Update README.md

Author: Kyriection

recipes/experimental/long-context/H2O/README.md

@@ -4,7 +4,7 @@
 
 Heavy-Hitter Oracle (H2O) is an efficient inference framework of LLMs. During the generative inference of transfomers, the size of KV cache grows linearly with the sequence length (prompt length + generation length) during long context generation. And the size KV cache is usually significantly larger than the model parameters, contrains the inference throughput. H2O identifies the critical KV pairs and evicts other unnecessary ones, maintaining a small cache size thus improving the throughput.
 
-Besides, LLMs usually have poor generation to long sequence during inference. H2O handles this issue by maintaining only heavy-hitter tokens and the most recent tokens. Incorporated with the positional rolling strategy (reassigning the position of each kv with the position in the kv cache instead of the original sequence), H2O can process sequence length much longer than the pretrained context window.
+Besides, LLMs usually have poor generation to long sequence during inference. H2O handles this issue by maintaining only heavy-hitter tokens and the most recent tokens. Incorporated with the positional rolling strategy (reassigning the position of each kv with the position in the kv cache instead of the original sequence), H2O can process sequence length much longer than the pretrained context window. Different from other approaches, like [Positional Interpolation](https://arxiv.org/abs/2306.15595), H2O is a KV cache policy and do not involve any training process for long context processing.
 
 Current implementation supports llama-1/2/3, from 7B to 70B. Since H2O only maintains the most important KV pairs, it might missing some important information in the middle content for some knowlege-intensive tasks.
 
@@ -77,7 +77,7 @@ python utils/needle_test/vis.py \
 
 ### One Demo on Streaming to "Infinite" Context Length
 
-The following example demonstrates the generation process of "infinite" sequence length. We use MT-Bench data and generate the context sample-by-sample. The KV Cache will keep the KV pairs from the previous samples while maintain a fixed size.
+The following example demonstrates the generation process of "infinite" sequence length. We use MT-Bench data and generate the context sample-by-sample. The KV Cache will keep the KV pairs from the previous samples while maintain a fixed size. Results can be found on [Demo](https://allenz.work/?p=11) (Video 1).
 
 ```
 # run with full cache