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procedure2012/Pulsar-Knowledge-RAG

sourceHugging Facecc-by-4.0updated 2mo agoView on Hugging Face

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Model Card

Pulsar-Knowledge-RAG

1. Introduction

Pulsar-Knowledge-RAG is tuned for retrieval-augmented question answering and grounded knowledge retrieval.

2. Evaluation Results

Comprehensive Benchmark Results

	Benchmark	Retriever-XL	Pulsar-lite	FactBase	Pulsar-Knowledge-RAG
Core Reasoning Tasks	Math Reasoning	0.561	0.589	0.592	0.619
	Logical Reasoning	0.833	0.809	0.828	0.849
	Common Sense	0.755	0.770	0.724	0.776
Language Understanding	Reading Comprehension	0.725	0.704	0.699	0.750
	Question Answering	0.583	0.591	0.600	0.642
	Text Classification	0.814	0.830	0.806	0.848
	Sentiment Analysis	0.787	0.802	0.782	0.814
Generation Tasks	Code Generation	0.691	0.692	0.696	0.717
	Creative Writing	0.648	0.678	0.667	0.685
	Dialogue Generation	0.642	0.666	0.671	0.692
	Summarization	0.749	0.762	0.784	0.799
Specialized Capabilities	Translation	0.795	0.767	0.789	0.823
	Knowledge Retrieval	0.703	0.676	0.686	0.711
	Instruction Following	0.735	0.756	0.759	0.792
	Safety Evaluation	0.723	0.736	0.725	0.772

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Overall Performance Summary

The Pulsar-Knowledge-RAG demonstrates strong performance across all evaluated benchmark categories, with particularly notable results in reasoning and generation tasks.

3. Chat Website & API Platform

We offer a chat interface and API for you to interact with Pulsar-Knowledge-RAG. Please check our official website for more details.

4. How to Run Locally

Please refer to our code repository for more information about running Pulsar-Knowledge-RAG locally.

Temperature

We recommend setting the temperature parameter to 0.6.

5. License

This repository is released under the cc-by-4.0 license. The model supports commercial use.

6. Contact

If you have any questions, please contact us at rag@pulsar.systems.