Jan 27, 2024 · For this experiment, we'll focus on fine-tuning LLaVA on a custom dataset using the official LLaVA repo with the Llama-2 7B backbone language model. We will use the OK …

Mar 4, 2024 · Fine-tuning LLaVA with the OK-VQA dataset necessitates data formatting to adhere to the LLaVA repository’s specific requirements. OK-VQA presents a distinct challenge due to …

Convert your data to a JSON file of a List of all samples. Sample metadata should contain id (a unique identifier), image (the path to the image), and conversations (the conversation data …

Feb 16, 2024 · For an effective fine-tuning process of LLaVA-v1.5–13B, consider the following hardware prerequisites: Recommended GPUs: Utilize high-end GPUs such as NVIDIA A100 …

LLaVa-NeXT (also called LLaVa-1.6) improves upon LLaVa by increasing the input image resolution and training on an improved visual instruction tuning dataset to improve OCR and …

Apr 16, 2025 · By employing existing datasets composed of text-image pairs, they initiate a process where GPT-4—inherently a text-based model—is prompted to elaborate on the text …

Oct 1, 2024 · These days I have been finetuning LLaVA-NeXT with my custom dataset but the loss stays at around 18 and does not decrease the during the training process. script. train.py.

This text provides a tutorial on how to fine-tune the LLaVA multimodal language model on a custom dataset, specifically using the OK-VQA dataset. Abstract. The tutorial introduces the …

Fusing semantic and metric data into templated VQA chat, Vision Language Models can be instruction-tuned with low-rank adapters to enhance their baseline spatial reasoning …

Apr 10, 2024 · Make sure to replace the processor, model and chat templates by the one of LLaVa-Next instead of LLaVa. Regarding deployment, TGI (a framework meant for …