How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Artefact2/CodeLlama-70b-Instruct-hf-GGUF:# Run inference directly in the terminal:
llama-cli -hf Artefact2/CodeLlama-70b-Instruct-hf-GGUF: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 Artefact2/CodeLlama-70b-Instruct-hf-GGUF:# Run inference directly in the terminal:
./llama-cli -hf Artefact2/CodeLlama-70b-Instruct-hf-GGUF: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 Artefact2/CodeLlama-70b-Instruct-hf-GGUF:# Run inference directly in the terminal:
./build/bin/llama-cli -hf Artefact2/CodeLlama-70b-Instruct-hf-GGUF:Use Docker
docker model run hf.co/Artefact2/CodeLlama-70b-Instruct-hf-GGUF:Quick Links
These are GGUF quantized versions of codellama/CodeLlama-70b-Instruct-hf.
The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using wiki.train.raw.
The IQ2_XXS and IQ2_XS versions are compatible with llama.cpp, version 147b17a or later. The IQ3_XXS requires version f4d7e54 or later.
Some model files above 50GB are split into smaller files. To concatenate them, use the cat command (on Windows, use PowerShell): cat foo-Q6_K.gguf.* > foo-Q6_K.gguf
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Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf Artefact2/CodeLlama-70b-Instruct-hf-GGUF:# Run inference directly in the terminal: llama-cli -hf Artefact2/CodeLlama-70b-Instruct-hf-GGUF: