Update README.md

README.md

@@ -42,7 +42,7 @@ widget:
       values of \\( k \\) is:\n\n\\[\n\\boxed{4}\n\\]\n
 pipeline_tag: text-generation
 model-index:
-- name: Numina-Math-7B
+- name: NuminaMath-7B-TIR-GPTQ
   results: []
 license: apache-2.0
 language:
@@ -52,13 +52,13 @@ language:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-<img src="https://huggingface.co/AI-MO/Numina-Math-7B/resolve/main/thumbnail.png" alt="Numina Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+<img src="https://huggingface.co/AI-MO/NuminaMath-7B-TIR/resolve/main/thumbnail.png" alt="Numina Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
 
 
-# Model Card for NuminaMath 7B GPTQ
+# Model Card for NuminaMath 7B TIR GPTQ
 
-NuminaMath is a series of language models that are trained to solve math problems using tool-integrated reasoning. NuminaMath 7B won the first progress prize of the [AI Math Olympiad (AIMO)](https://aimoprize.com), with a score of 29/50 on the public and private tests sets. 
+NuminaMath is a series of language models that are trained to solve math problems using tool-integrated reasoning (TIR). NuminaMath 7B TIR won the first progress prize of the [AI Math Olympiad (AIMO)](https://aimoprize.com), with a score of 29/50 on the public and private tests sets. 
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/NyhBs_gzg40iwL995DO9L.png)
 
-This model is an 8-bit version of [`AI-MO/Numina-Math-7B`](https://huggingface.co/AI-MO/Numina-Math-7B), which we quantized with [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) to run fast inference in the Kaggle submissions. Please consult the original model card for more details.
+This model is an 8-bit version of [`AI-MO/NuminaMath-7B-TIR`](https://huggingface.co/AI-MO/NuminaMath-7B-TIR), which we quantized with [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) to run fast inference in the Kaggle submissions. Please consult the original model card for more details.