Add pipeline tag, paper link, project page link and Github repo

README.md

@@ -1,4 +1,6 @@
 ---
+base_model:
+- mistralai/Mistral-Small-3.1-24B-Instruct-2503
 language:
 - en
 - fr
@@ -24,14 +26,12 @@ language:
 - vi
 - hi
 - bn
-license: apache-2.0
 library_name: vllm
+license: apache-2.0
 inference: false
-base_model:
-- mistralai/Mistral-Small-3.1-24B-Instruct-2503
-extra_gated_description: >-
-  If you want to learn more about how we process your personal data, please read
-  our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
+extra_gated_description: If you want to learn more about how we process your personal
+  data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
+pipeline_tag: text-generation
 ---
 
 # Model Card for Magistral-Small-2506
@@ -42,9 +42,11 @@ Magistral Small can be deployed locally, fitting within a single RTX 4090 or a 3
 
 Learn more about Magistral in our [blog post](https://mistral.ai/news/magistral/).
 
+The model was presented in the paper [Magistral](https://huggingface.co/papers/2506.10910).
+
 ## Key Features
 - **Reasoning:** Capable of long chains of reasoning traces before providing an answer.
-- **Multilingual:** Supports dozens of languages, including English, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Malay, Nepali, Polish, Portuguese, Romanian, Russian, Serbian, Spanish, Swedish, Turkish, Ukrainian, Vietnamese, Arabic, Bengali, Chinese, and Farsi.
+- **Multilingual:** Supports dozens of languages, including English, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Malay, Nepali, Polish, Portuguese, Romanian, Russian, Serbian, Spanish, Turkish, Ukrainian, Vietnamese, Arabic, Bengali, Chinese, and Farsi.
 - **Apache 2.0 License:** Open license allowing usage and modification for both commercial and non-commercial purposes.
 - **Context Window:** A 128k context window, **but** performance might degrade past **40k**. Hence we recommend setting the maximum model length to 40k.
 
@@ -208,7 +210,8 @@ for chunk in stream:
   if content is not None:
     if not printed_content:
         printed_content = True
-        print("\ncontent:", end="", flush=True)
+        print("
+content:", end="", flush=True)
     # Extract and print the content
     print(content, end="", flush=True)