How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf grapevine-AI/Qwen2-72B-Instruct-GGUF:Q4_K_M# Run inference directly in the terminal:
llama cli -hf grapevine-AI/Qwen2-72B-Instruct-GGUF:Q4_K_MUse 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 grapevine-AI/Qwen2-72B-Instruct-GGUF:Q4_K_M# Run inference directly in the terminal:
./llama-cli -hf grapevine-AI/Qwen2-72B-Instruct-GGUF:Q4_K_MBuild 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 grapevine-AI/Qwen2-72B-Instruct-GGUF:Q4_K_M# Run inference directly in the terminal:
./build/bin/llama-cli -hf grapevine-AI/Qwen2-72B-Instruct-GGUF:Q4_K_MUse Docker
docker model run hf.co/grapevine-AI/Qwen2-72B-Instruct-GGUF:Q4_K_MQuick Links
What is this?
Qwen2-72B-InstructをGGUFフォーマットに変換したものです。
imatrix dataset
日本語能力を重視し、日本語が多量に含まれるTFMC/imatrix-dataset-for-japanese-llmデータセットを使用しました。
なお、計算リソースの関係上imatrixの算出においてはQ8_0量子化モデルを使用しました。
Chat template
<|im_start|>system
ここにSystem Promptを書きます。<|im_end|>
<|im_start|>user
ここにMessageを書きます。<|im_end|>
<|im_start|>assistant
Environment
Windows版llama.cpp-b3171および同時リリースのconvert_hf_to_gguf.pyを使用して量子化作業を実施しました。
License
Tongyi Qianwen LICENSE
Developer
Alibaba Cloud
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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf grapevine-AI/Qwen2-72B-Instruct-GGUF:Q4_K_M# Run inference directly in the terminal: llama cli -hf grapevine-AI/Qwen2-72B-Instruct-GGUF:Q4_K_M