Finetuning
Contents
Finetuning¶
Supported Finetuning Methods¶
Example: Vicuna Finetuning¶
To do a vicuna finetune of your first model through LLM-ATC, run the following
# start training
$ llm-atc train --model_type vicuna --finetune_data ./vicuna_test.json --name myvicuna --checkpoint_bucket my-trainy-bucket --checkpoint_store S3 --description "This is a finetuned model that just says its name is vicuna" -c mycluster --cloud gcp --envs "MODEL_BASE='meta-llama/Llama-2-7b-hf' HF_TOKEN=<huggingface_token> WANDB_API_KEY=<wandb_key" --accelerator A100:8 --region asia-southeast1
# Once training is done, shutdown the cluster
$ sky down
In this example, llm-atc train requests a single A100:8 instance from GCP.
If there is availability, a GPU instance is allocated and your finetuning data is
uploaded from your laptop to the instance as training data for finetuning. URIs to
object stores will also work. For example,
$ llm-atc train ... --finetune_data s3://my-bucket/vicuna_test.json
Model checkpoints will be saved into object store for deployment later. For instance,
# if trained on GCP, uses GS
gs://llm-atc/myvicuna
# otherwise uses AWS S3 by default
s3://llm-atc/myvicuna
# check where the actual object store is
$ sky storage ls
Model List¶
Models trained through LLM-ATC can be viewed using llm-atc list.
$ llm-atc list
+-------------+------------+---------------------+--------------------------------------+-------------------------------------------------------------+
| model_name | model_type | time | task | description |
+-------------+------------+---------------------+--------------------------------------+-------------------------------------------------------------+
| myvicuna | vicuna | 2023/08/01 15:17:46 | /Users/asai/.llm_atc/myvicuna.yml | This is a finetuned model that just says its name is vicuna |
+-------------+------------+---------------------+--------------------------------------+-------------------------------------------------------------+