CandyLLM 0.0.1

CandyLLM: Unified framework for HuggingFace and OpenAI Text-generation Models

CandyLLM 🍬

A simple, easy-to-use framework for HuggingFace and OpenAI text-generation models. The goal is to eventually integrate other sources such as custom large language models (LLMs) as well to create a coherent UI.

This is a work-in-progress, so pull-requests and issues are welcome! We try to keep it as stable as possible though, so people installing this library do not have any problems.

If you use this library, please cite Shreyan Mitra.

With all the administrivia out of the way, here are some examples of how to use the library. We are still setting up the official documentation. The following examples show some use cases, or tasks, and how an user of llm-wrapper would invoke the model of their choice.

Install package

pip install CandyLLM

Task: Fetch Llama3-8b and run it with default parameters on a simple QA Prompt without retrieval augmented generation

from CandyLLM import*
myLLM = LLMWrapper("MY_HF_TOKEN", testing=False)
myLLM.answer("What is the capital of Uzbekistan?") #Returns Tashkent

This behavior is due to the fact that the default model is Llama3-8b

Task: Fetch Llama2-7b and run it with tempereature = 0.6 on an QA Prompt with retrieval augmented generation

from CandyLLM import*
myLLM = LLMWrapper("MY_HF_TOKEN", testing=False, modelName = "Llama2-7b") #or myLLM = LLMWrapper("MY_HF_TOKEN", testing=False, modelName = "meta-llama/Llama-2-7b-chat-hf", modelNameType="path")
myLLM.answer("What is the capital of Funlandia?", task="QAWithRAG", "The capital of Funlandia is Funtown", temperature=0.6) #Returns Funtown

Task: Fetch GPT-4 and run it with presence_penalty = 0.5 on an Open-Ended Prompt

from CandyLLM import*
myLLM = LLMWrapper("MY_OPENAI_TOKEN", testing=False, source="OpenAI", modelName = "gpt-4-turbo", modelNameType="path")
myLLM.answer("Write a creative essay about sustainability", task="Open-ended", presence_penalty=0.5)

Log out of HuggingFace and OpenAI and remove my API keys from the environment

myLLM = LLMWrapper(...) #Create some LLM wrapper
myLLM.answer(...) #Do something with the LLM
myLLM.logout()

Check for malicious input prompts

myLLM = LLMWrapper(...) #Create some LLM wrapper
myLLM.promptSafetyCheck("Is 1010 John Doe's social security number?") #Returns false to indicate unsafe prompt

Change Config

Want to use a different model. No need to create another wrapper.

myLLM = LLMWrapper(...) #Create some LLM wrapper
myLLM.setConfig("MY_TOKEN", testing = False, source="HuggingFace", modelName = "Mistral", modelNameType = "alias") #Tada: a changed LLM wrapper

Dummy LLM

Sometimes, you don't want to spend the time and money to make api calls to an actual LLM, especially if you are testing an UI or an integration of a chat service. Dummy LLMs to the rescue! Our dummy LLM is called "Useless" and it will return answers immediately with very little computation spent (granted, the results it gives are useless - but, hey, what did you expect? 😃)