memorag-qwen2-7b-inst - A memory module model in the MemoRAG project, designed for handling ultra-long contexts.

## Base Model of memorag-qwen2-7b-inst The base model of memorag-qwen2-7b-inst is Qwen2-7B-Instruct, which is a fine-tuned version of Qwen2-7B. ## Context Length Capability of memorag-qwen2-7b-inst memorag-qwen2-7b-inst can handle contexts up to 600K tokens, achieved through compression techniques. ## Primary Functions of memorag-qwen2-7b-inst The primary functions of memorag-qwen2-7b-inst include generating retrieval clues from long contexts to aid in database information retrieval and compressing ultra-long context information to make it more manageable for further processing. ## Access Points for memorag-qwen2-7b-inst Users can access the memorag-qwen2-7b-inst model through its official Hugging Face page at [Hugging Face](https://huggingface.co/TommyChien/memorag-qwen2-7b-inst) and the MemoRAG GitHub repository at [MemoRAG](https://github.com/qhjqhj00/MemoRAG). ## Model Size of memorag-qwen2-7b-inst The model size of memorag-qwen2-7b-inst is 8.08 billion parameters. ## Tensor Type in memorag-qwen2-7b-inst memorag-qwen2-7b-inst uses BF16 tensor type, which optimizes computational efficiency. ## Purpose of Compression in memorag-qwen2-7b-inst The compression feature in memorag-qwen2-7b-inst is designed to reduce the size of ultra-long context information, thereby improving processing speed and storage efficiency. ## Context Length Capability of memorag-qwen2-7b-inst Yes, there are discrepancies. While the official documentation suggests that memorag-qwen2-7b-inst can handle up to 600K tokens, the GitHub repository mentions managing 400K tokens, and the technical report indicates that performance remains good up to 600K tokens with a compression ratio of 16, beyond which performance may degrade. ### Citation sources: - [memorag-qwen2-7b-inst](https://huggingface.co/TommyChien/memorag-qwen2-7b-inst) - Official URL Updated: 2025-03-28