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Revert "fix some typos" (#6244)

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Lianmin Zheng
2025-05-12 12:53:26 -07:00
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parent bad7c26fdc
commit e8e18dcdcc
95 changed files with 276 additions and 276 deletions
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docs/start/install.md
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@@ -23,7 +23,7 @@ uv pip install "sglang[all]>=0.4.6.post3"
1. Use `export CUDA_HOME=/usr/local/cuda-<your-cuda-version>` to set the `CUDA_HOME` environment variable.
2. Install FlashInfer first following [FlashInfer installation doc](https://docs.flashinfer.ai/installation.html), then install SGLang as described above.
- If you encounter `ImportError; cannot import name 'is_valid_list_of_images' from 'transformers.models.llama.image_processing_llama'`, try to use the specified version of `transformers` in [pyproject.toml](https://github.com/sgl-project/sglang/blob/main/python/pyproject.toml). Currently, run `pip install transformers==4.51.1`.
- If you encounter `ImportError; cannot import name 'is_valid_list_of_images' from 'transformers.models.llama.image_processing_llama'`, try to use the specified version of `transformers` in [pyproject.toml](https://github.com/sgl-project/sglang/blob/main/python/pyproject.toml). Currently, just running `pip install transformers==4.51.1`.
## Method 2: From source
@@ -54,10 +54,10 @@ cd ..
pip install -e "python[all_hip]"
```
## Method 3: Using Docker
## Method 3: Using docker
The docker images are available on Docker Hub as [lmsysorg/sglang](https://hub.docker.com/r/lmsysorg/sglang/tags), built from [Dockerfile](https://github.com/sgl-project/sglang/tree/main/docker).
Replace `<secret>` below with your HuggingFace hub [token](https://huggingface.co/docs/hub/en/security-tokens).
Replace `<secret>` below with your huggingface hub [token](https://huggingface.co/docs/hub/en/security-tokens).
```bash
docker run --gpus all \
@@ -89,7 +89,7 @@ drun -p 30000:30000 \
drun v0.4.6.post3-rocm630 python3 -m sglang.bench_one_batch --batch-size 32 --input 1024 --output 128 --model amd/Meta-Llama-3.1-8B-Instruct-FP8-KV --tp 8 --quantization fp8
```
## Method 4: Using Docker Compose
## Method 4: Using docker compose
<details>
<summary>More</summary>
@@ -164,4 +164,4 @@ sky status --endpoint 30000 sglang
- [FlashInfer](https://github.com/flashinfer-ai/flashinfer) is the default attention kernel backend. It only supports sm75 and above. If you encounter any FlashInfer-related issues on sm75+ devices (e.g., T4, A10, A100, L4, L40S, H100), please switch to other kernels by adding `--attention-backend triton --sampling-backend pytorch` and open an issue on GitHub.
- If you only need to use OpenAI models with the frontend language, you can avoid installing other dependencies by using `pip install "sglang[openai]"`.
- The language frontend operates independently of the backend runtime. You can install the frontend locally without needing a GPU, while the backend can be set up on a GPU-enabled machine. To install the frontend, run `pip install sglang`, and for the backend, use `pip install sglang[srt]`. `srt` is the abbreviation of SGLang runtime.
- To reinstall FlashInfer locally, use the following command: `pip install "flashinfer-python==0.2.5" -i https://flashinfer.ai/whl/cu124/torch2.6 --force-reinstall --no-deps` and then delete the cache with `rm -rf ~/.cache/flashinfer`.
- To reinstall flashinfer locally, use the following command: `pip install "flashinfer-python==0.2.5" -i https://flashinfer.ai/whl/cu124/torch2.6 --force-reinstall --no-deps` and then delete the cache with `rm -rf ~/.cache/flashinfer`.