Update README.md

README.md

@@ -7,13 +7,13 @@ base_model: CohereForAI/c4ai-command-r-plus
 **2024-04-06**: Support for this model is still being worked on - [`PR #6491`](https://github.com/ggerganov/llama.cpp/pull/6491).  
 I am currently re-uploading all the quants compatible with the PR.
 
-* What is importance matrix (imatrix)? You can [read more about it from the author here](https://github.com/ggerganov/llama.cpp/pull/4861).
-* How do I use imatrix quants? Just like any other GGUF, the `.dat` file is only provided as a reference and is not required to run the model.
 * GGUF importance matrix (imatrix) quants for https://huggingface.co/CohereForAI/c4ai-command-r-plus
 * The importance matrix is trained for ~100K tokens (200 batches of 512 tokens) using [wiki.train.raw](https://huggingface.co/datasets/wikitext).
 * [Which GGUF is right for me? (from Artefact2)](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
 * The [imatrix is being used on the K-quants](https://github.com/ggerganov/llama.cpp/pull/4930) as well (only for < Q6_K).
 * You can merge GGUFs with `gguf-split --merge <first-chunk> <output-file>` although this is not required since [f482bb2e](https://github.com/ggerganov/llama.cpp/commit/f482bb2e4920e544651fb832f2e0bcb4d2ff69ab).
+* What is importance matrix (imatrix)? You can [read more about it from the author here](https://github.com/ggerganov/llama.cpp/pull/4861).
+* How do I use imatrix quants? Just like any other GGUF, the `.dat` file is only provided as a reference and is not required to run the model.
 
 > C4AI Command R+ is an open weights research release of a 104B billion parameter model with highly advanced capabilities, this includes Retrieval Augmented Generation (RAG) and tool use to automate sophisticated tasks. The tool use in this model generation enables multi-step tool use which allows the model to combine multiple tools over multiple steps to accomplish difficult tasks. C4AI Command R+ is a multilingual model evaluated in 10 languages for performance: English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Arabic, and Simplified Chinese. Command R+ is optimized for a variety of use cases including reasoning, summarization, and question answering.