Create README.md

README.md

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+---
+license: apache-2.0
+---
+
+# GGUF Quantized LLaVA 1.6 Mistral 7B
+
+## Provided files
+| Name | Quant method | Bits | Size | Use case |
+| ---- | ---- | ---- | ---- | ----- |
+| [llava-v1.6-mistral-7b.Q3_K_XS.gguf](https://huggingface.co/cjpais/llava-1.6-mistral-7b-gguf/blob/main/capybarahermes-2.5-mistral-7b.Q2_K.gguf) | Q3_K_XS | 2 | 2.99 GB| very small, high quality loss |
+| [llava-v1.6-mistral-7b.Q3_K_M.gguf](https://huggingface.co/cjpais/llava-1.6-mistral-7b-gguf/blob/main/capybarahermes-2.5-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](https://huggingface.co/cjpais/llava-1.6-mistral-7b-gguf/blob/main/capybarahermes-2.5-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](https://huggingface.co/cjpais/llava-1.6-mistral-7b-gguf/blob/main/capybarahermes-2.5-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](https://huggingface.co/cjpais/llava-1.6-mistral-7b-gguf/blob/main/capybarahermes-2.5-mistral-7b.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| large, very low quality loss - recommended |
+
+<br>
+<br>
+
+# 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](https://huggingface.co/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](https://huggingface.co/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.