama-3-70B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bartowski/Meta-Llama-3-70B-Instruct-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 bartowski/Meta-Llama-3-70B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bartowski/Meta-Llama-3-70B-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bartowski/Meta-Llama-3-70B-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/Meta-Llama-3-70B-Instruct-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": "bartowski/Meta-Llama-3-70B-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF:Q4_K_M
- Ollama
How to use bartowski/Meta-Llama-3-70B-Instruct-GGUF with Ollama:
ollama run hf.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF:Q4_K_M
- Unsloth Studio
How to use bartowski/Meta-Llama-3-70B-Instruct-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 bartowski/Meta-Llama-3-70B-Instruct-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 bartowski/Meta-Llama-3-70B-Instruct-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bartowski/Meta-Llama-3-70B-Instruct-GGUF to start chatting
- Docker Model Runner
How to use bartowski/Meta-Llama-3-70B-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF:Q4_K_M
- Lemonade
How to use bartowski/Meta-Llama-3-70B-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bartowski/Meta-Llama-3-70B-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Meta-Llama-3-70B-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Is the Q8_0 quant also imatrix'd? Why?
What was the basis of the decision to use imatrix vs. regular quantization for Q8_0? Doesn't imatrix reduce performance?
It shouldn't reduce performance (unless you have a source on that) but it also should not affect it much if at all, since at Q8 there's no need to compress portions further than others
Edit: this is based on old knowledge, if you come across this the real answer is that Q8 quants completely disable the imatrix, as you can see here:
Was just pointed at this discussion - imatrix data is not used for Q8_0 quants, so the resulting quant will be essentially the same regardless of whether an imatrix is specified or not (the only thing that changes is the header values saying which imatrix file is used etc., not the actual model data). Q6_K is the highest-bpw tensor format that uses the imatrix data for quantisation.
Yeah this is a very old comment for me, I learned since then that in the code itself on Q8 it explicitly disabled the imatrix before doing the quantization
updated the comment in case others come across it