Update README.md

README.md

@@ -9,7 +9,7 @@ tags:
 datasets:
 - AI-MO/NuminaMath-CoT
 model-index:
-- name: sft_deepseek-math-7b_aimo_v53.24
+- name: AI-MO/NuminaMath-7B-CoT
   results: []
 language:
 - en
@@ -46,7 +46,6 @@ NuminaMath 7B CoT is the model from Stage 1 and was fine-tuned on [AI-MO/NuminaM
 Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
 
 ```python
-import re
 import torch
 from transformers import pipeline
 
@@ -67,20 +66,13 @@ gen_config = {
 outputs = pipe(prompt, **gen_config)
 text = outputs[0]["generated_text"]
 print(text)
-
-# WARNING: This code will execute the python code in the string. We show this for eductional purposes only.
-# Please refer to our full pipeline for a safer way to execute code.
-python_code = re.findall(r"```python(.*?)```", text, re.DOTALL)[0]
-exec(python_code)
-```
-
-The above executes a single step of Python code - for more complex problems, you will want to run the logic for several steps to obtain the final solution.  
+``` 
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-NuminaMath 7B TIR was created to solve problems in the narrow domain of competition-level mathematics. As a result, the model should not be used for general chat applications. With greedy decoding, we find the model is capable of solving problems at the level of [AMC 12](https://artofproblemsolving.com/wiki/index.php/2023_AMC_12A_Problems), but often struggles generate a valid solution on harder problems at the AIME and Math Olympiad level. The model also struggles to solve geometry problems, likely due to it's limited capacity and lack of other modalities like vision. 
+NuminaMath 7B CoT was created to solve problems in the narrow domain of competition-level mathematics. As a result, the model should not be used for general chat applications. With greedy decoding, we find the model is capable of solving problems at the level of [AMC 12](https://artofproblemsolving.com/wiki/index.php/2023_AMC_12A_Problems), but often struggles generate a valid solution on harder problems at the AIME and Math Olympiad level. The model also struggles to solve geometry problems, likely due to it's limited capacity and lack of other modalities like vision. 
 
 
 ## Training procedure