Update README.md

README.md

@@ -87,6 +87,6 @@ The easiest way to deploy Pleias-RAG-1B is through [our official library](https:
 
 With 1.2B parameters, Pleias-RAG-1B can be readily deployed in many constrained infrastructures, including desktop systems on CPU RAM.
 
-We also release an unquantized GGUF version for deployment on CPU. Our internal performance benchmarks suggest that waiting times are currently acceptable for most either even under constrained RAM: about 20 seconds for a complex generation including reasoning traces on 8g RAM and below. Since the model is unquantized, quality of text generation should be identical to the original model.
+We also release an [unquantized GGUF version](https://huggingface.co/PleIAs/Pleias-RAG-1B-gguf) for deployment on CPU. Our internal performance benchmarks suggest that waiting times are currently acceptable for most either even under constrained RAM: about 20 seconds for a complex generation including reasoning traces on 8g RAM and below. Since the model is unquantized, quality of text generation should be identical to the original model.
 
 Once integrated into a RAG system, Pleias-RAG-1B can also be used in a broader range of non-conversational use cases including user support or educational assistance. Through this release, we aims to make SLMs workable in production by relying systematically on an externalized memory.