Update README.md

README.md

@@ -21,6 +21,8 @@ This model was obtained by quantizing the weights and activations of [Bielik-1.5
 AutoFP8 is used for quantization. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
 Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-tensor quantization is applied, in which a single linear scaling maps the FP8 representations of the quantized weights and activations. 
 
+📚 Technical report: https://arxiv.org/abs/2505.02550
+
 FP8 compuation is supported on Nvidia GPUs with compute capability > 8.9 (Ada Lovelace, Hopper).
 
 **DISCLAIMER: Be aware that quantised models show reduced response quality and possible hallucinations!**