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README.md

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+---
+license: apache-2.0
+---
+[![banner](https://maddes8cht.github.io/assets/buttons/Huggingface-banner.jpg)]()
+
+## I am still building the structure of these descriptions.
+
+These will contain increasingly more content to help find the best models for a purpose.
+
+# openbuddy-falcon-7b-v6-bf16 - GGUF
+- Model creator: [OpenBuddy](https://huggingface.co/OpenBuddy)
+- Original model: [openbuddy-falcon-7b-v6-bf16](https://huggingface.co/OpenBuddy/openbuddy-falcon-7b-v6-bf16)
+## Note:
+
+This is v6 of OpenBuddy's Falcon-7b Variant. Somehow they forgot to provide a real `Model Card` for v6, so refer to the v5 `Model Card` instead:
+
+https://huggingface.co/OpenBuddy/openbuddy-falcon-7b-v5-fp16
+
+OpenBuddy provides strong multiligual Model variants. On their Huggingface Organization Card they say:
+
+> Our mission with OpenBuddy is to provide a free, open, and offline-capable AI model that operates on users' devices, irrespective of their language or cultural background. We strive to empower individuals worldwide to access and benefit from AI technology.
+
+# About GGUF format
+
+`gguf` is the current file format used by the [`ggml`](https://github.com/ggerganov/ggml) library.
+A growing list of Software is using it and can therefore use this model.
+The core project making use of the ggml library is the [llama.cpp](https://github.com/ggerganov/llama.cpp) project by Georgi Gerganov
+
+# Quantization variants
+
+There is a bunch of quantized files available. How to choose the best for you:
+
+# legacy quants
+
+Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are `legacy` quantization types.
+Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants.
+Falcon 7B models cannot be quantized to K-quants.
+
+# K-quants
+
+K-quants are based on the idea that the quantization of certain parts affects the quality in different ways. If you quantize certain parts more and others less, you get a more powerful model with the same file size, or a smaller file size and lower memory load with comparable performance.
+So, if possible, use K-quants.
+With a Q6_K you should find it really hard to find a quality difference to the original model - ask your model two times the same question and you may encounter bigger quality differences.
+
+
+# Original Model Card:
+
+
+      <center>
+<a href="https://maddes8cht.github.com"><img src="/assets/buttons/maddes8cht-github-io.jpg" alt="GitHub" /></a>
+<a href="https://stackexchange.com/users/26485911"><img src="https://stackexchange.com/users/flair/26485911.png" width="208" height="58" alt="profile for maddes8cht on Stack Exchange, a network of free, community-driven Q&amp;A sites" title="profile for maddes8cht on Stack Exchange, a network of free, community-driven Q&amp;A sites"></a>
+<a href="https://github.com/maddes8cht"><img src="/assets/buttons/github-button.jpg" alt="GitHub" /></a>
+<a href="https://huggingface.co/maddes8cht"><img src="/assets/buttons/huggingface-button.jpg" alt="HuggingFace" /></a></p>
+<a href="https://twitter.com/maddes1966"><img src="/assets/buttons/twitter-button.jpg" alt="HuggingFace" /></a></p>
+      </center>