Update README.md

README.md

@@ -22,25 +22,27 @@ model-index:
 <!-- 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/blob/main/thumbnail.png" alt="Numina Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+<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'"/>
 
 
 # Model Card for NuminaMath 7B
 
-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. This model is a fine-tuned version of [deepseek-ai/deepseek-math-7b-base](https://huggingface.co/deepseek-ai/deepseek-math-7b-base) with two stages of supervised fine-tuning:
+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. 
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/NyhBs_gzg40iwL995DO9L.png)
+
+This model is a fine-tuned version of [deepseek-ai/deepseek-math-7b-base](https://huggingface.co/deepseek-ai/deepseek-math-7b-base) with two stages of supervised fine-tuning:
 
 * **Stage 1:** fine-tune the base model on a large, diverse dataset of natural language math problems and solutions, where each solution is templated with Chain of Thought (CoT) to facilitate learning. 
 * **Stage 2:** fine-tune the model from Stage 1 on a synthetic dataset of tool-integrated reasoning, where each math problem is decomposed into a sequence of rationales, Python programs, and their outputs. Here we followed [Microsoft’s ToRA paper](https://arxiv.org/abs/2309.17452) and prompted GPT-4 to produce solutions in the ToRA format with code execution feedback. Fine-tuning on this data produces a reasoning agent that can solve mathematical problems via a mix of natural language reasoning and use of the Python REPL to compute intermediate results.
 
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/NyhBs_gzg40iwL995DO9L.png)
-
 
 ## Model description
 
-- **Model type:** A 7B parameter Math model fine-tuned in two stages of training, first on a dataset with math problem-solution pairs and then on a dataset with examples of multi-step synthetic generations using tool integrated reasoning.
+- **Model type:** A 7B parameter math LLM fine-tuned in two stages of supervised fine-tuning, first on a dataset with math problem-solution pairs and then on a synthetic dataset with examples of multi-step generations using tool-integrated reasoning.
 - **Language(s) (NLP):** Primarily English
-- **License:** Apacahe 2.0
+- **License:** Apache 2.0
 - **Finetuned from model:** [deepseek-ai/deepseek-math-7b-base](https://huggingface.co/deepseek-ai/deepseek-math-7b-base)
 
 ### Model Sources
@@ -80,8 +82,10 @@ print(text)
 # 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)
-
 ```
+
+In practice you will want to repeat the 
+
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->