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---
license: apache-2.0
tags:
- llava
pipeline_tag: image-text-to-text
---
# GGUF Quantized LLaVA 1.6 34B
Updated quants and projector from [PR #5267](https://github.com/ggerganov/llama.cpp/pull/5267)
## Provided files
| Name | Quant method | Bits | Size | Use case |
| ---- | ---- | ---- | ---- | ----- |
| [llava-v1.6-34b.Q3_K_XS.gguf](https://huggingface.co/cjpais/llava-v1.6-34B-gguf/blob/main/llava-1.6-34b.Q3_K_XS.gguf) | Q3_K_XS | 3 | 14.2 GB| very small, high quality loss |
| [llava-v1.6-34b.Q3_K_M.gguf](https://huggingface.co/cjpais/llava-v1.6-34B-gguf/blob/main/llava-1.6-34b.Q3_K.gguf) | Q3_K_M | 3 | 16.7 GB| very small, high quality loss |
| [llava-v1.6-34b.Q4_K_M.gguf](https://huggingface.co/cjpais/llava-v1.6-34B-gguf/blob/main/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](https://huggingface.co/cjpais/llava-v1.6-34B-gguf/blob/main/llava-1.6-34b.Q5_K_S.gguf) | Q5_K_S | 5 | 23.7 GB| large, low quality loss - recommended |
| [llava-v1.6-34b.Q5_K_M.gguf](https://huggingface.co/cjpais/llava-v1.6-34B-gguf/blob/main/ggml-model-Q5_K.gguf) | Q5_K_M | 5 | 24.3 GB| large, very low quality loss - recommended |
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# 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](https://huggingface.co/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](https://huggingface.co/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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