fix cross-reference error and spelling mistakes (#4101)

Signed-off-by: samzong <samzong.lu@gmail.com>

docs/backend/server_arguments.md

@@ -15,7 +15,7 @@ python -m sglang_router.launch_server --model-path meta-llama/Meta-Llama-3-8B-In
 ```
 python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --mem-fraction-static 0.7
 ```
-- See [hyperparameter tuning](../references/hyperparameter_tuning.md) on tuning hyperparameters for better performance.
+- See [hyperparameter tuning](hyperparameter_tuning.md) on tuning hyperparameters for better performance.
 - If you see out-of-memory errors during prefill for long prompts, try to set a smaller chunked prefill size.
 ```
 python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --chunked-prefill-size 4096
@@ -24,7 +24,7 @@ python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct
 - To enable torchao quantization, add `--torchao-config int4wo-128`. It supports other [quantization strategies (INT8/FP8)](https://github.com/sgl-project/sglang/blob/v0.3.6/python/sglang/srt/server_args.py#L671) as well.
 - To enable fp8 weight quantization, add `--quantization fp8` on a fp16 checkpoint or directly load a fp8 checkpoint without specifying any arguments.
 - To enable fp8 kv cache quantization, add `--kv-cache-dtype fp8_e5m2`.
-- If the model does not have a chat template in the Hugging Face tokenizer, you can specify a [custom chat template](../references/custom_chat_template.md).
+- If the model does not have a chat template in the Hugging Face tokenizer, you can specify a [custom chat template](custom_chat_template.md).
 
 - To run tensor parallelism on multiple nodes, add `--nnodes 2`. If you have two nodes with two GPUs on each node and want to run TP=4, let `sgl-dev-0` be the hostname of the first node and `50000` be an available port, you can use the following commands. If you meet deadlock, please try to add `--disable-cuda-graph`
 ```
@@ -101,7 +101,7 @@ Please consult the documentation below to learn more about the parameters you ma
 
 * `stream_interval`: Interval (in tokens) for streaming responses. Smaller values lead to smoother streaming, and larger values lead to better throughput.
 * `random_seed`: Can be used to enforce more deterministic behavior.
-* `watchdog_timeout`: Adjusts the watchdog thread’s timeout before killing the server if batch generation takes too long.
+* `watchdog_timeout`: Adjusts the watchdog thread's timeout before killing the server if batch generation takes too long.
 * `download_dir`: Use to override the default Hugging Face cache directory for model weights.
 * `base_gpu_id`: Use to adjust first GPU used to distribute the model across available GPUs.
 * `allow_auto_truncate`: Automatically truncate requests that exceed the maximum input length.
@@ -117,14 +117,14 @@ Please consult the documentation below to learn more about the parameters you ma
 
 ## Multi-node distributed serving
 
-* `dist_init_addr`: The TCP address used for initializing PyTorch’s distributed backend (e.g. `192.168.0.2:25000`).
+* `dist_init_addr`: The TCP address used for initializing PyTorch's distributed backend (e.g. `192.168.0.2:25000`).
 * `nnodes`: Total number of nodes in the cluster. Refer to how to run the [Llama 405B model](https://docs.sglang.ai/references/llama_405B.html#run-405b-fp16-on-two-nodes).
 * `node_rank`: Rank (ID) of this node among the `nnodes` in the distributed setup.
 
 
 ## LoRA
 
-* `lora_paths`: You may provide a list of adapters to your model as a list. Each batch element will get model response with the corresponding lora adapter applied. Currently `cuda_graph` and `radix_attention` are not supportet with this option so you need to disable them manually. We are still working on through these [issues](https://github.com/sgl-project/sglang/issues/2929).
+* `lora_paths`: You may provide a list of adapters to your model as a list. Each batch element will get model response with the corresponding lora adapter applied. Currently `cuda_graph` and `radix_attention` are not supported with this option so you need to disable them manually. We are still working on through these [issues](https://github.com/sgl-project/sglang/issues/2929).
 * `max_loras_per_batch`: Maximum number of LoRAs in a running batch including base model.
 * `lora_backend`: The backend of running GEMM kernels for Lora modules, can be one of `triton` or `flashinfer`. Defaults to be `triton`.
 

docs/references/deepseek.md

@@ -57,7 +57,7 @@ Detailed commands for reference:
 
 ### Download Weights
 
-If you encounter errors when starting the server, ensure the weights have finished downloading. It's recommended to download them beforehand or restart multiple times until all weights are downloaded. Please refer to [DeepSeek V3]([https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#installation--launch](https://huggingface.co/deepseek-ai/DeepSeek-V3-Base#61-inference-with-deepseek-infer-demo-example-only)) offical guide to download the weights.
+If you encounter errors when starting the server, ensure the weights have finished downloading. It's recommended to download them beforehand or restart multiple times until all weights are downloaded. Please refer to [DeepSeek V3](https://huggingface.co/deepseek-ai/DeepSeek-V3-Base#61-inference-with-deepseek-infer-demo-example-only) official guide to download the weights.
 
 ### Caching `torch.compile`