chore: import upstream snapshot with attribution

doc/source/data/doc_code/working-with-llms/minimal_quickstart.py

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+"""
+Quickstart: vLLM + Ray Data batch inference.
+
+1. Installation
+2. Dataset creation
+3. Processor configuration
+4. Running inference
+5. Getting results
+"""
+
+# __minimal_vllm_quickstart_start__
+import ray
+from ray.data.llm import vLLMEngineProcessorConfig, build_processor
+
+# Initialize Ray
+ray.init()
+
+# simple dataset
+ds = ray.data.from_items([
+    {"prompt": "What is machine learning?"},
+    {"prompt": "Explain neural networks in one sentence."},
+])
+
+# Minimal vLLM configuration
+config = vLLMEngineProcessorConfig(
+    model_source="unsloth/Llama-3.1-8B-Instruct",
+    concurrency=1,  # 1 vLLM engine replica
+    batch_size=32,  # 32 samples per batch
+    engine_kwargs={
+        "max_model_len": 4096, # Fit into test GPU memory
+    }
+)
+
+# Build processor
+# preprocess: converts input row to format expected by vLLM (OpenAI chat format)
+# postprocess: extracts generated text from vLLM output
+processor = build_processor(
+    config,
+    preprocess=lambda row: {
+        "messages": [{"role": "user", "content": row["prompt"]}],
+        "sampling_params": {"temperature": 0.7, "max_tokens": 100},
+    },
+    postprocess=lambda row: {
+        "prompt": row["prompt"],
+        "response": row["generated_text"],
+    },
+)
+
+# inference
+ds = processor(ds)
+
+# iterate through the results
+for result in ds.iter_rows():
+    print(f"Q: {result['prompt']}")
+    print(f"A: {result['response']}\n")
+
+# Alternative ways to get results:
+# results = ds.take(10)  # Get first 10 results
+# ds.show(limit=5)       # Print first 5 results
+# ds.write_parquet("output.parquet")  # Save to file
+# __minimal_vllm_quickstart_end__
+