Feat: minor docs improvements for RLHF and faq on embeddings (#2401) [skip ci]

* feat: add doc on shrink_embeddings and custom calling

* chore: rename inference doc

* fix: clarify same config is used for all cli

* chore: rearrange order inference qmd

* feat: add simpo to doc

* fix: update defaults

* feat: add rl configs to doc

* fix: ensure beta consistent with trl.beta

* fix: clarify about lora/fft

* chore: rename title

* chore: fix language

* feat: move config reference higher

* Update docs/getting-started.qmd

Co-authored-by: salman <salman.mohammadi@outlook.com>

* Update docs/rlhf.qmd

Co-authored-by: salman <salman.mohammadi@outlook.com>

---------

Co-authored-by: salman <salman.mohammadi@outlook.com>

docs/getting-started.qmd

@@ -36,7 +36,9 @@ The YAML configuration file controls everything about your training. Here's what
 
 ```yaml
 base_model: NousResearch/Llama-3.2-1B
-# hub_model_id: username/custom_model_name
+
+load_in_8bit: true
+adapter: lora
 
 datasets:
   - path: teknium/GPT4-LLM-Cleaned
@@ -44,11 +46,15 @@ datasets:
 dataset_prepared_path: last_run_prepared
 val_set_size: 0.1
 output_dir: ./outputs/lora-out
-
-adapter: lora
-lora_model_dir:
 ```
 
+::: {.callout-tip}
+`load_in_8bit: true` and `adapter: lora` enables LoRA adapter finetuning.
+
+- To perform Full finetuning, remove these two lines.
+- To perform QLoRA finetuning, replace with `load_in_4bit: true` and `adapter: qlora`.
+:::
+
 See our [Config options](config.qmd) for more details.
 
 ### Training {#sec-training}
@@ -56,7 +62,7 @@ See our [Config options](config.qmd) for more details.
 When you run `axolotl train`, Axolotl:
 
 1. Downloads the base model
-2. (If specified) applies LoRA adapter layers
+2. (If specified) applies QLoRA/LoRA adapter layers
 3. Loads and processes the dataset
 4. Runs the training loop
 5. Saves the trained model and / or LoRA weights
@@ -69,6 +75,8 @@ Let's modify the example for your own data:
 
 ```yaml
 base_model: NousResearch/Nous-Hermes-llama-1b-v1
+
+load_in_8bit: true
 adapter: lora
 
 # Training settings
@@ -104,8 +112,6 @@ format):
 {"instruction": "Classify this text", "input": "Not good at all", "output": "negative"}
 ```
 
-Please consult the supported [Dataset Formats](dataset-formats/) for more details.
-
 3. Run the training:
 
 ```bash