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--- |
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license: apache-2.0 |
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tags: |
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- llava |
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pipeline_tag: image-text-to-text |
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--- |
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# GGUF Quantized LLaVA 1.6 34B |
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Updated quants and projector from [PR #5267](https://github.com/ggerganov/llama.cpp/pull/5267) |
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## Provided files |
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| Name | Quant method | Bits | Size | Use case | |
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| ---- | ---- | ---- | ---- | ----- | |
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| [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 | |
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| [llava-v1.6-34b.Q3_K_M.gguf](https://huggingface.co/cjpais/llava-v1.6-34B-gguf/blob/main/llava-v1.6-34b.Q3_K_M.gguf) | Q3_K_M | 3 | 16.7 GB| very small, high quality loss | |
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| [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 | |
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| [llava-v1.6-34b.Q5_K_S.gguf](https://huggingface.co/cjpais/llava-v1.6-34B-gguf/blob/main/llava-v1.6-34b.Q5_K_S.gguf) | Q5_K_S | 5 | 23.7 GB| large, low quality loss - recommended | |
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| [llava-v1.6-34b.Q5_K_M.gguf](https://huggingface.co/cjpais/llava-v1.6-34B-gguf/blob/main/llava-v1.6-34b.Q5_K_M.gguf) | Q5_K_M | 5 | 24.3 GB| large, very low quality loss - recommended | |
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| [llava-v1.6-34b.Q6_K.gguf](https://huggingface.co/cjpais/llava-v1.6-34B-gguf/blob/main/llava-v1.6-34b.Q6_K.gguf) | Q6_K | 5 | 28.2 GB| very large, extremely low quality loss | |
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| [llava-v1.6-34b.Q8_0.gguf](https://huggingface.co/cjpais/llava-v1.6-34B-gguf/blob/main/llava-v1.6-34b.Q8_0.gguf) | Q8_0 | 5 | 36.5 GB| very large, extremely low quality loss - not recommended | |
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<br> |
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<br> |
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# ORIGINAL LLaVA Model Card |
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## Model details |
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**Model type:** |
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LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. |
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It is an auto-regressive language model, based on the transformer architecture. |
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Base LLM: [NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) |
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**Model date:** |
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LLaVA-v1.6-34B was trained in December 2023. |
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**Paper or resources for more information:** |
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https://llava-vl.github.io/ |
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## License |
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[NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) license. |
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**Where to send questions or comments about the model:** |
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https://github.com/haotian-liu/LLaVA/issues |
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## Intended use |
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**Primary intended uses:** |
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The primary use of LLaVA is research on large multimodal models and chatbots. |
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**Primary intended users:** |
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The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. |
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## Training dataset |
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- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. |
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- 158K GPT-generated multimodal instruction-following data. |
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- 500K academic-task-oriented VQA data mixture. |
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- 50K GPT-4V data mixture. |
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- 40K ShareGPT data. |
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## Evaluation dataset |
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A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs. |
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