Update LLM documentation

Author: jackzhxng

docs/source/llm/build-run-llama3-qualcomm-ai-engine-direct-backend.md

@@ -13,37 +13,57 @@ This tutorial demonstrates how to export Llama 3 8B Instruct for Qualcomm AI Eng
 
 ## Instructions
 
-### Step1: Prepare the checkpoint of the model and optimized matrix from [Spin Quant](https://github.com/facebookresearch/SpinQuant)
+### Step 1: Prepare the checkpoint of the model and optimized matrix from [Spin Quant](https://github.com/facebookresearch/SpinQuant)
 
 1. For Llama 3 tokenizer and checkpoint, please refer to https://github.com/meta-llama/llama-models/blob/main/README.md for further instructions on how to download `tokenizer.model`, `consolidated.00.pth` and `params.json`.
 2. To get the optimized matrix, please refer to [SpinQuant on GitHub](https://github.com/facebookresearch/SpinQuant). You can download the optimized rotation matrices in the Quantized Models section. Please choose **LLaMA-3-8B/8B_W4A16KV16_lr_1.5_seed_0**.
 
-### Step2: Export to ExecuTorch with Qualcomm AI Engine Direct Backend
+### Step 2: Export to ExecuTorch with Qualcomm AI Engine Direct Backend
 Deploying large language models like Llama 3 on-device presents the following challenges:
 
 1. The model size is too large to fit in device memory for inference.
 2. High model loading and inference time.
 3. Difficulty in quantization.
 
 To address these challenges, we have implemented the following solutions:
-1. Using `--pt2e_quantize qnn_16a4w` to quantize activations and weights, thereby reducing the on-disk model size and alleviating memory pressure during inference.
-2. Using `--num_sharding 8` to shard the model into sub-parts.
+1. Using `quantization.pt2e_quantize = "qnn_16a4w'` to quantize activations and weights, thereby reducing the on-disk model size and alleviating memory pressure during inference.
+2. Using `backed.qnn.num_sharding = 8` to shard the model into sub-parts.
 3. Performing graph transformations to convert or decompose operations into more accelerator-friendly operations.
-4. Using `--optimized_rotation_path <path_to_optimized_matrix>` to apply R1 and R2 of [Spin Quant](https://github.com/facebookresearch/SpinQuant) to improve accuracy.
-5. Using `--calibration_data "<|start_header_id|>system<|end_header_id|..."` to ensure that during the quantization of Llama 3 8B instruct, the calibration includes special tokens in the prompt template. For more details on the prompt template, refer to [the model card of meta llama3 instruct](https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/).
+4. Using `backend.qnn.optimized_rotation_path = "<path_to_optimized_matrix>"` to apply R1 and R2 of [Spin Quant](https://github.com/facebookresearch/SpinQuant) to improve accuracy.
+5. Using `quantization.calibration_data = "<|start_header_id|>system<|end_header_id|..."` to ensure that during quantization, the calibration includes special tokens in the prompt template. For more details on the prompt template, refer to [the model card](https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/).
 
-To export Llama 3 8B instruct with the Qualcomm AI Engine Direct Backend, ensure the following:
+To export with the Qualcomm AI Engine Direct Backend, ensure the following:
 
 1. The host machine has more than 100GB of memory (RAM + swap space).
 2. The entire process takes a few hours.
 
 ```bash
-# Please note that calibration_data must include the prompt template for special tokens.
-python -m examples.models.llama.export_llama -t <path_to_tokenizer.model>
-llama3/Meta-Llama-3-8B-Instruct/tokenizer.model -p <path_to_params.json> -c <path_to_checkpoint_for_Meta-Llama-3-8B-Instruct>  --use_kv_cache  --qnn --pt2e_quantize qnn_16a4w --disable_dynamic_shape --num_sharding 8 --calibration_tasks wikitext --calibration_limit 1 --calibration_seq_length 128 --optimized_rotation_path <path_to_optimized_matrix> --calibration_data "<|start_header_id|>system<|end_header_id|>\n\nYou are a funny chatbot.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nCould you tell me about Facebook?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+# path/to/config.yaml
+base:
+  model_class: llama3
+  checkpoint: path/to/consolidated.00.pth
+  params: path/to/params.json
+  tokenizer_path: path/to/tokenizer.model
+  metadata: '{"get_bos_id":128000, "get_eos_ids":[128009, 128001]}'
+model:
+  use_kv_cache: True
+  enable_dynamic_shape: False
+quantization:
+  pt2e_quantize: qnn_16a4w
+  # Please note that calibration_data must include the prompt template for special tokens.
+  calibration_data: "<|start_header_id|>system<|end_header_id|>\n\nYou are a funny chatbot.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nCould you tell me about Facebook?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+backend:
+  qnn:
+    enabled: True
+    num_sharding: 8
+    
+
+# export_llm
+python -m extension.llm.export.export_llm \
+  --config path/to/config.yaml
 ```
 
-### Step3: Invoke the Runtime on an Android smartphone with Qualcomm SoCs
+### Step 3: Invoke the Runtime on an Android smartphone with Qualcomm SoCs
 1. Build executorch with Qualcomm AI Engine Direct Backend for android
     ```bash
     cmake \
@@ -116,9 +136,9 @@ You should see the message:
 ```
 
 ## What is coming?
-- Improve the performance for Llama 3 Instruct
+- Performance improvements
 - Reduce the memory pressure during inference to support 12GB Qualcomm devices
-- Support more LLMs
+- Support more LLMs (Qwen, Phi-4-mini, etc.)
 
 ## FAQ