chore: import upstream snapshot with attribution

tutorials/developer-docs/python-api.md

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+# LitGPT High-level Python API
+
+This is a work-in-progress draft for a high-level LitGPT Python API.
+
+ 
+## Model loading & saving
+
+The `LLM.load` command loads an `llm` object, which contains both the model object (a PyTorch module) and a preprocessor.
+
+```python
+from litgpt import LLM
+
+llm = LLM.load(
+    model="url | local_path",
+    # high-level user only needs to care about those:
+    memory_reduction="none | medium | strong"
+    # advanced options for technical users:
+    source="hf | local | other"
+    quantize="bnb.nf4",
+    precision="bf16-true",
+    device=""auto | cuda | cpu",
+)
+```
+
+Here,
+
+-  `llm.model` contains the PyTorch Module
+- and `llm.preprocessor.tokenizer`  contains the tokenizer
+
+The `llm.save` command saves the model weights, tokenizer, and configuration information.
+
+
+```python
+llm.save(checkpoint_dir, format="lightning | ollama | hf")
+```
+
+
+ 
+## Inference / Chat
+
+```
+response = llm.generate(
+    prompt="What do Llamas eat?",
+    temperature=0.1,
+    top_p=0.8,
+    ...
+)
+```
+
+
+ 
+## Dataset
+
+The `llm.prepare_dataset` command prepares a dataset for training.
+
+```
+llm.download_dataset(
+    URL,
+    ...
+)
+```
+
+```
+dataset = llm.prepare_dataset(
+    path,
+    task="pretrain | instruction_finetune",
+    test_portion=0.1,
+    ...
+)
+```
+
+ 
+## Training
+
+
+```python
+llm.instruction_finetune(
+    config=None,
+    dataset=dataset,
+    max_iter=10,
+    method="full | lora | adapter | adapter_v2"
+)
+```
+
+```python
+llm.pretrain(config=None, dataset=dataset, max_iter=10, ...)
+```
+
+ 
+## Serving
+
+
+```python
+llm.serve(port=8000)
+```
+
+Then in another Python session:
+
+```python
+import requests, json
+
+response = requests.post(
+    "http://127.0.0.1:8000/predict",
+    json={"prompt": "Fix typos in the following sentence: Example input"}
+)
+
+print(response.json()["output"])
+```