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[docs] fix misspellings & typos (#18276)

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rinbaro
2026-02-04 19:35:29 -08:00
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parent c8212b9fac
commit de6a03260f
18 changed files with 23 additions and 23 deletions
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2
docs/references/environment_variables.md
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@@ -44,7 +44,7 @@ SGLang supports various environment variables that can be used to configure its
| `SGLANG_MM_PRECOMPUTE_HASH` | Enable precomputing of hash values for MultimodalDataItem | `false` |
| `SGLANG_NCCL_ALL_GATHER_IN_OVERLAP_SCHEDULER_SYNC_BATCH` | Enable NCCL for gathering when preparing mlp sync batch under overlap scheduler (without this flag gloo is used for gathering) | `false` |
| `SGLANG_SYMM_MEM_PREALLOC_GB_SIZE` | Size of preallocated GPU buffer (in GB) for NCCL symmetric memory pool to limit memory fragmentation. Only have an effect when server arg `--enable-symm-mem` is set. | `4` |
| `SGLANG_CUSTOM_ALLREDUCE_ALGO` | The algorithom of custom all-reduce. Set to `oneshot` or `1stage` to force use one-shot. Set to `twoshot` or `2stage` to force use two-shot. | `` |
| `SGLANG_CUSTOM_ALLREDUCE_ALGO` | The algorithm of custom all-reduce. Set to `oneshot` or `1stage` to force use one-shot. Set to `twoshot` or `2stage` to force use two-shot. | `` |
## DeepGEMM Configuration (Advanced Optimization)

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docs/references/multi_node_deployment/lws_pd/lws_pd_deploy.md
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@@ -638,7 +638,7 @@ kubectl apply -f p.yaml
kubectl apply -f d.yaml
```
At this point, we have completed the deployment of the 1P1D SGlang engine part.
At this point, we have completed the deployment of the 1P1D SGLang engine part.
To allow our users to directly experience the model API, we still need a load balancer to handle sequential calls between prefill and decode. Different companies implement LBs differently, and the community will also officially release a new LB component written in Rust in the near future.

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docs/references/post_training_integration.md
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@@ -6,7 +6,7 @@ What makes SGLang essential for post-training?
- Open-To-Use Refit Functionality: diverse method for colocate or disaggregate
- Easy To Postpone Generation: enable partial rollout and dedicated rollout control
- Fine-Grained Engine Sleep And Wake Up: facilitate maxium-powered rollout and training
- Fine-Grained Engine Sleep And Wake Up: facilitate maximum-powered rollout and training
- Training Serving Alignment: ensure the performance consistency in training and serving
- Load Balancing Router: cache-aware load-balancing for high-throughput rollout
- Deterministic Inference: ensure zero kl divergence between rollout and training
@@ -28,4 +28,4 @@ These capabilities, combined with native integration support across major framew
## Collaboration
Due to the privacy of the design parternes, we cannot list the companies that adopt SGLang for post-training. However, we are happy to share the details with you if you are interested and trust the choice among 10+ top companies and frontier labs across US and China. If you are interested in integrating SGLang with your training framework or need technical support, we're here to help! Reach out to us at **rl_team@lmsys.org** for partnerships, integration guidance, and custom feature development.
Due to the privacy of the design partners, we cannot list the companies that adopt SGLang for post-training. However, we are happy to share the details with you if you are interested and trust the choice among 10+ top companies and frontier labs across US and China. If you are interested in integrating SGLang with your training framework or need technical support, we're here to help! Reach out to us at **rl_team@lmsys.org** for partnerships, integration guidance, and custom feature development.

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docs/references/production_request_trace.md
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@@ -1,6 +1,6 @@
# Production Request Tracing
SGlang exports request trace data based on the OpenTelemetry Collector. You can enable tracing by adding the `--enable-trace` and configure the OpenTelemetry Collector endpoint using `--otlp-traces-endpoint` when launching the server.
SGLang exports request trace data based on the OpenTelemetry Collector. You can enable tracing by adding the `--enable-trace` and configure the OpenTelemetry Collector endpoint using `--otlp-traces-endpoint` when launching the server.
You can find example screenshots of the visualization in https://github.com/sgl-project/sglang/issues/8965.