How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf henrik3/Qwen2.5-Coder-7B-Instruct-ServiceNow-v0.1:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf henrik3/Qwen2.5-Coder-7B-Instruct-ServiceNow-v0.1:Q5_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf henrik3/Qwen2.5-Coder-7B-Instruct-ServiceNow-v0.1:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf henrik3/Qwen2.5-Coder-7B-Instruct-ServiceNow-v0.1:Q5_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 henrik3/Qwen2.5-Coder-7B-Instruct-ServiceNow-v0.1:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf henrik3/Qwen2.5-Coder-7B-Instruct-ServiceNow-v0.1:Q5_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 henrik3/Qwen2.5-Coder-7B-Instruct-ServiceNow-v0.1:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf henrik3/Qwen2.5-Coder-7B-Instruct-ServiceNow-v0.1:Q5_K_M
Use Docker
docker model run hf.co/henrik3/Qwen2.5-Coder-7B-Instruct-ServiceNow-v0.1:Q5_K_M
Quick Links

Automated benchmark (due to time constrains)

This benchmark compares the Qwen2.5-Coder-7B-Instruct model with this servicenow finetune, Qwen QwQ 32B and Quasar-Alpha (Secret new model on Openrouter, revealed as a Pre-Release of GPT 4.1, coding comparable a bit better than DeepSeek V3, https://openrouter.ai/openrouter/quasar-alpha, https://openrouter.ai/openai/gpt-4.1). DeepSeek R1 evaluated the results of each benchmark question.

Please note: This process definitly needs some improvements, for a general overview it should be good enough tho

Results were okay but not as good as i wanted, definitly taking another look at the training data and different approaches

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Model size
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Architecture
qwen2
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