Model: ubitech-edg/llava-7b-cpt
Author: ubitech-edg
Pipeline: image-text-to-text
Library: transformers
Tags: transformers, safetensors, llava, image-to-text, image-text-to-text, visual-question-answering, vision-language, multimodal, causal-lm, continual-pretraining, lora, axolotl, deepspeed, eu-hpc, conversational, en, dataset:mm_captions_chat, dataset:text_cpt_corpus, base_model:llava-hf/llava-1.5-7b-hf, base_model:adapter:llava-hf/llava-1.5-7b-hf, license:llama2, endpoints_compatible, region:us
Base models (adapter): id: llava-hf/llava-1.5-7b-hf
Card metadata: language: en
license: llama2
tags: image-text-to-text, visual-question-answering, vision-language, llava, multimodal, causal-lm, continual-pretraining, lora, axolotl, deepspeed, transformers, eu-hpc
datasets: mm_captions_chat, text_cpt_corpus
metrics: loss
library_name: transformers
framework: pytorch
base_model: llava-hf/llava-1.5-7b-hf
model_name: llava-7b-cpt
pipeline_tag: image-text-to-text
task_categories: image-text-to-text, visual-question-answering
model_type: llava
inference: parameters: max_new_tokens: 128
temperature: 0.2
top_p: 0.9
trained_on: Leonardo EuroHPC
description: Two-stage continual pretraining (CPT) of LLaVA 1.5 7B: first on **text-only** data, then on **image–text** chat-style captions. LoRA adapters merged into base.
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Created: 2025-11-10 09:17:25