LLaVA (LoRA, Preview)

NOTE: This is a technical preview, and is not yet ready for production use. We are still running hyperparameter search for the LoRA model, and will release the final model soon. If you'd like to contribute to this, please contact us.

You need latest code base for LoRA support (instructions here)

Demo (Web UI)

Please execute each of the commands below one by one (after the previous one has finished). The commands are the same as launching other demos except for an additional --model-base flag to specify the base model to use. Please make sure the base model corresponds to the LoRA checkpoint that you are using. For this technical preview, you need Vicuna v1.1 (7B) checkpoint (if you do not have that already, follow the instructions here).

Launch a controller

python -m llava.serve.controller --host 0.0.0.0 --port 10000

Launch a gradio web server.

python -m llava.serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload

You just launched the Gradio web interface. Now, you can open the web interface with the URL printed on the screen. You may notice that there is no model in the model list. Do not worry, as we have not launched any model worker yet. It will be automatically updated when you launch a model worker.

Launch a model worker

python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-vicuna-7b-v1.1-lcs_558k-instruct_80k_3e-lora-preview-alpha --model-base /path/to/vicuna-v1.1

Wait until the process finishes loading the model and you see "Uvicorn running on ...". Now, refresh your Gradio web UI, and you will see the model you just launched in the model list.

You can launch as many workers as you want, and compare between different model checkpoints in the same Gradio interface. Please keep the --controller the same, and modify the --port and --worker to a different port number for each worker.

Training

Please see sample training scripts for LoRA and QLoRA.

We provide sample DeepSpeed configs, zero3.json is more like PyTorch FSDP, and zero3_offload.json can further save memory consumption by offloading parameters to CPU. zero3.json is usually faster than zero3_offload.json but requires more GPU memory, therefore, we recommend trying zero3.json first, and if you run out of GPU memory, try zero3_offload.json. You can also tweak the per_device_train_batch_size and gradient_accumulation_steps in the config to save memory, and just to make sure that per_device_train_batch_size and gradient_accumulation_steps remains the same.

If you are having issues with ZeRO-3 configs, and there are enough VRAM, you may try zero2.json. This consumes slightly more memory than ZeRO-3, and behaves more similar to PyTorch FSDP, while still supporting parameter-efficient tuning.

Create Merged Checkpoints

python scripts/merge_lora_weights.py \
    --model-path /path/to/lora_model \
    --model-base /path/to/base_model \
    --save-model-path /path/to/merge_model