Add link to paper

README.md

@@ -1,38 +1,40 @@
----
-license: apache-2.0
----
-
-We introduced a new model designed for the Code generation task. Its test accuracy on the HumanEval base dataset surpasses that of GPT-4 Turbo (April 2024). (90.9% vs 90.2%).
-
-Additionally, compared to previous open-source models, AutoCoder offers a new feature: it can **automatically install the required packages** and attempt to run the code until it deems there are no issues, **whenever the user wishes to execute the code**.
-
-See details on the [AutoCoder GitHub](https://github.com/bin123apple/AutoCoder).
-
-Simple test script:
-
-```
-model_path = ""
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-model = AutoModelForCausalLM.from_pretrained(model_path, 
-                                             device_map="auto")
-
-HumanEval = load_dataset("evalplus/humanevalplus")
-
-Input = "" # input your question here
- 
-messages=[
-    { 'role': 'user', 'content': Input}
-]
-inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, 
-                                        return_tensors="pt").to(model.device)
-
-outputs = model.generate(inputs, 
-                        max_new_tokens=1024, 
-                        do_sample=False, 
-                        temperature=0.0,
-                        top_p=1.0, 
-                        num_return_sequences=1, 
-                        eos_token_id=tokenizer.eos_token_id)
-
-answer = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
-```
+---
+license: apache-2.0
+---
+
+We introduced a new model designed for the Code generation task. Its test accuracy on the HumanEval base dataset surpasses that of GPT-4 Turbo (April 2024). (90.9% vs 90.2%).
+
+Additionally, compared to previous open-source models, AutoCoder offers a new feature: it can **automatically install the required packages** and attempt to run the code until it deems there are no issues, **whenever the user wishes to execute the code**.
+
+See details on the [AutoCoder GitHub](https://github.com/bin123apple/AutoCoder).
+
+Simple test script:
+
+```
+model_path = ""
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path, 
+                                             device_map="auto")
+
+HumanEval = load_dataset("evalplus/humanevalplus")
+
+Input = "" # input your question here
+ 
+messages=[
+    { 'role': 'user', 'content': Input}
+]
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, 
+                                        return_tensors="pt").to(model.device)
+
+outputs = model.generate(inputs, 
+                        max_new_tokens=1024, 
+                        do_sample=False, 
+                        temperature=0.0,
+                        top_p=1.0, 
+                        num_return_sequences=1, 
+                        eos_token_id=tokenizer.eos_token_id)
+
+answer = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
+```
+
+Paper: https://arxiv.org/abs/2405.14906