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.Q3_K_XS.gguf | Q3_K_XS | 3 | 2.99 GB | very small, high quality loss |
| llava-v1.6-mistral-7b.Q3_K_M.gguf | Q3_K_M | 3 | 3.52 GB | very small, high quality loss |
| llava-v1.6-mistral-7b.Q4_K_M.gguf | Q4_K_M | 4 | 4.37 GB | medium, balanced quality - recommended |
| llava-v1.6-mistral-7b.Q5_K_S.gguf | Q5_K_S | 5 | 5.00 GB | large, low quality loss - recommended |
| llava-v1.6-mistral-7b.Q5_K_M.gguf | Q5_K_M | 5 | 5.13 GB | large, very low quality loss - recommended |
| llava-v1.6-mistral-7b.Q6_K.gguf | Q6_K | 6 | 5.94 GB | very large, extremely low quality loss |
| llava-v1.6-mistral-7b.Q8_0.gguf | Q8_0 | 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.