tocount 0.5

ToCount: Lightweight Token Estimator

ToCount: Lightweight Token Estimator

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Overview

ToCount is a lightweight and extensible Python library for estimating token counts from text inputs using both rule-based and machine learning methods. Designed for flexibility, speed, and accuracy, ToCount provides a unified interface for different estimation strategies, making it ideal for tasks like prompt analysis, token budgeting, and optimizing interactions with token-based systems.

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Code Quality

Installation

PyPI

• Check Python Packaging User Guide
• Run pip install tocount==0.5

Source code

• Download Version 0.5 or Latest Source
• Run pip install .

Models

Rule-Based

Model Name	R²	MAE	RMSE	MedAE	D²
RULE_BASED.UNIVERSAL	0.8175	106.70	617.78	18	0.6377
RULE_BASED.GPT_3_5	0.7266	152.34	756.17	35	0.4828
RULE_BASED.GPT_4	0.6878	161.93	808.04	40	0.4502

Tiktoken R50K

Model Name	R²	MAE	RMSE	MedAE	D²
TIKTOKEN_R50K.LINEAR_ALL	0.7334	152.39	733.40	28.55	0.4826
TIKTOKEN_R50K.LINEAR_ENGLISH	0.8703	62.76	508.20	8.87	0.7287

Tiktoken CL100K

Model Name	R²	MAE	RMSE	MedAE	D²
TIKTOKEN_CL100K.LINEAR_ALL	0.9127	64.09	298.02	15.73	0.6804
TIKTOKEN_CL100K.LINEAR_ENGLISH	0.9711	27.43	185.07	6.34	0.8527

Tiktoken O200K

Model Name	R²	MAE	RMSE	MedAE	D²
TIKTOKEN_O200K.LINEAR_ALL	0.9563	38.23	197.16	9.70	0.7818
TIKTOKEN_O200K.LINEAR_ENGLISH	0.9730	26.00	177.54	5.96	0.8581

Deepseek R1

Model Name	R²	MAE	RMSE	MedAE	D²
DEEPSEEK_R1.LINEAR_ALL	0.9531	40.66	212.11	10.71	0.7741
DEEPSEEK_R1.LINEAR_ENGLISH	0.9696	28.44	192.36	6.36	0.8477

Qwen QwQ

Model Name	R²	MAE	RMSE	MedAE	D²
QWEN_QWQ.LINEAR_ALL	0.9342	45.50	257.97	12.17	0.7542
QWEN_QWQ.LINEAR_ENGLISH	0.9570	29.06	236.10	6.68	0.8457

Llama 3.1

Model Name	R²	MAE	RMSE	MedAE	D²
LLAMA_3_1.LINEAR_ALL	0.9538	44.37	207.58	11.70	0.7578
LLAMA_3_1.LINEAR_ENGLISH	0.9731	26.59	177.94	6.24	0.8564

ℹ️ The training and testing dataset is taken from Lmsys-chat-1m [1] and Wildchat [2].

Usage

>>> from tocount import estimate_text_tokens, TextEstimator
>>> estimate_text_tokens("How are you?", estimator=TextEstimator.RULE_BASED.UNIVERSAL)
4

Issues & bug reports

Just fill an issue and describe it. We'll check it ASAP! or send an email to [e-mail].

• Please complete the issue template

You can also join our discord server

References

1- Zheng, Lianmin, et al. "Lmsys-chat-1m: A large-scale real-world llm conversation dataset." International Conference on Learning Representations (ICLR) 2024 Spotlights.

2- Zhao, Wenting, et al. "Wildchat: 1m chatgpt interaction logs in the wild." International Conference on Learning Representations (ICLR) 2024 Spotlights.

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If you do like our project and we hope that you do, can you please support us? Our project is not and is never going to be working for profit. We need the money just so we can continue doing what we do ;-) .

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

Unreleased

0.5 - 2026-01-02

Added

• DEEPSEEK_R1.LINEAR_ALL model
• DEEPSEEK_R1.LINEAR_ENGLISH model
• QWEN_QWQ.LINEAR_ALL model
• QWEN_QWQ.LINEAR_ENGLISH model
• LLAMA_3_1.LINEAR_ALL model
• LLAMA_3_1.LINEAR_ENGLISH model

Changed

• README.md updated

0.4 - 2025-12-17

Added

• Logo

Changed

• TIKTOKEN_CL100K.LINEAR_ALL model updated
• TIKTOKEN_CL100K.LINEAR_ENGLISH model updated
• TIKTOKEN_O200K.LINEAR_ALL model updated
• TIKTOKEN_O200K.LINEAR_ENGLISH model updated
• TIKTOKEN_R50K.LINEAR_ALL model updated
• TIKTOKEN_R50K.LINEAR_ENGLISH model updated

0.3 - 2025-10-21

Added

• TIKTOKEN_CL100K.LINEAR_ALL model
• TIKTOKEN_CL100K.LINEAR_ENGLISH model
• TIKTOKEN_O200K.LINEAR_ALL model
• TIKTOKEN_O200K.LINEAR_ENGLISH model

Changed

• README.md updated
• Python 3.14 added to test.yml

0.2 - 2025-10-02

Added

• TIKTOKEN_R50K.LINEAR_ALL model
• TIKTOKEN_R50K.LINEAR_ENGLISH model

Changed

• README.md updated

0.1 - 2025-08-30

Added

• RULE_BASED.UNIVERSAL model
• RULE_BASED.GPT_4 model
• RULE_BASED.GPT_3_5 model