03. BakLLaVA Setup

Chapter 3 of 18 · 20 min

KEY INSIGHT

BakLLaVA offers improved multi-modal performance through better vision-language alignment while maintaining compatibility with LLaVA's interfaces, making it a straightforward swap-in replacement. BakLLaVA builds upon LLaVA's architecture with modified training objectives that improve visual comprehension. The model uses a Mistral-7B base combined with a CLIP-based vision encoder. Setup closely mirrors LLaVA with minor configuration differences. ```bash # Install with BakLLaVA-specific requirements pip install bakllava-requirements # if available # Or add to existing environment pip install einops==0.7.0 pip install xformers==0.0.24 # Download BakLLaVA weights huggingface-cli download --repo-type model \ ikesaurus/bakllava-1-7b --local-dir models/bakllava-1-7b ``` Configuration differs slightly from LLaVA. Create a custom config file: ```yaml # config.yaml model_name: models/bakllava-1-7b vision_tower: clip-vit-large-patch14-336 freeze_vision_tower: false pretrain_mm_mlp_adapter: models/bakllava-1-7b/mm_projector.bin text_model: name: mistralai/Mistral-7B-v0.1 quantize: 4bit inference: max_length: 2048 temperature: 0.7 top_p: 0.9 ``` Initialize with the custom configuration: ```python import torch from bakllava import BakLLaVAModel, BakLLaVAProcessor config = { "model_path": "models/bakllava-1-7b", "torch_dtype": torch.float16, "device_map": "auto" } processor = BakLLaVAProcessor.from_pretrained(config["model_path"]) model = BakLLaVAModel.from_pretrained(config["model_path"]) ``` Performance comparison shows BakLLaVA often produces more detailed captions: ```python # Test both models on same image image_path = "test_images/sample.jpg" # LLaVA output tends toward brief descriptions # BakLLaVA often includes spatial relationships and fine details ``` Potential issues during setup: - **Vision encoder mismatch**: Ensure CLIP weights match exactly - **Quantization errors**: The 4-bit mode requires `bitsandbytes` 0.41+ - **Memory fragmentation**: Call `torch.cuda.empty_cache()` between tests

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.

Local verification checkpoint

EXERCISE

Load both LLaVA and BakLLaVA in your environment and run identical inference on a complex image. Document differences in output length, detail level, and inference time.