Add vLLM and Transformers usage snippets

README.md

@@ -132,11 +132,56 @@ Thanks to support from Ollama and the mlx-lm team...
 
 #### vLLM
 
-[...]
+Serve Laguna XS.2 locally with vLLM and query it from any OpenAI-compatible client (see [Controlling reasoning](#controlling-reasoning) for tool calls, streaming, and reasoning extraction):
+
+```shell
+pip install "vllm>=<PENDING_VERSION>"
+
+vllm serve poolside/Laguna-XS.2 \
+    --max-model-len 131072 \
+    --default-chat-template-kwargs '{"enable_thinking": true}'
+```
 
 #### Transformers
 
-[...]
+> Requires `transformers >= <PENDING_VERSION>` (Laguna support is on the upcoming release; until then install from source).
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "poolside/Laguna-XS.2"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+messages = [
+    {"role": "user", "content": "Write a Python retry wrapper with exponential backoff."},
+]
+
+# Reasoning is on by default; pass enable_thinking=False to skip the <think> block.
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt",
+    enable_thinking=True,
+).to(model.device)
+
+outputs = model.generate(
+    inputs,
+    max_new_tokens=1024,
+    do_sample=True,
+    temperature=0.7,
+    top_k=20,
+)
+
+response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
+print(response)
+```
 
 #### [Other frameworks]