"Update README.md"

README.md

@@ -24,21 +24,7 @@ I'm constantly enhancing these model descriptions to provide you with the most r
 - Model creator: [mosaicml](https://huggingface.co/mosaicml)
 - Original model: [mpt-30b-instruct](https://huggingface.co/mosaicml/mpt-30b-instruct)
 
-# Important Update for Falcon Models in llama.cpp Versions After October 18, 2023
-
-As noted on the [Llama.cpp GitHub repository](https://github.com/ggerganov/llama.cpp#hot-topics), all new Llama.cpp releases after October 18, 2023, will require a re-quantization due to the new BPE tokenizer.
-
-**Good news!** I am glad that my re-quantization process for Falcon Models is nearly complete. Download the latest quantized models to ensure compatibility with recent llama.cpp software.
-
-**Key Points:**
-
-- **Stay Informed:** Keep an eye on software application release schedules using llama.cpp libraries.
-- **Monitor Upload Times:** Re-quantization is *almost* done. Watch for updates on my Hugging Face Model pages.
-
-**Important Compatibility Note:** Old software will work with old Falcon models, but expect updated software to exclusively support the new models.
-
-This change primarily affects **Falcon** and **Starcoder** models, with other models remaining unaffected.
-
+MPT-7b and MPT-30B are part of the family of Mosaic Pretrained Transformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference.
 
 
 
@@ -50,19 +36,21 @@ The core project making use of the ggml library is the [llama.cpp](https://githu
 
 # Quantization variants
 
-There is a bunch of quantized files available. How to choose the best for you:
+There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option 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.
+## Note:
+Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not *real* K-quants. More details can be found in affected model descriptions.
+(This mainly refers to Falcon 7b and Starcoder models)
 
 # 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.
+K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load.
 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.
+With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences.