Instructions to use Ken1drick/sendai_and_miyagi 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 Ken1drick/sendai_and_miyagi 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 Ken1drick/sendai_and_miyagi:Q8_0 # Run inference directly in the terminal: llama cli -hf Ken1drick/sendai_and_miyagi:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Ken1drick/sendai_and_miyagi:Q8_0 # Run inference directly in the terminal: llama cli -hf Ken1drick/sendai_and_miyagi:Q8_0
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 Ken1drick/sendai_and_miyagi:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf Ken1drick/sendai_and_miyagi:Q8_0
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 Ken1drick/sendai_and_miyagi:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Ken1drick/sendai_and_miyagi:Q8_0
Use Docker
docker model run hf.co/Ken1drick/sendai_and_miyagi:Q8_0
- LM Studio
- Jan
- Ollama
How to use Ken1drick/sendai_and_miyagi with Ollama:
ollama run hf.co/Ken1drick/sendai_and_miyagi:Q8_0
- Unsloth Desktop
- Pi
How to use Ken1drick/sendai_and_miyagi with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Ken1drick/sendai_and_miyagi:Q8_0
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": "Ken1drick/sendai_and_miyagi:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Ken1drick/sendai_and_miyagi with Docker Model Runner:
docker model run hf.co/Ken1drick/sendai_and_miyagi:Q8_0
- Lemonade
How to use Ken1drick/sendai_and_miyagi with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Ken1drick/sendai_and_miyagi:Q8_0
Run and chat with the model
lemonade run user.sendai_and_miyagi-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use Ken1drick/sendai_and_miyagi with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Ken1drick/sendai_and_miyagi:Q8_0
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 Ken1drick/sendai_and_miyagi:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Ken1drick/sendai_and_miyagi with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Ken1drick/sendai_and_miyagi:Q8_0
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 "Ken1drick/sendai_and_miyagi:Q8_0" \ --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"
github link: https://github.com/chocolatesource/llm_first_test/tree/main?tab=readme-ov-file
Summer holiday finetune
between year 1 to year 2 in university
did around 3 weeks
我在大學的暑假中 finetune
做了大概 3 個禮拜
Novel:
Story About Buying My Classmate Once A Week
週に一度クラスメイトを買う話 ~ふたりの時間、言い訳の五千円~
我買下了與她的每週密會
Dialouge extractedusing deepseek about ~5HKD shown on the json file
It is finetune on unsloth in google colab
- Downloads last month
- 40
8-bit
Model tree for Ken1drick/sendai_and_miyagi
Base model
meta-llama/Llama-3.2-3B-Instruct