How to use from
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 MicroFlare/microFlare-v1
# Run inference directly in the terminal:
llama cli -hf MicroFlare/microFlare-v1
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MicroFlare/microFlare-v1
# Run inference directly in the terminal:
llama cli -hf MicroFlare/microFlare-v1
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 MicroFlare/microFlare-v1
# Run inference directly in the terminal:
./llama-cli -hf MicroFlare/microFlare-v1
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 MicroFlare/microFlare-v1
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MicroFlare/microFlare-v1
Use Docker
docker model run hf.co/MicroFlare/microFlare-v1
Quick Links

microFlare v1

microFlare v1 is an asymmetrically quantized version of Qwen 3.6 35B-A3B. It is designed so it can be ran on low spec hardware, as low as a 4GB GPU + 16GB of system RAM utilizing CPU offloading of experts.

Revision A has a Perplexity score of 6.2761 +/- 0.03961

For more details, see my blog post.

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Model size
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Architecture
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