TinyCLIP-23m_Qwen2.5-0.5B_523M

VLM Distillation (LLaVA)

Small toolkit for training and serving a custom vision-language model (VLM) using a vision encoder + LoRA-tuned language model + projector.

Main Files

• vlm_distill_LLaVA.py: Train pipeline for LLaVA-style data (llava_images_100k/). Builds model, trains, and saves checkpoints.
• test_LLaVA.py: Loads a trained checkpoint and runs single-sample inference on the dataset split.
• run_model_LLaVA.py: FastAPI server for inference (/chat) from local image path or base64 image.
• data_extract.py: Dataset/data extraction helper.
• requirements.txt: Python dependencies.
• checkpoints/: Saved LoRA adapters + projector weights.
• images_test/: Local images for quick inference testing.

Quick Setup

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Run API Server to hit model

python run_model_LLaVA.py

Server starts on http://0.0.0.0:8000 with endpoint:

• POST /chat
  • fields: prompt, and either image_path or image_base64

Example request body:

{
  "prompt": "Summarize the image in one sentence.",
  "image_path": "images_test/bowl.jpg"
}