feat: Consolidate llama examples

examples/05_Llama_Examples/README.md

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+# Running Endpoints with Llama Models
+
+This example covers benchmarking two Llama models:
+
+- **[Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)** — CNN/DailyMail summarization, offline and online modes
+- **[Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)** — Open Orca, online (Poisson) mode
+
+---
+
+## Llama-3.1-8B-Instruct
+
+### Dataset
+
+The Llama3.1-8B benchmark uses the [cnn/dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail) dataset. When using the provided config files, the validation split is downloaded automatically by specifying the dataset name as `- name: cnn_dailymail::llama3_8b`.
+
+For post-training quantization calibration, use the repository-provided [`calibration-list.txt`](./calibration-list.txt), which corresponds to the [cnn-dailymail-calibration-list](https://github.com/mlcommons/inference/blob/v4.0/calibration/CNNDailyMail/calibration-list.txt):
+
+```bash
+uv run python download_cnndm.py --save-dir data --calibration-ids-file calibration-list.txt --split train
+```
+
+### Environment
+
+```bash
+export HF_TOKEN=<your Hugging Face token>
+export HF_HOME=<path to your hf_home, e.g. ~/.cache/huggingface>
+export MODEL_NAME=meta-llama/Llama-3.1-8B-Instruct
+hf download $MODEL_NAME
+```
+
+### Launch the server
+
+**Note:** To generate outputs matching MLPerf submissions from legacy loadgen, apply a custom chat template (handled automatically by the `cnn_dailymail::llama3_8b` preset). The `--trust-request-chat-template` flag is required. **Security warning:** this flag allows execution of request-provided chat templates and should only be used in trusted environments. Do not enable it on publicly exposed endpoints.
+
+```bash
+docker run --runtime nvidia --gpus all \
+    -v ${HF_HOME}:/root/.cache/huggingface \
+    --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
+    -p 8000:8000 \
+    --ipc=host \
+    vllm/vllm-openai:latest --model ${MODEL_NAME} --trust-request-chat-template
+```
+
+### Offline mode
+
+```bash
+uv run inference-endpoint benchmark from-config -c offline_llama3_8b_cnn.yaml --timeout 600
+```
+
+### Online mode
+
+```bash
+uv run inference-endpoint benchmark from-config -c online_llama3_8b_cnn.yaml --timeout 600
+```
+
+These configs run in performance-only mode by default. To also evaluate summarization quality, add `--mode both` and install the accuracy dependencies listed in the [Llama-2-70b accuracy setup](#accuracy-evaluation-setup-optional) section below.
+
+---
+
+## Llama-2-70b-chat-hf
+
+### Dataset
+
+Download the preprocessed Open Orca dataset from the MLCommons R2 bucket. Navigate to your desired download directory and run:
+
+```bash
+bash <(curl -s https://raw.githubusercontent.com/mlcommons/r2-downloader/refs/heads/main/mlc-r2-downloader.sh) \
+    https://inference.mlcommons-storage.org/metadata/llama-2-70b-open-orca-dataset.uri
+```
+
+Running the script above will download the dataset to the `./open_orca` directory. Additional instructions for downloading the model and dataset are in the [Reference Implementation for llama2-70b](https://github.com/mlcommons/inference/tree/master/language/llama2-70b).
+
+### Environment
+
+Go to [meta-llama/Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) and request access, then create a HuggingFace access token with read permissions.
+
+```bash
+export MODEL_NAME=meta-llama/Llama-2-70b-chat-hf
+export HF_TOKEN=<your Hugging Face token>
+export HF_HOME=<path to your hf_home, e.g. ~/.cache/huggingface>
+hf download $MODEL_NAME
+```
+
+### Accuracy evaluation setup (optional)
+
+Accuracy evaluation requires additional packages. Skip this for performance-only runs.
+
+```bash
+uv pip install nltk evaluate rouge_score
+uv run python -c 'import nltk; nltk.download("punkt"); nltk.download("punkt_tab")'
+```
+
+### Launch the server
+
+```bash
+docker run --runtime nvidia --gpus all \
+    -v ${HF_HOME}:/root/.cache/huggingface \
+    --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
+    -p 8000:8000 \
+    --ipc=host \
+    vllm/vllm-openai:latest --model ${MODEL_NAME} --gpu-memory-utilization 0.95
+```
+
+### Online mode
+
+```bash
+uv run inference-endpoint benchmark from-config -c online_llama2_70b_orca.yaml --timeout 600
+```