ludwig-ai--ludwig
wehub-resource-sync
593b94c120
pytest / Unit Tests (push) Has been cancelled
pytest / Integration (integration_tests_a) (push) Has been cancelled
pytest / Integration (integration_tests_b) (push) Has been cancelled
pytest / Integration (integration_tests_c) (push) Has been cancelled
pytest / Integration (integration_tests_d) (push) Has been cancelled
pytest / Integration (integration_tests_e) (push) Has been cancelled
pytest / Integration (integration_tests_f) (push) Has been cancelled
pytest / Integration (integration_tests_g) (push) Has been cancelled
pytest / Integration (integration_tests_h) (push) Has been cancelled
pytest / Integration (integration_tests_i) (push) Has been cancelled
pytest / Integration (integration_tests_j) (push) Has been cancelled
pytest / Distributed (distributed_a) (push) Has been cancelled
pytest / Distributed (distributed_b) (push) Has been cancelled
pytest / Distributed (distributed_c) (push) Has been cancelled
pytest / Distributed (distributed_d) (push) Has been cancelled
pytest / Distributed (distributed_e) (push) Has been cancelled
pytest / Distributed (distributed_f) (push) Has been cancelled
pytest / Minimal Install (push) Has been cancelled
pytest / Event File (push) Has been cancelled
pytest (slow) / py-slow (push) Has been cancelled
Publish JSON Schema / publish-schema (push) Has been cancelled
Llama2-7b Fine-Tuning 4bit (QLoRA)
This example shows how to fine-tune Llama2-7b to follow instructions. Instruction tuning is the first step in adapting a general purpose Large Language Model into a chatbot.
This example uses no distributed training or big data functionality. It is designed to run locally on any machine with GPU availability.
Prerequisites
- HuggingFace API Token
- Access approval to Llama2-7b-hf
- GPU with at least 12 GiB of VRAM (in our tests, we used an Nvidia T4)
Running
Command Line
Set your token environment variable from the terminal, then run the API script:
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:
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:
ludwig upload hf_hub -r <your_org>/<model_name> -m <path/to/model>