Added CLI llama2 example (#3491)

examples/llama2_7b_finetuning_4bit/README.md

@@ -12,11 +12,30 @@ with GPU availability.
 - Access approval to [Llama2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
 - GPU with at least 12 GiB of VRAM (in our tests, we used an Nvidia T4)
 
-## Running the example
+## Running
+
+### Command Line
+
+Set your token environment variable from the terminal, then run the API script:
+
+```bash
+export HUGGING_FACE_HUB_TOKEN="<api_token>"
+./run_train.sh
+```
+
+### Python API
 
 Set your token environment variable from the terminal, then run the API script:
 
 ```bash
 export HUGGING_FACE_HUB_TOKEN="<api_token>"
 python train_alpaca.py
 ```
+
+## Upload to HuggingFace
+
+You can upload to the HuggingFace Hub from the command line:
+
+```bash
+ludwig upload hf_hub -r <your_org>/<model_name> -m <path/to/model>
+```

examples/llama2_7b_finetuning_4bit/llama2_7b_4bit.yaml

@@ -0,0 +1,28 @@
+model_type: llm
+base_model: meta-llama/Llama-2-7b-hf
+
+quantization:
+  bits: 4
+
+adapter:
+  type: lora
+
+input_features:
+  - name: instruction
+    type: text
+
+output_features:
+  - name: output
+    type: text
+
+trainer:
+  type: finetune
+  learning_rate: 0.0003
+  batch_size: 2
+  gradient_accumulation_steps: 8
+  epochs: 3
+  learning_rate_scheduler:
+    warmup_fraction: 0.01
+
+backend:
+  type: local

examples/llama2_7b_finetuning_4bit/run_train.sh

@@ -0,0 +1,10 @@
+#!/usr/bin/env bash
+
+# Fail fast if an error occurs
+set -e
+
+# Get the directory of this script, which contains the config file
+SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
+
+# Train
+ludwig train --config ${SCRIPT_DIR}/llama2_7b_4bit.yaml --dataset ludwig://alpaca