Experimental GGUF Quantized LLaVA 1.6 34B

Seem to work decently well. Unknown limitations compared to original model

Notes: Was prepared with a unofficial script, and is likely missing some data and lacking some performance. Will update quants when better script is available

Provided files

ORIGINAL LLaVA Model Card

Model details

Model type: LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: NousResearch/Nous-Hermes-2-Yi-34B

Model date: LLaVA-v1.6-34B was trained in December 2023.

Paper or resources for more information: https://llava-vl.github.io/

License

NousResearch/Nous-Hermes-2-Yi-34B license.

Where to send questions or comments about the model: https://github.com/haotian-liu/LLaVA/issues

Intended use

Primary intended uses: The primary use of LLaVA is research on large multimodal models and chatbots.

Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.

Training dataset

• 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
• 158K GPT-generated multimodal instruction-following data.
• 500K academic-task-oriented VQA data mixture.
• 50K GPT-4V data mixture.
• 40K ShareGPT data.

Evaluation dataset

A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.