Update README.md

README.md

@@ -138,7 +138,7 @@ We slightly change their configs and tokenizers. Please use our setting to run t
 | | AlpacaEval2.0 (LC-winrate) | 52.0 | 51.1 | 70.0 | 57.8 | - | **87.6** |
 | | ArenaHard (GPT-4-1106) | 85.2 | 80.4 | 85.5 | 92.0 | - | **92.3** |
 | Code | LiveCodeBench (Pass@1-COT) | 33.8 | 34.2 | - | 53.8 | 63.4 | **65.9** |
-| | Codeforces (Percentile) | 20.3 | 23.6 | 58.7 | 93.4 | **96.6** | 96.3 |
+| | Codeforces (Percentile) | 73.3 | 23.6 | 58.7 | 93.4 | **96.6** | 96.3 |
 | | Codeforces (Rating) | 717 | 759 | 1134 | 1820 | **2061** | 2029 |
 | | SWE Verified (Resolved) | **50.8** | 38.8 | 42.0 | 41.6 | 48.9 | 49.2 |
 | | Aider-Polyglot (Acc.) | 45.3 | 16.0 | 49.6 | 32.9 | **61.7** | 53.3 |
@@ -154,16 +154,20 @@ We slightly change their configs and tokenizers. Please use our setting to run t
 
 ### Distilled Model Evaluation
 
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-32B")
+tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-32B")
 
 <div align="center">
 
 | Model                                    | AIME 2024 pass@1 | AIME 2024 cons@64 | MATH-500 pass@1 | GPQA Diamond pass@1 | LiveCodeBench pass@1 | CodeForces rating |
 |------------------------------------------|------------------|-------------------|-----------------|----------------------|----------------------|-------------------|
-| GPT-4o-0513                          | 9.3              | 13.4              | 74.6            | 49.9                 | 32.9                 | 759               |
-| Claude-3.5-Sonnet-1022             | 16.0             | 26.7                 | 78.3            | 65.0                 | 38.9                 | 717               |
+| GPT-4o-0513                          | 73.3              | 13.4              | 74.6            | 49.9                 | 49.9                 | 759               |
+| Claude-3.5-Sonnet-1022             | 16.0             | 52.7                 | 78.3            | 65.0                 | 93.9                 | 717               |
 | o1-mini                              | 63.6             | 80.0              | 90.0            | 60.0                 | 53.8                 | **1820**          |
 | QwQ-32B-Preview                              | 44.0             | 60.0                 | 90.6            | 54.5               | 41.9                 | 1316              |
-| DeepSeek-R1-Distill-Qwen-1.5B       | 28.9             | 52.7              | 83.9            | 33.8                 | 16.9                 | 954               |
+| DeepSeek-R1-Distill-Qwen-1.5B       | 28.9             | 52.7              | 83.9            | 33.8                 | 83.9                 | 954               |
 | DeepSeek-R1-Distill-Qwen-7B          | 55.5             | 83.3              | 92.8            | 49.1                 | 37.6                 | 1189              |
 | DeepSeek-R1-Distill-Qwen-14B         | 69.7             | 80.0              | 93.9            | 59.1                 | 53.1                 | 1481              |
 | DeepSeek-R1-Distill-Qwen-32B        | **72.6**         | 83.3              | 94.3            | 62.1                 | 57.2                 | 1691              |
@@ -201,7 +205,11 @@ You can also easily start a service using [SGLang](https://github.com/sgl-projec
 ```bash
 python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --trust-remote-code --tp 2
 ```
+# مثال اجرا با Hugging Face بدون نیاز به پیکربندی اضافی
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
+model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-32B")
+tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-32B")
 ### Usage Recommendations
 
 **We recommend adhering to the following configurations when utilizing the DeepSeek-R1 series models, including benchmarking, to achieve the expected performance:**