[doc] fix doc typo (hpcaitech#5256)

* [doc] fix annotation display

* [doc] fix llama2 doc

colossalai/shardformer/README.md

@@ -116,18 +116,18 @@ We will follow this roadmap to develop Shardformer:
 
 | model |   tensor parallel    |  pipeline parallel   |   lazy initialization |  xformer   |  flash attn2 | jit fused operator | fused layernorm |  sequence parallel |  overlap |
 | :------: | :-----: | :-----: | :--------: | :---------: | :------: | :-----: | :-----: | :--------: | :---------: |
-| bert |   [x]   |  [x]   |   [x] |  [x]   |  [x] | [x] | [x] |  [x] |  [x] |
-| t5 |   [x]   |  [x]   |   [x] |  [x]   |  [x] | [x] | [x] |  [ ] |  [ ] |
-| llama V1/V2 |   [x]   |  [x]   |   [x] |  [x]   |  [x] | [x] | [x] |  [ ] |  [ ] |
-| gpt2 |   [x]   |  [x]   |   [x] |  [x]   |  [x] | [x] | [x] |  [x] |  [x] |
-| opt |   [x]   |  [x]   |   [x] |  [x]   |  [x] | [x] | [x] |  [ ] |  [ ] |
-| bloom |   [x]   |  [x]   |   [x] |  [x]   |  [x] | [x] | [x] |  [x] |  [x] |
-| chatglm2 |   [x]   |  [x]   |   [x] |  [x]   |  [x] | [x] | [x] |  [x] |  [x] |
-| vit |   [x]   |  [x]   |   [ ] |  [x]   |  [x] | [x] | [x] |  [ ] |  [ ] |
-| whisper |   [x]   |  [x]   |   [x] |  [x]   |  [x] | [ ] | [x] |  [ ] |  [ ] |
-| sam |   [x]   |  [ ]   |   [ ] |  [x]   |  [x] | [x] | [x] |  [ ] |  [ ] |
-| blip2 |   [x]   |  [ ]   |   [ ] |  [x]   |  [x] | [x] | [x] |  [ ] |  [ ] |
-| falcon |   [x]   |  [x]   |   [x] |  [x]   |  [x] | [ ] | [x] |  [ ] |  [ ] |
+| bert |   [√]   |  [√]   |   [√] |  [√]   |  [√] | [√] | [√] |  [√] |  [√] |
+| t5 |   [√]   |  [√]   |   [√] |  [√]   |  [√] | [√] | [√] |  [ ] |  [ ] |
+| llama V1/V2 |   [√]   |  [√]   |   [√] |  [√]   |  [√] | [√] | [√] |  [ ] |  [ ] |
+| gpt2 |   [√]   |  [√]   |   [√] |  [√]   |  [√] | [√] | [√] |  [√] |  [√] |
+| opt |   [√]   |  [√]   |   [√] |  [√]   |  [√] | [√] | [√] |  [ ] |  [ ] |
+| bloom |   [√]   |  [√]   |   [√] |  [√]   |  [√] | [√] | [√] |  [√] |  [√] |
+| chatglm2 |   [√]   |  [√]   |   [√] |  [√]   |  [√] | [√] | [√] |  [√] |  [√] |
+| vit |   [√]   |  [√]   |   [ ] |  [√]   |  [√] | [√] | [√] |  [ ] |  [ ] |
+| whisper |   [√]   |  [√]   |   [√] |  [√]   |  [√] | [ ] | [√] |  [ ] |  [ ] |
+| sam |   [√]   |  [ ]   |   [ ] |  [√]   |  [√] | [√] | [√] |  [ ] |  [ ] |
+| blip2 |   [√]   |  [ ]   |   [ ] |  [√]   |  [√] | [√] | [√] |  [ ] |  [ ] |
+| falcon |   [√]   |  [√]   |   [√] |  [√]   |  [√] | [ ] | [√] |  [ ] |  [ ] |
 | roberta |   [ ]   |  [ ]   |   [ ] |  [ ]   |  [ ] | [ ] | [ ] |  [ ] |  [ ] |
 | albert |   [ ]   |  [ ]   |   [ ] |  [ ]   |  [ ] | [ ] | [ ] |  [ ] |  [ ] |
 | ernie |   [ ]   |  [ ]   |   [ ] |  [ ]   |  [ ] | [ ] | [ ] |  [ ] |  [ ] |
@@ -137,7 +137,7 @@ We will follow this roadmap to develop Shardformer:
 | swin |   [ ]   |  [ ]   |   [ ] |  [ ]   |  [ ] | [ ] | [ ] |  [ ] |  [ ] |
 | swin V2 |   [ ]   |  [ ]   |   [ ] |  [ ]   |  [ ] | [ ] | [ ] |  [ ] |  [ ] |
 | qwen |   [ ]   |  [ ]   |   [ ] |  [ ]   |  [ ] | [ ] | [ ] |  [ ] |  [ ] |
-| mistral |   [x]   |  [ ]   |   [ ] |  [x]   |  [x] | [x] | [x] |  [ ] |  [ ] |
+| mistral |   [√]   |  [ ]   |   [ ] |  [√]   |  [√] | [√] | [√] |  [ ] |  [ ] |
 
 
 ## 💡 API Design

examples/language/llama2/README.md

@@ -6,7 +6,6 @@
 </p>
 
 - 70 billion parameter LLaMA2 model training accelerated by 195%
-[[code]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/llama2)
 [[blog]](https://www.hpc-ai.tech/blog/70b-llama2-training)
 
 ### LLaMA1
@@ -15,7 +14,6 @@
 </p>
 
 - 65-billion-parameter large model pretraining accelerated by 38%
-[[code]](https://github.com/hpcaitech/ColossalAI/tree/example/llama/examples/language/llama)
 [[blog]](https://www.hpc-ai.tech/blog/large-model-pretraining)
 
 ## Dataset
@@ -123,7 +121,7 @@ Here we will show an example of how to run training
 llama pretraining with `gemini, batch_size=16, sequence_length=4096, gradient_checkpoint=True, flash_attn=True`.
 
 #### a. Running environment
-This experiment was performed on 4 computing nodes with 32 A800 GPUs in total for LLaMA-1 65B. The nodes are
+This experiment was performed on 4 computing nodes with 32 A800/H800 80GB GPUs in total for LLaMA-1 65B or LLaMA-2 70B. The nodes are
 connected with RDMA and GPUs within one node are fully connected with NVLink.
 
 #### b. Running command