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v1.2.2.post1
Fix deepspeed version
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v1.2.2
Improve serving.
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v1.2.1
Bump version to 1.2.1
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Release v1.2.0 (LLaVA-1.6)
LLaVA-1.6 is out! With additional scaling to LLaVA-1.5, LLaVA-1.6-34B outperforms Gemini Pro on some benchmarks. It can now process 4x more pixels and perform more tasks/applications than before. Check out the blog post, and explore the demo! Models are available in Model Zoo. Training/eval data and scripts coming soon.
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Release v1.1.3 (Bring your own data, LoRA training)
Updates Support LoRA for the instruction tuning stage of LLaVA-1.5 -- comparable performance to full-model finetuning, and reduced requirements on GPU VRAM. (ckpts/logs, script) Bring your own data and finetune LLaVA-1.5 to your own task. (instruction) Basic support for Windows. (instruction) Fix: the training behavior with gradient accumulation is the same as large-batch training. Notes A new LoR…
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Release v1.1.1
In this version, we release the training scripts, data, and evaluation scripts on benchmarks for LLaVA 1.5. Bake your LLaVA today! LLaVA-1.5 achieves SoTA on 11 benchmarks, with just simple modifications to the original LLaVA, utilizes all public data, completes training in ~1 day on a single 8-A100 node, and surpasses methods like Qwen-VL-Chat that use billion-scale data. Check out the technical…
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Release v1.1.0
🔥 LLaVA-1.5 is out! This release supports LLaVA-1.5 model inference and serving. We will release the training scripts, data, and evaluation scripts on benchmarks in the coming week. LLaVA-1.5 achieves SoTA on 11 benchmarks, with just simple modifications to the original LLaVA, utilizes all public data, completes training in ~1 day on a single 8-A100 node, and surpasses methods like Qwen-VL-Chat th…
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Release v1.0.2
Added model zoo Improved support for ScienceQA with latest training configurations Improved docs We are working to continue improving the documentation. Please let us know if you find any documentation unclear, thanks!
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Release v1.0.1
Added LLaMA-2 support Full LoRA support. To make model training more accessible, we release a set of model weights based on LoRA, which supports training on academic resources (e.g. 4x A6000s, or 8x 3090s, without the need of CPU offloading) A more versatile design for training large multimodal models, including swapping different language models, vision encoders, and more coming soon Support high…