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
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Download EXQ12_FORMAT.md from ToTo-40417/EXLLM: direct link, hf CLI and curl.
- Browser
- Download file 1.07 kB
-
https://huggingface.co/ToTo-40417/EXLLM/resolve/main/EXQ12_FORMAT.md
- Command line
-
hf download hf://ToTo-40417/EXLLM/EXQ12_FORMAT.md
-
curl -L -o EXQ12_FORMAT.md https://huggingface.co/ToTo-40417/EXLLM/resolve/main/EXQ12_FORMAT.md
1.07 kB
EXQ12 deployment format
model.q12 is produced deterministically from the published EXLLM8 file by
tools/export_exq12.py. It contains the same 39 tensors in the order stored by
EXLLM8. The EX-word runtime uses integer matrix weights and fixed-point scales;
the file is not GGUF and is not interchangeable with EXLLM8.
Header
- 8 bytes:
EXQ12\0\0\0 - little-endian
uint32: format version (1) - little-endian
uint32: tensor count (39for the 5M release)
Each tensor record contains a little-endian uint16 name length, uint8
dimension count, uint8 qtype, UTF-8 name bytes, uint32 dimensions, uint32
scale count, uint32 payload byte count, optional scale data, and the payload.
- qtype
1: signed int8 matrix payload; one signed Q20int32scale per row. - qtype
3: signed Q12int16vector payload; no separate scale table.
Reproduce the release artifact
python tools/export_exq12.py
Expected result:
bytes: 5443105
sha256: d64037fde791e5c0e48101bc1a8ab366a3287f1f36b4464879e36495ea7e5a53
tensors: 39