Tokencal: Token Price Estimation for LLMs
Overview
Tokencal is a sophisticated tool designed to estimate token prices for large language models (LLMs). Whether you're a developer, researcher, or business, Tokencal helps you optimize token usage and manage costs effectively.
Features
- Accurate Estimations: Get precise token price estimates for various LLMs.
- Efficient Performance: Fast calculations to save your time.
- Extensive Compatibility: Works with multiple LLMs.
Installation
To install Tokencal, simply run:
pip install tokencal
Usage
Using Tokencal is straightforward. Here’s a quick example to get you started:
from tokencal import TokenEstimator
# Initialize the estimator with your LLM model details
estimator = TokenEstimator(model_name="Your_LLM_Model")
# Estimate token price
price = estimator.estimate_price(text="Sample text to estimate token price.")
print(f"Estimated token price: {price}")
Keywords
Token price estimation, LLM, Large Language Models, cost optimization, token usage
Metadata
Release files for tokencal 0.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| tokencal-0.1.2.tar.gz | 2.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tokencal-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 5.8 kB
Release files / tokencal-0.1.2.tar.gz
| Download URL | tokencal-0.1.2.tar.gz |
|---|---|
| Size | 2.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.0 CPython/3.12.4
|
Release files / tokencal-0.1.2-py3-none-any.whl
| Download URL | tokencal-0.1.2-py3-none-any.whl |
|---|---|
| Size | 2.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.0 CPython/3.12.4
|