docs: add deployment guidance for llm fine-tuning examples

examples/llm_finetune/README.md

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+# LLM Fine-Tuning Examples
+
+This directory contains NeMo AutoModel LLM fine-tuning recipes organized by model family. Each subdirectory provides YAML configs for a specific family, such as Llama, Mistral, Qwen, Gemma, Nemotron, and others. The main AutoModel README identifies `examples/llm_finetune/` as the location for LLM fine-tune configs and shows these recipes being launched through the `automodel` CLI.
+
+## Running a Recipe
+
+Set up the environment with `uv`, then launch a recipe with `automodel`:
+
+```bash
+uv venv
+uv sync --frozen
+automodel examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml
+```
+
+To run on multiple GPUs on a single node:
+
+```bash
+automodel examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml --nproc-per-node 8
+```
+
+These commands follow the repository's documented setup and launch pattern.
+
+## Important Note on `finetune.py`
+
+A legacy `finetune.py` entry point exists in this directory, but it is deprecated. The script emits a deprecation warning and explicitly instructs users to launch recipes with:
+
+```bash
+automodel <config.yaml> [--nproc-per-node N]
+```
+
+So new documentation in this directory should prefer `automodel` over `python finetune.py`. This is also consistent with the main README's documented usage. The inspected script loads a config, constructs `TrainFinetuneRecipeForNextTokenPrediction`, then runs `setup()` followed by `run_train_validation_loop()`, which confirms that these examples are training-entry recipes rather than deployment scripts.
+
+## Multi-Node Launches
+
+For SLURM-based multi-node runs, copy the reference `slurm.sub` script, adapt it for your cluster, and submit it with `sbatch`:
+
+```bash
+cp slurm.sub my_cluster.sub
+sbatch my_cluster.sub
+```
+
+Cluster-specific settings such as nodes, GPUs, partition, container, and mounts should be defined in the sbatch script. NeMo-Run sections are also supported through the cluster guide.
+
+## After Fine-Tuning
+
+These recipes are focused on training. After fine-tuning completes, the resulting checkpoints can be used in downstream evaluation, inference, or deployment workflows. The main README also highlights checkpointing and interoperability with Hugging Face and other NeMo ecosystem components. 
+
+## Deployment Guidance
+
+This examples directory does not currently document a single canonical deployment command for all fine-tuned LLM recipes. Based on the materials reviewed here, the safest documented guidance is:
+
+1. **Use the generated checkpoints in your follow-up evaluation or inference workflow.**
+2. **Use AutoModel's documented container workflow** when you want a reproducible GPU-backed environment. The contributing guide documents both the AutoModel container path and a custom Docker build path.
+3. **Refer to the broader NeMo and AutoModel documentation for production deployment architecture**, rather than assuming a serving/export API directly from these training examples. The repository positions AutoModel as part of the broader NeMo ecosystem for scalable training and deployment-oriented environments.
+
+## Development Notes
+
+If you update documentation here, the contributing guide points contributors to the documentation development guide and requires signed-off commits:
+
+```bash
+git commit -s -m "docs: add llm finetune README"
+```
+
+Unsigned commits are not accepted.