Instructions to use sqrtask/tamagotchi-models 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 sqrtask/tamagotchi-models 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 sqrtask/tamagotchi-models:Q3_K_M # Run inference directly in the terminal: llama cli -hf sqrtask/tamagotchi-models:Q3_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf sqrtask/tamagotchi-models:Q3_K_M # Run inference directly in the terminal: llama cli -hf sqrtask/tamagotchi-models:Q3_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 sqrtask/tamagotchi-models:Q3_K_M # Run inference directly in the terminal: ./llama-cli -hf sqrtask/tamagotchi-models:Q3_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 sqrtask/tamagotchi-models:Q3_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf sqrtask/tamagotchi-models:Q3_K_M
Use Docker
docker model run hf.co/sqrtask/tamagotchi-models:Q3_K_M
- LM Studio
- Jan
- Ollama
How to use sqrtask/tamagotchi-models with Ollama:
ollama run hf.co/sqrtask/tamagotchi-models:Q3_K_M
- Unsloth Desktop
- Pi
How to use sqrtask/tamagotchi-models with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf sqrtask/tamagotchi-models:Q3_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": "sqrtask/tamagotchi-models:Q3_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use sqrtask/tamagotchi-models with Docker Model Runner:
docker model run hf.co/sqrtask/tamagotchi-models:Q3_K_M
- Lemonade
How to use sqrtask/tamagotchi-models with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sqrtask/tamagotchi-models:Q3_K_M
Run and chat with the model
lemonade run user.tamagotchi-models-Q3_K_M
List all available models
lemonade list
- Hermes Agent
How to use sqrtask/tamagotchi-models with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf sqrtask/tamagotchi-models:Q3_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 sqrtask/tamagotchi-models:Q3_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use sqrtask/tamagotchi-models with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf sqrtask/tamagotchi-models:Q3_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 "sqrtask/tamagotchi-models:Q3_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"
Commit History
16 days ago
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g4 v11.32: gemma-4-E2B-it-web-enru-ple.bin (ядро + PLE по требованию, рядом с полной .task)
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g4 v11.32: gemma-4-E2B-it-web-enru-core.bin (ядро + PLE по требованию, рядом с полной .task)
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taesana encoder (madebyollin/taesd, MIT) for img2img
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taesana encoder (madebyollin/taesd, MIT) for img2img
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Компактный токенизатор Gemma-2 (tok_pack.py): номера как у tokenizers
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Свой движок 8h: описание весов для te8_engine.js
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Свой декодер taesana (M4): программы и веса fp16 для dec_engine.js
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gemma-4 E2B web.task: словарь — английский + русский + служебные/эмодзи (lang_keep.py)
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Таблица эмбеддингов Gemma-2 без строк других языков (lang_keep.py)
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Таблица эмбеддингов Gemma-2 без строк других языков (lang_keep.py)
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Таблица эмбеддингов Gemma-2 без строк других языков (lang_keep.py)
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Описание весов для своего движка SANA (make_engine_json.py)
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RGBA-декодер 768→512 (alpha/rgba512.py)
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RGBA-декодер 768→512 (alpha/rgba512.py)
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RGBA-декодер 768→512 (alpha/rgba512.py)
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RGBA-декодер 768→512 (alpha/rgba512.py)
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SANA-Sprint denoiser graph variants: fixed 768 + baked t, accuracy_level=4 (same weights)
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SANA-Sprint denoiser graph variants: fixed 768 + baked t, accuracy_level=4 (same weights)
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SANA-Sprint denoiser graph variants: fixed 768 + baked t, accuracy_level=4 (same weights)
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taesana + alpha branch v4 (ONNX, dynamic size)
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taesana + alpha branch v4 (ONNX, dynamic size)
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sana-sprint: урезанная Gemma-2 8h (4 бита, 231 МБ) для песочницы; лицензии sana-sprint/ и NOTICE Gemma
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taesana 512 (без ступени 1024), дообучен под DC-AE на парах Д2/Д4: PSNR 33.4 против 31.2, 24 мс против 48 на L4
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