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 GPUburnout/GPUburnout-3B-75K-Chat:Q4_K_M# Run inference directly in the terminal:
llama cli -hf GPUburnout/GPUburnout-3B-75K-Chat: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 GPUburnout/GPUburnout-3B-75K-Chat:Q4_K_M# Run inference directly in the terminal:
./llama-cli -hf GPUburnout/GPUburnout-3B-75K-Chat: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 GPUburnout/GPUburnout-3B-75K-Chat:Q4_K_M# Run inference directly in the terminal:
./build/bin/llama-cli -hf GPUburnout/GPUburnout-3B-75K-Chat:Q4_K_MUse Docker
docker model run hf.co/GPUburnout/GPUburnout-3B-75K-Chat:Q4_K_MQuick Links
GPUburnout-3B-75K-Chat (RETIRED)
This model is retired. It was trained in April 2026 with a buggy SFT data formatter
(plain string concatenation instead of apply_chat_template), which caused loop collapse
at inference and made it underperform the smaller 2B chat model on most benchmarks.
Use GPUburnout/GPUburnout-3B-75K-Chat-v2 instead.
Full postmortem: It Took Me Two Weeks to Read My Own Code
This repo is kept as a historical artifact, not for production use.
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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf GPUburnout/GPUburnout-3B-75K-Chat:Q4_K_M# Run inference directly in the terminal: llama cli -hf GPUburnout/GPUburnout-3B-75K-Chat:Q4_K_M