Instructions to use ToTo-40417/EXLLM-ONI5M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ToTo-40417/EXLLM-ONI5M 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-ONI5M:F16
# Run inference directly in the terminal:
llama cli -hf ToTo-40417/EXLLM-ONI5M: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-ONI5M:F16
# Run inference directly in the terminal:
llama cli -hf ToTo-40417/EXLLM-ONI5M: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-ONI5M:F16
# Run inference directly in the terminal:
./llama-cli -hf ToTo-40417/EXLLM-ONI5M: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-ONI5M:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ToTo-40417/EXLLM-ONI5M:F16

Use Docker

docker model run hf.co/ToTo-40417/EXLLM-ONI5M:F16

LM Studio
Jan

vLLM

How to use ToTo-40417/EXLLM-ONI5M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ToTo-40417/EXLLM-ONI5M"
# 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-ONI5M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ToTo-40417/EXLLM-ONI5M:F16

Ollama
How to use ToTo-40417/EXLLM-ONI5M with Ollama:
```
ollama run hf.co/ToTo-40417/EXLLM-ONI5M:F16
```
Unsloth Desktop
Docker Model Runner
How to use ToTo-40417/EXLLM-ONI5M with Docker Model Runner:
```
docker model run hf.co/ToTo-40417/EXLLM-ONI5M:F16
```

Lemonade

How to use ToTo-40417/EXLLM-ONI5M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ToTo-40417/EXLLM-ONI5M:F16

Run and chat with the model

lemonade run user.EXLLM-ONI5M-F16

List all available models

lemonade list

Atomic Chat

EXLLM-ONI5M

EXLLM-ONI5M is an experimental 0.005377824B-parameter Little Language Model trained to produce short Japanese replies in a deliberately impatient, old-school teacher persona. It is a playful, narrow template-based model—not a general assistant or a reliable source of facts.

EXQ12 inference has been verified on a CASIO EX-word XD-B4800 (DATAPLUS 6). The included GGUF is a separately trained Llama-compatible companion, not a conversion or quantization of the embedded checkpoint.

日本語の概要はこちらです。

Highlights

0.005377824B-parameter embedded checkpoint with a 128-token context and 868-token vocabulary
Short kana-oriented prompts and an intentionally gruff fictional persona
Physical-device inference verified at approximately 0.57 token/s in three recorded XD-B4800 runs
Separately trained 0.005441184B-parameter GGUF companion for LM Studio and llama.cpp
Project-authored templates and synthetic combinations; no external pretrained checkpoint
Apache-2.0

Intended Behavior

The training objective favors a short answer followed by a teacher-like admonition. Some unknown-input examples ask the user to rephrase, but this is learned text behavior—not a rejection mechanism or a guarantee against hallucination.

The persona is fictional. It may produce rude or inaccurate text and has not been evaluated as a safety system. It is intended for embedded inference experiments and entertainment, not factual, professional, or safety-critical use.

Physical EX-word Results

Readback SHA-256 verification, model loading, inference, screenshots, and PERFLOG capture were completed on an XD-B4800. The three recorded runs decoded at approximately 0.57 token/s. One prompt containing kanji did not produce the expected identity response and instead reached the 40-token limit, so these measurements demonstrate execution rather than general response quality.

The machine-readable observations are in benchmarks/xd-b4800-20260930.json.

Usage

LM Studio / llama.cpp

EXLLM-ONI5M-F16.gguf was separately initialized and trained from the ONI5M public training data. It has different weights, tokenizer, architecture details, and runtime context from the EX-word checkpoint.

llama-cli -m EXLLM-ONI5M-F16.gguf -c 512 --single-turn -p "こんにちは"

An RTX 3060 llama.cpp smoke test observed approximately 443 token/s and produced:

話を聞け！あいさつはいい！質問を言え！ 一つずつ確実にやれ！

This is a PC companion smoke-test measurement, not a directly comparable benchmark against the EX-word model. Training details and hashes are in lmstudio/training_manifest.json.

CASIO EX-word

Use exllm-exword v1.3.1 or later with weights/oni5m.q12. The file can be checked before deployment with exllm-model-check.

Training and Provenance

The embedded checkpoint was trained from random initialization for 18 epochs, recording 6,692,919 processed tokens and 4,532,281 loss-bearing tokens. Its data consists of project-authored templates and synthetic combinations. No third-party pretrained checkpoint or real-person quotation corpus was used.

See DATA_PROVENANCE.md, training/RECIPE.md, and release-manifest.json for the release boundary and reproducibility details.

Release Artifacts

Artifact	Size	SHA-256	Purpose
`weights/EXLLM-ONI5M-v1.0.pt`	21,528,721 bytes (20.53 MiB; 0.021528721 GB)	`661857ffc19dbca63f738173d1ef5e6c36e110413028fe88e0e24ad0799d6e7b`	FP32 PyTorch checkpoint
`weights/EXLLM-ONI5M-v1.0-int8.bin`	5,443,105 bytes (5.19 MiB; 0.005443105 GB)	`5a6bb0b7c9b15bfcda2cdd50c734b428488e9994a53ef47f9fa49653cf9b9adf`	EXLLM8 artifact
`weights/oni5m.q12`	5,443,105 bytes (5.19 MiB; 0.005443105 GB)	`64c8ce542fc251e26e9e10d756ef0bd33cbdf9e0d7336d1a7048538aca42c5ee`	EX-word EXQ12 artifact
`EXLLM-ONI5M-F16.gguf`	10,918,368 bytes (10.41 MiB; 0.010918368 GB)	`6c78fa17860062730bdcfa45835528dc6372b47c0fc28667bd0bc9e4d18483a8`	Separately trained LM Studio / llama.cpp companion
`tokenizer.json`	6,864 bytes	`4c2a583b554e8165936cb3b11727f79d2ede43b73d416a48681f8d38385f5964`	Embedded-model tokenizer

Limitations

This is a narrow persona experiment trained on finite templates and synthetic combinations.
It may answer incorrectly, ignore the intended persona, repeat text, or consume the full generation limit.
Unknown-input rephrasing is not guaranteed and does not prevent hallucination.
Kana variation, typos, kanji, long prompts, arithmetic, and open-ended conversation are unreliable.
The embedded model context is 128 tokens; the GGUF companion's 512-token runtime allocation does not establish 512-token task quality.
Physical-device and PC companion results belong to different models and numerical runtimes.
Do not use it for medical, legal, financial, safety-critical, or other professional decisions.

日本語

EXLLM-ONI5Mは、短気な昔気質の先生を演じる、5,377,824（0.005377824B）パラメータの遊び寄りのLittle Language Modelです。短いかな中心の入力に対し、短い回答と小言を返すよう学習していますが、汎用会話モデルでも安全機構でもありません。誤答や想定外の応答も発生します。

CASIO XD-B4800（DATAPLUS 6）でEXQ12推論を確認しています。同梱GGUFはEX-word用重みの変換ではなく、公開学習データから別途学習したPC用companionです。詳しい来歴、実行方法、制限事項は上の英語本文と付属資料を参照してください。

Downloads last month: 141

GGUF

Model size

5.44M params

Architecture

llama

Hardware compatibility

16-bit

ToTo-40417
/

EXLLM-ONI5M