Update README.md

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README.md

@@ -33,7 +33,7 @@ configs:
 
 We introduce HumanEval for Kotlin, created from scratch by human experts.
 Solutions and tests for all 161 HumanEval tasks are written by an expert olympiad programmer with 6 years of experience in Kotlin, and independently checked by a programmer with 4 years of experience in Kotlin. 
-The tests we implement are equivalent to the original HumanEval tests for Python.
+The tests we implement are eqivalent to the original HumanEval tests for Python.
 
 # How to use 
 
@@ -82,8 +82,9 @@ def generate(problem):
     problem = tokenizer.encode(problem, return_tensors="pt").to('cuda')
     sample = model.generate(
         problem,
-        max_new_tokens=256,
-        min_new_tokens=128,
+        temperature=0.1,
+        max_length=512,
+        min_new_tokens=5,
         pad_token_id=tokenizer.eos_token_id,
         do_sample=False,
         num_beams=1,
@@ -152,5 +153,4 @@ print(f'Pass rate: {correct/total}')
 
 # Results
 
-We evaluated multiple coding models using this benchmark, and the results are presented in the figure below:
-![results](https://huggingface.co/datasets/JetBrains/Kotlin_HumanEval/resolve/main/model_scores.png)
+We evaluated multiple coding models using this benchmark, and the results are presented in the table below.