chore: vendor sglang v0.5.10 snapshot

third_party/sglang/docs/basic_usage/ollama_api.md

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+# Ollama-Compatible API
+
+SGLang provides Ollama API compatibility, allowing you to use the Ollama CLI and Python library with SGLang as the inference backend.
+
+## Prerequisites
+
+```bash
+# Install the Ollama Python library (for Python client usage)
+pip install ollama
+```
+
+> **Note**: You don't need the Ollama server installed - SGLang acts as the backend. You only need the `ollama` CLI or Python library as the client.
+
+## Endpoints
+
+| Endpoint | Method | Description |
+|----------|--------|-------------|
+| `/` | GET, HEAD | Health check for Ollama CLI |
+| `/api/tags` | GET | List available models |
+| `/api/chat` | POST | Chat completions (streaming & non-streaming) |
+| `/api/generate` | POST | Text generation (streaming & non-streaming) |
+| `/api/show` | POST | Model information |
+
+## Quick Start
+
+### 1. Launch SGLang Server
+
+```bash
+python -m sglang.launch_server \
+    --model Qwen/Qwen2.5-1.5B-Instruct \
+    --port 30001 \
+    --host 0.0.0.0
+```
+
+> **Note**: The model name used with `ollama run` must match exactly what you passed to `--model`.
+
+### 2. Use Ollama CLI
+
+```bash
+# List available models
+OLLAMA_HOST=http://localhost:30001 ollama list
+
+# Interactive chat
+OLLAMA_HOST=http://localhost:30001 ollama run "Qwen/Qwen2.5-1.5B-Instruct"
+```
+
+If connecting to a remote server behind a firewall:
+
+```bash
+# SSH tunnel
+ssh -L 30001:localhost:30001 user@gpu-server -N &
+
+# Then use Ollama CLI as above
+OLLAMA_HOST=http://localhost:30001 ollama list
+```
+
+### 3. Use Ollama Python Library
+
+```python
+import ollama
+
+client = ollama.Client(host='http://localhost:30001')
+
+# Non-streaming
+response = client.chat(
+    model='Qwen/Qwen2.5-1.5B-Instruct',
+    messages=[{'role': 'user', 'content': 'Hello!'}]
+)
+print(response['message']['content'])
+
+# Streaming
+stream = client.chat(
+    model='Qwen/Qwen2.5-1.5B-Instruct',
+    messages=[{'role': 'user', 'content': 'Tell me a story'}],
+    stream=True
+)
+for chunk in stream:
+    print(chunk['message']['content'], end='', flush=True)
+```
+
+## Smart Router
+
+For intelligent routing between local Ollama (fast) and remote SGLang (powerful) using an LLM judge, see the [Smart Router documentation](https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/entrypoints/ollama/README.md).
+
+## Summary
+
+| Component | Purpose |
+|-----------|---------|
+| **Ollama API** | Familiar CLI/API that developers already know |
+| **SGLang Backend** | High-performance inference engine |
+| **Smart Router** | Intelligent routing - fast local for simple tasks, powerful remote for complex tasks |