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Fix docs (#2164)

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This commit is contained in:
Lianmin Zheng
2024-11-24 08:25:56 -08:00
committed by GitHub
parent be0124bda0
commit 8912b7637f
2 changed files with 18 additions and 21 deletions
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20
README.md
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@@ -41,15 +41,10 @@ The core features include:
- **Active Community**: SGLang is open-source and backed by an active community with industry adoption.
## Getting Started
Install SGLang: See [https://sgl-project.github.io/start/install.html](https://sgl-project.github.io/start/install.html)
Send requests: See [https://sgl-project.github.io/start/send_request.html](https://sgl-project.github.io/start/send_request.html)
## Backend: SGLang Runtime (SRT)
See [https://sgl-project.github.io/backend/backend.html](https://sgl-project.github.io/backend/backend.html)
## Frontend: Structured Generation Language (SGLang)
See [https://sgl-project.github.io/frontend/frontend.html](https://sgl-project.github.io/frontend/frontend.html)
- [Install SGLang](https://sgl-project.github.io/start/install.html)
- [Send requests](https://sgl-project.github.io/start/send_request.html)
- [Backend: SGLang Runtime (SRT)](https://sgl-project.github.io/backend/backend.html)
- [Frontend: Structured Generation Language (SGLang)](https://sgl-project.github.io/frontend/frontend.html)
## Benchmark And Performance
Learn more in our release blogs: [v0.2 blog](https://lmsys.org/blog/2024-07-25-sglang-llama3/), [v0.3 blog](https://lmsys.org/blog/2024-09-04-sglang-v0-3/)
@@ -57,6 +52,9 @@ Learn more in our release blogs: [v0.2 blog](https://lmsys.org/blog/2024-07-25-s
## Roadmap
[Development Roadmap (2024 Q4)](https://github.com/sgl-project/sglang/issues/1487)
## Citation And Acknowledgment
## Adoption and Sponsorship
The project is supported by (alphabetically): AMD, Baseten, Etched, Hyperbolic, Jam & Tea Studios, LinkedIn, NVIDIA, RunPod, Stanford, UC Berkeley, and xAI.
## Acknowledgment and Citation
We learned from the design and reused code from the following projects: [Guidance](https://github.com/guidance-ai/guidance), [vLLM](https://github.com/vllm-project/vllm), [LightLLM](https://github.com/ModelTC/lightllm), [FlashInfer](https://github.com/flashinfer-ai/flashinfer), [Outlines](https://github.com/outlines-dev/outlines), and [LMQL](https://github.com/eth-sri/lmql).
Please cite our paper, [SGLang: Efficient Execution of Structured Language Model Programs](https://arxiv.org/abs/2312.07104), if you find the project useful.
We also learned from the design and reused code from the following projects: [Guidance](https://github.com/guidance-ai/guidance), [vLLM](https://github.com/vllm-project/vllm), [LightLLM](https://github.com/ModelTC/lightllm), [FlashInfer](https://github.com/flashinfer-ai/flashinfer), [Outlines](https://github.com/outlines-dev/outlines), and [LMQL](https://github.com/eth-sri/lmql).

19
docs/backend/offline_engine_api.ipynb
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@@ -39,7 +39,6 @@
"# launch the offline engine\n",
"\n",
"import sglang as sgl\n",
"from sglang.utils import print_highlight\n",
"import asyncio\n",
"\n",
"llm = sgl.Engine(model_path=\"meta-llama/Meta-Llama-3.1-8B-Instruct\")"
@@ -69,8 +68,8 @@
"\n",
"outputs = llm.generate(prompts, sampling_params)\n",
"for prompt, output in zip(prompts, outputs):\n",
" print_highlight(\"===============================\")\n",
" print_highlight(f\"Prompt: {prompt}\\nGenerated text: {output['text']}\")"
" print(\"===============================\")\n",
" print(f\"Prompt: {prompt}\\nGenerated text: {output['text']}\")"
]
},
{
@@ -93,10 +92,10 @@
"]\n",
"sampling_params = {\"temperature\": 0.8, \"top_p\": 0.95}\n",
"\n",
"print_highlight(\"\\n=== Testing synchronous streaming generation ===\")\n",
"print(\"\\n=== Testing synchronous streaming generation ===\")\n",
"\n",
"for prompt in prompts:\n",
" print_highlight(f\"\\nPrompt: {prompt}\")\n",
" print(f\"\\nPrompt: {prompt}\")\n",
" print(\"Generated text: \", end=\"\", flush=True)\n",
"\n",
" for chunk in llm.generate(prompt, sampling_params, stream=True):\n",
@@ -125,15 +124,15 @@
"\n",
"sampling_params = {\"temperature\": 0.8, \"top_p\": 0.95}\n",
"\n",
"print_highlight(\"\\n=== Testing asynchronous batch generation ===\")\n",
"print(\"\\n=== Testing asynchronous batch generation ===\")\n",
"\n",
"\n",
"async def main():\n",
" outputs = await llm.async_generate(prompts, sampling_params)\n",
"\n",
" for prompt, output in zip(prompts, outputs):\n",
" print_highlight(f\"\\nPrompt: {prompt}\")\n",
" print_highlight(f\"Generated text: {output['text']}\")\n",
" print(f\"\\nPrompt: {prompt}\")\n",
" print(f\"Generated text: {output['text']}\")\n",
"\n",
"\n",
"asyncio.run(main())"
@@ -159,12 +158,12 @@
"]\n",
"sampling_params = {\"temperature\": 0.8, \"top_p\": 0.95}\n",
"\n",
"print_highlight(\"\\n=== Testing asynchronous streaming generation ===\")\n",
"print(\"\\n=== Testing asynchronous streaming generation ===\")\n",
"\n",
"\n",
"async def main():\n",
" for prompt in prompts:\n",
" print_highlight(f\"\\nPrompt: {prompt}\")\n",
" print(f\"\\nPrompt: {prompt}\")\n",
" print(\"Generated text: \", end=\"\", flush=True)\n",
"\n",
" generator = await llm.async_generate(prompt, sampling_params, stream=True)\n",