Update README.md

README.md

@@ -66,4 +66,11 @@ inputs = tokenizer(prompt, return_tensors="pt")
 inputs = inputs.to(model.device)
 pred = model.generate(**inputs, max_length=4096, do_sample=True, top_k=50, top_p=0.99, temperature=0.9, num_return_sequences=1)
 print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
-```
+```
+
+## Other Information
+Paper reference: [Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks](https://arxiv.org/abs/2401.02731)
+[Original Paper repo](https://github.com/wuhy68/Parameter-Efficient-MoE)
+[Forked repo with mistral support (sparsetral)](https://github.com/serp-ai/Parameter-Efficient-MoE)
+
+If you are interested in faster inferencing, check out our [fork of vLLM](https://github.com/serp-ai/vllm) that adds sparsetral support