Text Generation
GGUF
Japanese
japanese
instruction-tuning
little-language-model
tiny-language-model
edge-ai
embedded-ai
ex-word
llama-cpp
lm-studio
custom-code
conversational
Instructions to use ToTo-40417/EXLLM 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 ToTo-40417/EXLLM 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 ToTo-40417/EXLLM:F16 # Run inference directly in the terminal: llama cli -hf ToTo-40417/EXLLM:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ToTo-40417/EXLLM:F16 # Run inference directly in the terminal: llama cli -hf ToTo-40417/EXLLM:F16
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 ToTo-40417/EXLLM:F16 # Run inference directly in the terminal: ./llama-cli -hf ToTo-40417/EXLLM:F16
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 ToTo-40417/EXLLM:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ToTo-40417/EXLLM:F16
Use Docker
docker model run hf.co/ToTo-40417/EXLLM:F16
- LM Studio
- Jan
- vLLM
How to use ToTo-40417/EXLLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ToTo-40417/EXLLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ToTo-40417/EXLLM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ToTo-40417/EXLLM:F16
- Ollama
How to use ToTo-40417/EXLLM with Ollama:
ollama run hf.co/ToTo-40417/EXLLM:F16
- Unsloth Desktop
- Docker Model Runner
How to use ToTo-40417/EXLLM with Docker Model Runner:
docker model run hf.co/ToTo-40417/EXLLM:F16
- Lemonade
How to use ToTo-40417/EXLLM with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ToTo-40417/EXLLM:F16
Run and chat with the model
lemonade run user.EXLLM-F16
List all available models
lemonade list
- Atomic Chat
| #!/usr/bin/env python3 | |
| import argparse, torch | |
| from src.loader import load_release_model | |
| from src.runtime import answer | |
| def main(): | |
| ap=argparse.ArgumentParser(description='EXLLM v1.1.0 reference chat runtime') | |
| ap.add_argument('--temperature',type=float,default=0.0,help='0 for deterministic; e.g. 0.55 for sampling') | |
| ap.add_argument('--top-k',type=int,default=8) | |
| ap.add_argument('--threads',type=int,default=4) | |
| ap.add_argument('prompt',nargs='*') | |
| a=ap.parse_args(); torch.set_num_threads(max(1,a.threads)) | |
| m,t=load_release_model() | |
| if a.prompt: | |
| print(answer(m,t,' '.join(a.prompt),temperature=a.temperature,top_k=a.top_k)); return | |
| print('EXLLM v1.1.0 / 終了: /exit') | |
| while True: | |
| try:q=input('> ') | |
| except (EOFError,KeyboardInterrupt): break | |
| if q.strip() in ('/exit','/quit'): break | |
| print(answer(m,t,q,temperature=a.temperature,top_k=a.top_k)) | |
| if __name__=='__main__': main() | |