Instructions to use DoctorPuppy/studyapp-writer 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 DoctorPuppy/studyapp-writer 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 DoctorPuppy/studyapp-writer # Run inference directly in the terminal: llama cli -hf DoctorPuppy/studyapp-writer
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf DoctorPuppy/studyapp-writer # Run inference directly in the terminal: llama cli -hf DoctorPuppy/studyapp-writer
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 DoctorPuppy/studyapp-writer # Run inference directly in the terminal: ./llama-cli -hf DoctorPuppy/studyapp-writer
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 DoctorPuppy/studyapp-writer # Run inference directly in the terminal: ./build/bin/llama-cli -hf DoctorPuppy/studyapp-writer
Use Docker
docker model run hf.co/DoctorPuppy/studyapp-writer
- LM Studio
- Jan
- Ollama
How to use DoctorPuppy/studyapp-writer with Ollama:
ollama run hf.co/DoctorPuppy/studyapp-writer
- Unsloth Desktop
- Pi
How to use DoctorPuppy/studyapp-writer with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf DoctorPuppy/studyapp-writer
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": "DoctorPuppy/studyapp-writer" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use DoctorPuppy/studyapp-writer with Docker Model Runner:
docker model run hf.co/DoctorPuppy/studyapp-writer
- Lemonade
How to use DoctorPuppy/studyapp-writer with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull DoctorPuppy/studyapp-writer
Run and chat with the model
lemonade run user.studyapp-writer-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use DoctorPuppy/studyapp-writer with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf DoctorPuppy/studyapp-writer
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 DoctorPuppy/studyapp-writer
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use DoctorPuppy/studyapp-writer with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf DoctorPuppy/studyapp-writer
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 "DoctorPuppy/studyapp-writer" \ --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"
StudyApp card writer
The card writer that the StudyApp desktop app downloads. It's Gemma 4 E2B (instruction-tuned), trained with a LoRA adapter on StudyApp's own tasks, merged, and quantized to Q4_K_M for llama.cpp. It runs locally, entirely offline.
The tasks it's trained on, using the app's own prompts and output formats:
- flashcards for each page of a student's notes
- facts, scenario questions and practice-test questions
- grading a typed answer, and checking the key points of a written one
- chat that answers only from the student's notes, and answers
NOT_FOUNDwhen the notes don't have the answer
StudyApp runs it with the multi-token-prediction head from
ggml-org/gemma-4-E2B-it-GGUF
(mtp-gemma-4-E2B-it-Q8_0.gguf) for faster generation.
Training data
There are 618 examples. Each one was written for excerpts of openly licensed textbooks and passed the same checks the app runs on its own output. 569 were used for training and 49 were held out to measure it.
The excerpts come from these OpenStax textbooks (openstax.org), which are licensed under CC BY 4.0. They're free to read at openstax.org.
- American Government 2e
- Anatomy and Physiology
- Biology
- Chemistry
- College Physics
- Introduction to Sociology 2e
- Introductory Statistics
- Precalculus
- Principles of Economics 2e
- Psychology
Release test
This writer was compared with the original Gemma 4 E2B on two textbooks it never trained on: OpenStax Astronomy and Introduction to Intellectual Property.
| Original | This writer | |
|---|---|---|
| Flashcards: answers that broke the app's format or limits | 0 of 46 | 0 of 46 |
| Flashcards: cards the app's checks flagged | 0 of 136 | 3 of 145 |
| Flashcards: blind read, which writer's cards were better | 6 | 22 (18 the same) |
| Chat: questions the notes answer, answered cleanly | 12 of 12 | 11 of 12 |
| Chat: questions the notes can't answer, declined | 9 of 11 | 10 of 11 |
| Practice-test questions: problems found in 3 page batches | 3 | 8 |
License
The model weights are licensed under Apache-2.0, the same as Gemma 4.
- Downloads last month
- 324
We're not able to determine the quantization variants.