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
File size: 1,668 Bytes
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"schema": "exllm-training-release-v1",
"model": "EXLLM-0.005B-Instruct",
"checkpoint": "weights/EXLLM-v1.1-5m-release3.pt",
"checkpoint_sha256": "48ad00c6640689595a253fc949cad83f3000e14cccdf69275fcf245e85be3ceb",
"parameters": 5377824,
"parameters_billions": 0.005377824,
"global_step": 1810,
"lineage": {
"project_origin": "random initialization",
"release_parent": "weights/EXLLM-v1.0.0.safetensors",
"release_parent_sha256": "caad6a93aa2a8f6f2b525c4671dc8d2f8e4328d262a093172a55c672cd4e24c9",
"expansion": "dimension-compatible parameter transplantation",
"external_pretrained_checkpoint": null,
"stage_manifest": "training/release-5m-stages.json"
},
"architecture": {
"vocab_size": 868,
"max_seq_len": 128,
"d_model": 288,
"n_layers": 6,
"n_heads": 9,
"d_ff": 896,
"activation": "relu",
"normalization": "pre-norm RMSNorm",
"rms_eps": 0.00001,
"position_encoding": "learned absolute"
},
"base_training": {
"optimizer": "AdamW",
"learning_rate": 0.0005,
"betas": [0.9, 0.95],
"weight_decay": 0.03,
"gradient_clip_norm": 1.0,
"schedule": "linear warm-up and cosine decay"
},
"final_corrective_stage": {
"source": "weights/EXLLM-v1.1-5m-release2.pt",
"data": "data/v1_1_release_fix.jsonl",
"steps": 100,
"learning_rate": 0.000006,
"loss_first": 0.038745223909500054,
"loss_last": 0.024198499539634214
},
"published_staged_data": {
"jsonl_file_records": 86408,
"records_millions": 0.086408,
"deduplicated": false,
"note": "Sum of records across overlapping staged files; not a unique-example count."
}
}
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