Update README.md

README.md

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 license: mit
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-This model is a 7B [Self-RAG](https://selfrag.github.io/) model that generates outputs to diverse user queries as well as *reflection tokens* to call the retrieval system adaptively and criticize its own output and retrieved passages.  
-
-Self-RAG is trained on our instruction-following corpora with interleaving passages and reflection tokens using the standard next-token prediction objective, enabling efficient and stable learning with fine-grained feedback.  
-At inference, we leverage reflection tokens covering diverse aspects of generations to sample the best output aligning users' preferences. 
-See full descriptions in See full descriptions in [our paper](https://arxiv.org/abs/2310.11511). 
+4 bit quantization of: https://huggingface.co/selfrag/selfrag_llama2_7b
 
 ## Usage
 Here, we show an easy way to quickly download our model from HuggingFace and run with `vllm` with pre-given passages. Make sure to install dependencies listed at [self-rag/requirements.txt](https://github.com/AkariAsai/self-rag/blob/main/requirements.txt). 
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 If you have additional input. 
 You can insert paragraphs anywhere after `### Response:\n"`, but make sure to mark paragraphs as paragraph tokens (i.e., `<paragraph>{0}</paragraph>`).
 
-## Training details
-Our training data is available at the HuggingFace dataset [selfrag_train_data](https://huggingface.co/datasets/selfrag/selfrag_train_data). 
-See our official repository for the training details. 
-We used 8 A100 40GB for training on the Stability HPC server.
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 ## Citation and contact
 If you use this model, please cite our work: 
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