mlx-examples/llava/README.md
Noah Kasmanoff a429263905
LlaVA in MLX (#461)
* add: llava mlx first draft

* add: weights comparision

* add forward pass skeleton

* update: now  imports weights correctly

* delete base

* latest

* adding config

* fix: use config

* add mlx config

* feat: add image processor for llava processor

* wip

* feat: llava working example

* chore: refactor generate script

* chore: clean up

* add: warning to user if no <image> token despite using one

* add: __call__ to LlavaModel

* add: call to LlavaModel

* update fp

* clean up var names

* update: native GeLU

* Cleanup

* update generate and readme

* remove todo comment

* rearrange tests

* fix example code

* nits in README

* update readme

* nit in readme

* nits in README

* chore(llava): refactor image embedding merging logic

* min mlx version

* nits in readmes

* fix cli prompt, some nits

* updates, slight simplify

---------

Co-authored-by: anchen <li.anchen.au@gmail.com>
Co-authored-by: Awni Hannun <awni@apple.com>
2024-03-01 10:28:35 -08:00

1.3 KiB

LLaVA

An example of LLaVA: Large Language and Vision Assistant in MLX.1 LLlava is a multimodal model that can generate text given combined image and text inputs.

Setup

Install the dependencies:

pip install -r requirements.txt

Run

You can use LLaVA to ask questions about images.

For example, using the command line:

python generate.py \
  --model llava-hf/llava-1.5-7b-hf \
  --image "http://images.cocodataset.org/val2017/000000039769.jpg" \
  --prompt "USER: <image>\nWhat are these?\nASSISTANT:" \
  --max-tokens 128 \
  --temp 0

This uses the following image:

alt text

And generates the output:

These are two cats lying on a pink couch.

You can also use LLaVA in Python:

from generate import load_model, prepare_inputs, generate_text

processor, model = load_model("llava-hf/llava-1.5-7b-hf")

max_tokens, temperature = 128, 0.0

prompt = "USER: <image>\nWhat are these?\nASSISTANT:"
image = "http://images.cocodataset.org/val2017/000000039769.jpg"
input_ids, pixel_values = prepare_inputs(processor, image, prompt)

reply = generate_text(
    input_ids, pixel_values, model, processor, max_tokens, temperature
)

print(reply)

  1. Refer to LLaVA project webpage for more information. ↩︎