---

license: apache-2.0 tags:

• llava

pipeline_tag: image-text-to-text

---

GGUF Quantized LLaVA 1.6 Mistral 7B

Updated quants and projector from PR #5267

Provided files

Name	Quant method	Bits	Size	Use case
llava-v1.6-mistral-7b.Q3KXS.gguf	Q3K_XS	3	2.99 GB	very small, high quality loss
llava-v1.6-mistral-7b.Q3KM.gguf	Q3K_M	3	3.52 GB	very small, high quality loss
llava-v1.6-mistral-7b.Q4KM.gguf	Q4K_M	4	4.37 GB	medium, balanced quality - recommended
llava-v1.6-mistral-7b.Q5KS.gguf	Q5K_S	5	5.00 GB	large, low quality loss - recommended
llava-v1.6-mistral-7b.Q5KM.gguf	Q5K_M	5	5.13 GB	large, very low quality loss - recommended
llava-v1.6-mistral-7b.Q6K.gguf	Q6K	6	5.94 GB	very large, extremely low quality loss
llava-v1.6-mistral-7b.Q80.gguf	Q80	8	7.7 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: mistralai/Mistral-7B-Instruct-v0.2

Model date:
LLaVA-v1.6-Mistral-7B was trained in December 2023.

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

License

mistralai/Mistral-7B-Instruct-v0.2 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.