Update README.md

README.md

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 The Bielik-7B-Instruct-v0.1 is an instruct fine-tuned version of the [Bielik-7B-v0.1](https://huggingface.co/speakleash/Bielik-7B-v0.1). Forementioned model stands as a testament to the unique collaboration between the open-science/open-souce project SpeakLeash and the High Performance Computing (HPC) center: ACK Cyfronet AGH. Developed and trained on Polish text corpora, which has been cherry-picked and processed by the SpeakLeash team, this endeavor leverages Polish large-scale computing infrastructure, specifically within the PLGrid environment, and more precisely, the HPC centers: ACK Cyfronet AGH. The creation and training of the Bielik-7B-Instruct-v0.1 was propelled by the support of computational grant number PLG/2024/016951, conducted on the Helios supercomputer, enabling the use of cutting-edge technology and computational resources essential for large-scale machine learning processes. As a result, the model exhibits an exceptional ability to understand and process the Polish language, providing accurate responses and performing a variety of linguistic tasks with high precision.
 
+[We have prepared quantized versions of the model as well as MLX format.](#quant-and-mlx-versions)
+
 ## Model
 
 The [SpeakLeash](https://speakleash.org/) team is working on their own set of instructions in Polish, which is continuously being expanded and refined by annotators. A portion of these instructions, which had been manually verified and corrected, has been utilized for training purposes. Moreover, due to the limited availability of high-quality instructions in Polish, publicly accessible collections of instructions in English were used - [OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) and [orca-math-word-problems-200k](https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k), which accounted for half of the instructions used in training. The instructions varied in quality, leading to a deterioration in model’s performance. To counteract this while still allowing ourselves to utilize forementioned datasets,several improvements were introduced:
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 Bielik-7B-Instruct-v0.1 has been trained with the use of an original open source framework called [ALLaMo](https://github.com/chrisociepa/allamo) implemented by [Krzysztof Ociepa](https://www.linkedin.com/in/krzysztof-ociepa-44886550/). This framework allows users to train language models with architecture similar to LLaMA and Mistral in fast and efficient way.
 
+
 ### Model description:
 
 * **Developed by:** [SpeakLeash](https://speakleash.org/)
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 | Precision                   | bfloat16 (mixed) |
 
 
+### Quant and MLX versions:
+We know that some people want to explore smaller models or don't have the resources to run a full model. Therefore, we have prepared quantized versions of the Bielik-7B-Instruct-v0.1 model. We are also mindful of Apple Silicon.
+<br>
+<br>
+Quantized versions (for non-GPU / weaker GPU):
+- https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-GGUF
+- https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-GPTQ
+- https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-AWQ
+- https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-EXL2
+
+For Apple Silicon:
+- https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-MLX
+
+
 ### Instruction format
 
 In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should start with the beginning of a sentence token. The generated completion will be finished by the end-of-sentence token.