Update README.md

README.md

@@ -19,10 +19,10 @@ tags:
 
 # Kexer models
 
-Kexer models is a collection of fine-tuned open-source generative text models fine-tuned on Kotlin Exercices dataset. 
-This is a repository for fine-tuned CodeLlama-7b model in the Hugging Face Transformers format.
+Kexer models are a collection of open-source generative text models fine-tuned on the [Kotlin Exercices](https://huggingface.co/datasets/JetBrains/KExercises) dataset. 
+This is a repository for the fine-tuned **CodeLlama-7b** model in the *Hugging Face Transformers* format.
 
-# Model use
+# How to use
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -59,7 +59,7 @@ As with the base model, we can use FIM. To do this, the following format must be
 
 # Training setup
 
-The model was trained on one A100 GPU with following hyperparameters:
+The model was trained on one A100 GPU with the following hyperparameters:
 
 |         **Hyperparameter**           |             **Value**              |
 |:---------------------------:|:----------------------------------------:|
@@ -69,24 +69,23 @@ The model was trained on one A100 GPU with following hyperparameters:
 |        `total_batch_size`        |          256 (~130K tokens per step)          |
 |        `num_epochs`        |          4          |
 
-More details about finetuning can be found in the technical report
+More details about fine-tuning can be found in the technical report.
 
 # Fine-tuning data
 
-For this model we used 15K exmaples of [Kotlin Exercices dataset](https://huggingface.co/datasets/JetBrains/KExercises). Every example follows HumanEval like format. In total dataset contains about 3.5M tokens. 
-For more information about the dataset follow the link.
+For tuning this model, we used 15K exmaples from the synthetically generated [Kotlin Exercices dataset](https://huggingface.co/datasets/JetBrains/KExercises). Every example follows the HumanEval format. In total, the dataset contains about 3.5M tokens. 
 
 # Evaluation 
 
-To evaluate we used Kotlin Humaneval ([more infromation here](https://huggingface.co/datasets/JetBrains/Kotlin_HumanEval))
+For evaluation, we used the [Kotlin HumanEval](https://huggingface.co/datasets/JetBrains/Kotlin_HumanEval) dataset, which contains all 161 tasks from HumanEval translated into Kotlin by human experts. You can find more details about the pre-processing necessary to obtain our results, including the code for running, on the [datasets's page](https://huggingface.co/datasets/JetBrains/Kotlin_HumanEval).
 
-Fine-tuned model:
+Here are the results of our evaluation:
 
 |         **Model name**           |             **Kotlin HumanEval Pass Rate**              |
 |:---------------------------:|:----------------------------------------:|
-|           `base model`            |           26.89            |
-|        `fine-tuned model`        |          **42.24**         |
+|           `CodeLlama-7B`            |           26.89            |
+|        `CodeLlama-7B-Kexer`        |          **42.24**         |
 
-# Ethical Considerations and Limitations
+# Ethical considerations and limitations
 
-CodeLlama-7B-Kexer and its variants are a new technology that carries risks with use. The testing conducted to date could not cover all scenarios. For these reasons, as with all LLMs, CodeLlama-7B-Kexer potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. The model was fine-tuned on a specific data format (Kotlin tasks), and deviation from this format can also lead to inaccurate or undesirable responses to user queries. Therefore, before deploying any applications of CodeLlama-7B-Kexer, developers should perform safety testing and tuning tailored to their specific applications of the model.
+CodeLlama-7B-Kexer is a new technology that carries risks with use. The testing conducted to date has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, CodeLlama-7B-Kexer's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. The model was fine-tuned on a specific data format (Kotlin tasks), and deviation from this format can also lead to inaccurate or undesirable responses to user queries. Therefore, before deploying any applications of CodeLlama-7B-Kexer, developers should perform safety testing and tuning tailored to their specific applications of the model.