Update README.md

README.md

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 ## Mixtral-8x7B-Instruct-v0.1-hf-attn-4bit-moe-2bitgs8-metaoffload-HQQ
 This is a version of the 
-<a href="https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1"> Mixtral-8x7B-Instruct-v0.1 model</a> quantized with a mix of 4-bit and 2-bit via Half-Quadratic Quantization (HQQ).
+<a href="https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1"> Mixtral-8x7B-Instruct-v0.1 model</a> quantized with a mix of 4-bit and 2-bit via Half-Quadratic Quantization (HQQ). More specifically, the attention layers are quantized to 4-bit and the experts are quantized to 2-bit. 
 
-More specifically, the attention layers are quantized to 4-bit and the experts are quantized to 2-bit. 
-
-The difference between this model and <a href="https://huggingface.co/mobiuslabsgmbh/Mixtral-8x7B-Instruct-v0.1-hf-attn-4bit-moe-2bit-HQQ"> this </a> is that this one offloads the metadata to the CPU and you only need 13GB Vram to run it instead of 20GB!
+This model was designed to get the best quality at a budget of ~13GB of VRAM. It reaches an impressive <b>70.01</b> LLM leaderboard score, not too far from the original model's <b>72.62</b>.
 
 ![image/gif](https://cdn-uploads.huggingface.co/production/uploads/636b945ef575d3705149e982/-gwGOZHDb9l5VxLexIhkM.gif)