Instructions to use PrithviRana/DevOps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use PrithviRana/DevOps with 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 PrithviRana/DevOps:Q4_K_M # Run inference directly in the terminal: llama cli -hf PrithviRana/DevOps:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf PrithviRana/DevOps:Q4_K_M # Run inference directly in the terminal: llama cli -hf PrithviRana/DevOps: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 PrithviRana/DevOps:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf PrithviRana/DevOps: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 PrithviRana/DevOps:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf PrithviRana/DevOps:Q4_K_M
Use Docker
docker model run hf.co/PrithviRana/DevOps:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use PrithviRana/DevOps with Ollama:
ollama run hf.co/PrithviRana/DevOps:Q4_K_M
- Unsloth Desktop
- Pi
How to use PrithviRana/DevOps with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf PrithviRana/DevOps:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "PrithviRana/DevOps:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use PrithviRana/DevOps with Docker Model Runner:
docker model run hf.co/PrithviRana/DevOps:Q4_K_M
- Lemonade
How to use PrithviRana/DevOps with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull PrithviRana/DevOps:Q4_K_M
Run and chat with the model
lemonade run user.DevOps-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use PrithviRana/DevOps with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf PrithviRana/DevOps:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default PrithviRana/DevOps:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use PrithviRana/DevOps with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf PrithviRana/DevOps:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "PrithviRana/DevOps:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
File size: 761 Bytes
9a40336 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | /root/ai-tuning/ βββ devops_dataset.jsonl β βββ train.jsonl βββ validation.jsonl β βββ train_lora.py β βββ lora-output/ β βββ adapter_model.safetensors β βββ adapter_config.json β βββ training_args.bin β βββ tokenizer.json β βββ ... β βββ merge_lora.py β βββ merged-qwen-devops/ β βββ model-00001-of-00007.safetensors β βββ model-00002-of-00007.safetensors β βββ ... β βββ model-00007-of-00007.safetensors β βββ tokenizer files β βββ qwen-devops-f16.gguf β βββ qwen-devops-q4_k_m.gguf β βββ Modelfile-qwen-devops β βββ benchmark_ollama.py β βββ benchmark_results.json |