Instructions to use iabheejit/ekatra-tutor-gguf 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 iabheejit/ekatra-tutor-gguf 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 iabheejit/ekatra-tutor-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf iabheejit/ekatra-tutor-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf iabheejit/ekatra-tutor-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf iabheejit/ekatra-tutor-gguf:Q4_K_M
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 iabheejit/ekatra-tutor-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf iabheejit/ekatra-tutor-gguf:Q4_K_M
Use Docker
docker model run hf.co/iabheejit/ekatra-tutor-gguf:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use iabheejit/ekatra-tutor-gguf with Ollama:
ollama run hf.co/iabheejit/ekatra-tutor-gguf:Q4_K_M
- Unsloth Desktop
- Pi
How to use iabheejit/ekatra-tutor-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf iabheejit/ekatra-tutor-gguf: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": "iabheejit/ekatra-tutor-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use iabheejit/ekatra-tutor-gguf with Docker Model Runner:
docker model run hf.co/iabheejit/ekatra-tutor-gguf:Q4_K_M
- Lemonade
How to use iabheejit/ekatra-tutor-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull iabheejit/ekatra-tutor-gguf:Q4_K_M
Run and chat with the model
lemonade run user.ekatra-tutor-gguf-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use iabheejit/ekatra-tutor-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf iabheejit/ekatra-tutor-gguf: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 iabheejit/ekatra-tutor-gguf:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use iabheejit/ekatra-tutor-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf iabheejit/ekatra-tutor-gguf: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 "iabheejit/ekatra-tutor-gguf: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"
Ekatra Tutor and Lite phone builds (GGUF, private)
LoRA merged into the base and converted with llama.cpp, for offline use on phones and laptops (intern task 7).
| File | Model | Quantisation |
|---|---|---|
| tutor4b-Q4_K_M-imat.gguf | Ekatra Tutor v1, Qwen3.5-4B | Q4_K_M with importance matrix (recommended phone build) |
| tutor4b-Q8_0.gguf | Ekatra Tutor v1, Qwen3.5-4B | Q8_0 |
| tutor4b.imatrix | importance matrix used above | - |
| lite2b-Q4_K_M.gguf | Ekatra Lite, Qwen3.5-2B | Q4_K_M |
| lite2b-Q8_0.gguf | Ekatra Lite, Qwen3.5-2B | Q8_0 |
On 399 frozen Tutor rows judged by Grok and Kimi, the 4B Q4_K_M+imatrix build scored 4.34 / 4.32 against 4.37 / 4.33 for bf16, and Q8_0 4.38 / 4.32. SHA256SUMS lists every file's runner-side hash.
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