Add StableLM 2 Example Scripts (#1327) [skip ci]

* Add StableLM examples and configurations

* Add FFT and LORA configuration files and modify readme with usage

examples/stablelm-2/README.md

@@ -0,0 +1,36 @@
+# StableLM 2
+
+This repository contains examples for training and processing using StableLM-2. It also includes a section to help you estimate the GPU requirements for your specific use case.
+
+## Estimating GPU Requirements
+
+| type          | deepspeed | batch size | context length | vRAM GPU (GBs) |
+|---------------|-----------|------------|----------------|----------------|
+| full finetune | N/A       | 1          | 4096           | ~21.5GBs       |
+| full finetune | zero2     | 1          | 4096           | ~20GBs         |
+| lora          | N/A       | 1          | 4096           | ~16.6GBs       |
+
+The above are estimates and might differ slight depending on the setup for example whether you pack your sequence lengths or not (the above assumes you do to length 4096).
+
+This blog post from Hamel Husain was a great resource for estimating these numbers: https://hamel.dev/notes/llm/03_estimating_vram.html
+
+## Training
+We have example scripts here for both full finetuning and lora using the popular alpaca dataset:
+
+```shell
+# preprocess the dataset
+CUDA_VISIBLE_DEVICES="" python -m axolotl.cli.preprocess examples/stablelm-2/1.6b/lora.yml
+```
+
+Single GPU Training:
+```shell
+python -m axolotl.cli.train examples/stablelm-2/fft.yml --deepspeed deepspeed_configs/zero2.json
+# OR
+python -m axolotl.cli.train examples/stablelm-2/1.6b/lora.yml
+```
+
+Multinode GPU Training with `accelerate`:
+```shell
+# make sure you've configured accelerate properly
+accelerate launch -m axolotl.cli.train examples/stablelm-2/1.6b/fft.yml --deepspeed deepspeed_configs/zero2.json
+```