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
Download training/release-5m.json from ToTo-40417/EXLLM: direct link, hf CLI and curl.
- Browser
- Download file 1.67 kB
-
https://huggingface.co/ToTo-40417/EXLLM/resolve/main/training/release-5m.json
- Command line
-
hf download hf://ToTo-40417/EXLLM/training/release-5m.json
-
curl -L -o release-5m.json https://huggingface.co/ToTo-40417/EXLLM/resolve/main/training/release-5m.json
1.67 kB
| { | |
| "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." | |
| } | |
| } | |