How does your application automatically assess questions?

Prior to that, the language model is provided with the query and instructions to respond succinctly; otherwise, certain models have a tendency to generate lengthy responses. Following this, the model's response is assessed using a two-step procedure: string comparison followed by LLM comparison. Initially, the response string is merely compared to the user-supplied correct answer string. When the values of the two are identical, the response is deemed accurate. If not, the model and accurate response are forwarded to an alternative LLM (GPT-3.5-Turbo). The evaluation model is tasked with ascertaining the semantic equivalent of the two provided answers. When the model ascertains that the two responses are semantically equivalent, it assigns the correct answer to the evaluation. Conversely, the response is deemed to be inaccurate.

How do I resolve the 502 Bad Gateway Error that I am receiving?

API requests to the language models occasionally delay out, resulting in our hosting server reaching its maximum capacity. Simply refresh the page with the same information sent again, and it should resume from where it left off. An approach towards a more enduring resolution is currently being considered.

Which configuration of the model are you employing?

All models are implemented in LangChain using the default parameters, with the exception of setting the temperature to 0. The temperature for Replicate-hosted models (llama-2, falcon) is established at 0.01.

Home AI Tools Krasp

Krasp

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Kraspai is an AI utility simplifying prompt testing across diverse language models, offering easy creation of testing suites without coding knowledge and supporting import/export functionalities for efficient test management.

Key AI Functions:IT,hr,human resources,developer ,information technology,misc tools

Description for Krasp

Kraspai is an AI utility designed for testing prompts across diverse closed and open-source language models. It allows effortless creation of testing suites for model comparison in under a minute, without coding knowledge.

Features of Krasp:

Effortless Testing Suite Creation: Enables quick creation of unique testing suites for comparing models without coding knowledge, taking less than a minute.
Import and Export Functionality: Allows importation of test cases and exportation of test results in CSV format, facilitating easy management and analysis of test data.
Model Support: Supports a variety of language models including OpenAI's GPT-3.5 Turbo, GPT-4, and Text-Davinci, along with Cohere Command, AlephAlpha Luminous, Meta-Llama, and TII Falcon.
Practicality and Effectiveness: Offers practical and effective means for evaluating language model performance across diverse scenarios and conducting comprehensive model testing.

Use Cases for Krasp

Model Functionality Comparison: Facilitates comparison of functionality among different language models to assess their strengths and weaknesses.

AI Model Capability Assessment: Provides a platform for evaluating the capabilities and performance of various AI models.

Prompt Response Accuracy Evaluation: Allows assessment of the accuracy of prompt responses generated by language models, aiding in determining their suitability for specific tasks.

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