Update README.md

README.md

@@ -49,9 +49,6 @@ Info about each model below, followed by settings/info on using this MOE model.
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 # Qwen2.5-Coder-32B-Instruct
-<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
-    <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
-</a>
 
 ## Introduction
 
@@ -74,10 +71,41 @@ Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (
   
 For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/), [GitHub](https://github.com/QwenLM/
 
----
+and see also:
+
+https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct
 
 ---
 
+# Model Card for OlympicCoder-32B
+
+OlympicCoder-32B is a code model that achieves very strong performance on competitive coding benchmarks such as LiveCodeBench andthe 2024 International Olympiad in Informatics.
+
+* Repository: https://github.com/huggingface/open-r1
+* Blog post: https://huggingface.co/blog/open-r1/update-3
+
+## Model description
+
+- **Model type:** A 32B parameter model fine-tuned on a decontaminated version of the codeforces dataset.
+- **Language(s) (NLP):** Primarily English
+- **License:** apache-2.0
+- **Finetuned from model:** [Qwen/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct)
+
+## Evaluation
+
+We compare the performance of OlympicCoder models on two main benchmarks for competitive coding:
+
+* **[IOI'2024:](https://github.com/huggingface/ioi)** 6 very challenging problems from the 2024 International Olympiad in Informatics. Models are allowed up to 50 submissions per problem.
+* **[LiveCodeBench:](https://livecodebench.github.io)** Python programming problems source from platforms like CodeForces and LeetCoder. We use the `v4_v5` subset of [`livecodebench/code_generation_lite`](https://huggingface.co/datasets/livecodebench/code_generation_lite), which corresponds to 268 problems. We use `lighteval` to evaluate models on LiveCodeBench using the sampling parameters described [here](https://github.com/huggingface/open-r1?tab=readme-ov-file#livecodebench).
+
+> [!NOTE]
+> The OlympicCoder models were post-trained exclusively on C++ solutions generated by DeepSeek-R1. As a result the performance on LiveCodeBench should be considered to be partially _out-of-domain_, since this expects models to output solutions in Python.
+
+
+For more info on this model, including benchmarks see:
+
+https://huggingface.co/open-r1/OlympicCoder-32B
+
 ---
 
 Model Settings / info: