GGUF Quantized LLaVA 1.6 34B

Updated quants and projector from PR #5267

Provided files

Name Quant method Bits Size Use case
llava-v1.6-34b.Q3_K_XS.gguf Q3_K_XS 3 14.2 GB very small, high quality loss
llava-v1.6-34b.Q3_K_M.gguf Q3_K_M 3 16.7 GB very small, high quality loss
llava-v1.6-34b.Q4_K_M.gguf Q4_K_M 4 20.66 GB medium, balanced quality - recommended
llava-v1.6-34b.Q5_K_S.gguf Q5_K_S 5 23.7 GB large, low quality loss - recommended
llava-v1.6-34b.Q5_K_M.gguf Q5_K_M 5 24.3 GB large, very low quality loss - recommended
llava-v1.6-34b.Q6_K.gguf Q6_K 5 28.2 GB very large, extremely low quality loss
llava-v1.6-34b.Q8_0.gguf Q8_0 5 36.5 GB very large, extremely low quality loss - not recommended


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.

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