[Doc] Add release note for 0.8.4rc1 (#557)

Add release note for 0.8.4rc1, we'll release 0.8.4rc1 now.

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

docs/source/faqs.md

@@ -5,6 +5,7 @@
 - [[v0.7.1rc1] FAQ & Feedback](https://github.com/vllm-project/vllm-ascend/issues/19)
 - [[v0.7.3rc1] FAQ & Feedback](https://github.com/vllm-project/vllm-ascend/issues/267)
 - [[v0.7.3rc2] FAQ & Feedback](https://github.com/vllm-project/vllm-ascend/issues/418)
+- [[v0.8.4rc1] FAQ & Feedback](https://github.com/vllm-project/vllm-ascend/issues/546)
 
 ## General FAQs
 
@@ -66,26 +67,22 @@ import vllm
 
 If all above steps are not working, feel free to submit a GitHub issue.
 
-### 7. Does vllm-ascend support Atlas 300I Duo?
-
-No, vllm-ascend now only supports Atlas A2 series. We are working on it.
-
-### 8. How does vllm-ascend perform?
+### 7. How does vllm-ascend perform?
 
 Currently, only some models are improved. Such as `Qwen2 VL`, `Deepseek  V3`. Others are not good enough. In the future, we will support graph mode and custom ops to improve the performance of vllm-ascend. And when the official release of vllm-ascend is released, you can install `mindie-turbo` with `vllm-ascend` to speed up the inference as well.
 
-### 9. How vllm-ascend work with vllm?
+### 8. How vllm-ascend work with vllm?
 vllm-ascend is a plugin for vllm. Basically, the version of vllm-ascend is the same as the version of vllm. For example, if you use vllm 0.7.3, you should use vllm-ascend 0.7.3 as well. For main branch, we will make sure `vllm-ascend` and `vllm` are compatible by each commit.
 
-### 10. Does vllm-ascend support Prefill Disaggregation feature?
+### 9. Does vllm-ascend support Prefill Disaggregation feature?
 
 Currently, only 1P1D is supported by vllm. For vllm-ascend, it'll be done by [this PR](https://github.com/vllm-project/vllm-ascend/pull/432). For NPND, vllm is not stable and fully supported yet. We will make it stable and supported by vllm-ascend in the future.
 
-### 11. Does vllm-ascend support quantization method?
+### 10. Does vllm-ascend support quantization method?
 
 Currently, there is no quantization method supported in vllm-ascend originally. And the quantization supported is working in progress, w8a8 will firstly be supported.
 
-### 12. How to run w8a8 DeepSeek model?
+### 11. How to run w8a8 DeepSeek model?
 
 Currently, running on v0.7.3, we should run w8a8 with vllm + vllm-ascend + mindie-turbo. And we only need vllm + vllm-ascend when v0.8.X is released. After installing the above packages, you can follow the steps below to run w8a8 DeepSeek:
 
@@ -93,15 +90,15 @@ Currently, running on v0.7.3, we should run w8a8 with vllm + vllm-ascend + mindi
 2. Copy the content of `quant_model_description_w8a8_dynamic.json` into the `quantization_config` of `config.json` of the quantized model files.
 3. Reference with the quantized DeepSeek model.
 
-### 13. There is not output in log when loading models using vllm-ascend, How to solve it?
+### 12. There is not output in log when loading models using vllm-ascend, How to solve it?
 
 If you're using vllm 0.7.3 version, this is a known progress bar display issue in VLLM, which has been resolved in [this PR](https://github.com/vllm-project/vllm/pull/12428), please cherry-pick it locally by yourself. Otherwise, please fill up an issue.
 
-### 14. How vllm-ascend is tested
+### 13. How vllm-ascend is tested
 
-vllm-ascend is tested by functionnal test, performance test and accuracy test.
+vllm-ascend is tested by functional test, performance test and accuracy test.
 
-- **Functionnal test**: we added CI, includes portion of vllm's native unit tests and vllm-ascend's own unit tests，on vllm-ascend's test, we test basic functional usability for popular models, include `Qwen2.5-7B-Instruct`、 `Qwen2.5-VL-7B-Instruct`、`Qwen2.5-VL-32B-Instruct`、`QwQ-32B`.
+- **Functional test**: we added CI, includes portion of vllm's native unit tests and vllm-ascend's own unit tests，on vllm-ascend's test, we test basic functional usability for popular models, include `Qwen2.5-7B-Instruct`、 `Qwen2.5-VL-7B-Instruct`、`Qwen2.5-VL-32B-Instruct`、`QwQ-32B`.
 
 - **Performance test**: we provide [benchmark](https://github.com/vllm-project/vllm-ascend/tree/main/benchmarks) tools for end-to-end performance benchmark which can easily to re-route locally, we'll publish a perf website like [vllm](https://simon-mo-workspace.observablehq.cloud/vllm-dashboard-v0/perf) does to show the performance test results for each pull request