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 Stephen123456/code-1238-cur
# Run inference directly in the terminal:
llama cli -hf Stephen123456/code-1238-cur
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Stephen123456/code-1238-cur
# Run inference directly in the terminal:
llama cli -hf Stephen123456/code-1238-cur
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 Stephen123456/code-1238-cur
# Run inference directly in the terminal:
./llama-cli -hf Stephen123456/code-1238-cur
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 Stephen123456/code-1238-cur
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Stephen123456/code-1238-cur
Use Docker
docker model run hf.co/Stephen123456/code-1238-cur
Quick Links

code-1238-cur โ€” Microtensor Subnet 92

LoRA fine-tuned Qwen2.5-Coder-0.5B, exported q4_0 GGUF for code/mt-3g.

Field Value
Round 1238
Track code
Class mt-3g
Quant q4_0
Max input 900 tokens
Base Qwen/Qwen2.5-Coder-0.5B-Instruct@ea3f2471
Hotkey 5FjmWwbnPzVUYdFG8aDS14zdu2tzgMRmdtpdTmyaVBkAsGQD
Entrypoint model.gguf
Downloads last month
27
GGUF
Model size
0.5B params
Architecture
qwen2
Hardware compatibility
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