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 zerolimites/Qwen3-0.6B-Base-Normal:F16
# Run inference directly in the terminal:
llama cli -hf zerolimites/Qwen3-0.6B-Base-Normal:F16
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf zerolimites/Qwen3-0.6B-Base-Normal:F16
# Run inference directly in the terminal:
llama cli -hf zerolimites/Qwen3-0.6B-Base-Normal:F16
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf zerolimites/Qwen3-0.6B-Base-Normal:F16
# Run inference directly in the terminal:
./build/bin/llama cli -hf zerolimites/Qwen3-0.6B-Base-Normal:F16
Use Docker
docker model run hf.co/zerolimites/Qwen3-0.6B-Base-Normal:F16
Quick Links

πŸ€– Qwen3-0.6B-Base-Normal (Safetensors + GGUF Q8_0 & F16 β€” Modelo Base de ReferΓͺncia)

Este repositΓ³rio contΓ©m o modelo Qwen/Qwen3-0.6B Normal (Sem EdiΓ§Γ£o de Pesos) exportado tanto em formato Hugging Face (model.safetensors) quanto em GGUF (Q8_0 8-bit e F16 16-bit), junto com os resultados de referΓͺncia (baseline) nos 3 benchmarks:

  1. Benchmark PT-BR ⇄ EN Professor (28 tarefas): 0/28 (0.00%) -> Veja zerolimites/Qwen3-0.6B-PTBR-EN-Professor (100.00%)
  2. Benchmark ExecutΓ‘vel de CΓ³digo (28 problemas): 13/28 (46.43%) -> Veja zerolimites/Qwen3-0.6B-Code-Surgery-v2 (96.43%)
  3. Benchmark de MatemΓ‘tica (30 problemas): 13/30 (43.33%) -> Veja zerolimites/Qwen3-0.6B-Math-Surgery-v2 (76.67%)
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