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 deepforce/deepforce-coder-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf deepforce/deepforce-coder-v1:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf deepforce/deepforce-coder-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf deepforce/deepforce-coder-v1:Q4_K_M
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 deepforce/deepforce-coder-v1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf deepforce/deepforce-coder-v1:Q4_K_M
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 deepforce/deepforce-coder-v1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf deepforce/deepforce-coder-v1:Q4_K_M
Use Docker
docker model run hf.co/deepforce/deepforce-coder-v1:Q4_K_M
Quick Links

DeepForce Coder v1

⚠️ Note: v1 has known issues with simple Apex generation due to adapter weight loss during training. v2 is currently in development with full retraining and will be significantly improved. Use v2 when available: coming soon.

Known Limitations in v1

  • Over-engineers simple Apex requests
  • Occasionally generates non-existent Apex APIs
  • Best used for complex generation, debug, review, and refactor tasks

v2 Coming Soon

Full retraining with verified adapter weights across all adapters.

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qwen2
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