48 GB VRAM
Collection
Quants that run fast on 2x3090 consuming 48GB total VRAM. • 2 items • Updated
How to use TeeZee/Reflection-Llama-3.1-70B-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf TeeZee/Reflection-Llama-3.1-70B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf TeeZee/Reflection-Llama-3.1-70B-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf TeeZee/Reflection-Llama-3.1-70B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf TeeZee/Reflection-Llama-3.1-70B-GGUF:Q4_K_M
# 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 TeeZee/Reflection-Llama-3.1-70B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf TeeZee/Reflection-Llama-3.1-70B-GGUF:Q4_K_M
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 TeeZee/Reflection-Llama-3.1-70B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf TeeZee/Reflection-Llama-3.1-70B-GGUF:Q4_K_M
docker model run hf.co/TeeZee/Reflection-Llama-3.1-70B-GGUF:Q4_K_M
How to use TeeZee/Reflection-Llama-3.1-70B-GGUF with Ollama:
ollama run hf.co/TeeZee/Reflection-Llama-3.1-70B-GGUF:Q4_K_M
How to use TeeZee/Reflection-Llama-3.1-70B-GGUF with Docker Model Runner:
docker model run hf.co/TeeZee/Reflection-Llama-3.1-70B-GGUF:Q4_K_M
How to use TeeZee/Reflection-Llama-3.1-70B-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull TeeZee/Reflection-Llama-3.1-70B-GGUF:Q4_K_M
lemonade run user.Reflection-Llama-3.1-70B-GGUF-Q4_K_M
lemonade list
Q4_K_M GGUF quant of Reflection-Llama-3.1-70B - fixed version.
Runs great on 48GB VRAM, tested.
Ollama modelfile added - version with original system prompt - output is split into "thinking" and "output" tags.
If you want llama 3.1 'vanilla' experience, just remove SYSTEM from modelfile before creating ollama model.
All comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel:

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