Add library name and link to Github repository (#2)

- Add library name and link to Github repository (623d0babcfb6ef616d3b4477e745560d8ce6a1f8)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,19 +1,19 @@
 ---
-license: apache-2.0
+base_model:
+- PleIAs/Pleias-350m-Preview
 language:
 - en
 - fr
 - it
 - de
 - es
-base_model:
-- PleIAs/Pleias-350m-Preview
+license: apache-2.0
 pipeline_tag: text-generation
 tags:
 - transformers
+library_name: transformers
 ---
 
-
 # Pleias-RAG-350m
 
 <div align="center">
@@ -21,7 +21,7 @@ tags:
 </div>
 
 <p align="center">
-  <a href="https://arxiv.org/abs/2504.18225"><b>Full model report</b></a>
+  <a href="https://huggingface.co/papers/2504.18225"><b>Full model report</b></a>
 </p>
 
 **Pleias-RAG-350M** is a 350 million parameters Small Reasoning Model, trained for retrieval-augmented general (RAG), search and source summarization. Along with Pleias-RAG-1B it belongs to the first generation of Pleias specialized reasoning models.
@@ -127,4 +127,6 @@ With only 350 million parameters, Pleias-RAG-350M is classified among the *phone
 
 We also release an unquantized [GGUF version](https://huggingface.co/PleIAs/Pleias-RAG-350M-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-350M can also be use in a broader range of non-conversational use cases including user support or educational assistance. Through this release, we aims to make tiny model workable in production by relying systematically on an externalized memory.
+Once integrated into a RAG system, Pleias-RAG-350M can also be use in a broader range of non-conversational use cases including user support or educational assistance. Through this release, we aims to make tiny model workable in production by relying systematically on an externalized memory.
+
+Github repository: https://github.com/Pleias/Pleias-RAG-Library