update usage for sparse attention

README.md

@@ -78,10 +78,17 @@ MiniCPM4.1 adopts sparse attention and speculative decoding to improve the infer
 ## Usage
 MiniCPM 4.1 can be used with following frameworks: Huggingface Transformers, SGLang, vLLM, and CPM.cu. For the ultimate inference speed, we highly recommend CPM.cu.
 
+MiniCPM4/MiniCPM4.1 supports both dense attention inference and sparse attention inference modes, where vLLM and SGLang currently only support dense inference mode. If you want to use sparse inference mode, please use Huggingface Transformers and CPM.cu.
+
+- Dense attention inference: vLLM, SGLang, Huggingface Transformers
+- Sparse attention inference: Huggingface Transformers, CPM.cu
+
+**To facilitate researches in sparse attention, we provide [InfLLM-V2 Training Kernels](https://github.com/OpenBMB/infllmv2_cuda_impl) and [InfLLM-V2 Inference Kernels](https://github.com/openbmb/cpm.cu).**
 
 ### Inference with Transformers
 MiniCPM4.1-8B requires `transformers>=4.56`.
 
+- **Inference with Dense Attention**
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
@@ -121,6 +128,7 @@ responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0
 print(responses)
 ```
 
+- **Inference with Sparse Attention**
 MiniCPM4.1-8B supports `InfLLM v2`, a sparse attention mechanism designed for efficient long-sequence inference. It requires the [infllmv2_cuda_impl](https://github.com/OpenBMB/infllmv2_cuda_impl) library.
 
 You can install it by running the following command:
@@ -158,6 +166,7 @@ These parameters control the behavior of InfLLM v2:
 * `use_nope` (default: false): Whether to use the NOPE technique in block selection for improved performance.
 * `dense_len` (default: 8192): Since Sparse Attention offers limited benefits for short sequences, the model can use standard (dense) attention for shorter texts. The model will use dense attention for sequences with a token length below `dense_len` and switch to sparse attention for sequences exceeding this length. Set this to `-1` to always use sparse attention regardless of sequence length.
 
+- **Long Context Extension**
 MiniCPM4.1 natively supports context lengths of up to 65,536(64k) tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques for effective handling of long texts. We have validated the model's performance on context lengths of up to 131,072 tokens by modifying the LongRoPE factor.
 
 You can apply the LongRoPE factor modification by modifying the model files. Specifically, in the `config.json` file, adjust the `rope_scaling` fields.