Instructions to use procedure2012/Pulsar-Knowledge-RAG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use procedure2012/Pulsar-Knowledge-RAG with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="procedure2012/Pulsar-Knowledge-RAG")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("procedure2012/Pulsar-Knowledge-RAG") model = AutoModel.from_pretrained("procedure2012/Pulsar-Knowledge-RAG", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: cc-by-4.0
library_name: transformers
---
# Pulsar-Knowledge-RAG
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## 1. Introduction
Pulsar-Knowledge-RAG is tuned for retrieval-augmented question answering and grounded knowledge retrieval.
## 2. Evaluation Results
### Comprehensive Benchmark Results
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| | 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.
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