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LICENSE DELETED
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- LLAMA 2 COMMUNITY LICENSE AGREEMENT
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- Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved
 
 
README.md CHANGED
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  # Model description
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- This is a repository for the **CodeLlama-7b** model fine-tuned on the [KStack-clean](https://huggingface.co/datasets/JetBrains/KStack-clean) dataset with rule-based filtering, in the *Hugging Face Transformers* format. KStack-clean is a small subset of [KStack](https://huggingface.co/datasets/JetBrains/KStack), the largest collection of permissively licensed Kotlin code, automatically filtered to include files that have the highest "educational value for learning algorithms in Kotlin".
 
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- # How to use
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  | `total_batch_size` | 32 (~30K tokens per step) |
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  | `num_epochs` | 2 |
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- More details about fine-tuning can be found in the technical report (coming soon!).
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  # Fine-tuning data
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- For tuning the model, we used 25K exmaples from the [KStack-clean](https://huggingface.co/datasets/JetBrains/KStack-clean) dataset, selected from the larger [KStack](https://huggingface.co/datasets/JetBrains/KStack) dataset according to educational value for learning algorithms. In total, the dataset contains about 23M tokens.
 
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  # Evaluation
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- 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).
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- Here are the results of our evaluation:
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  | **Model name** | **Kotlin HumanEval Pass Rate** |
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  |:---------------------------:|:----------------------------------------:|
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- | `CodeLlama-7B` | 26.89 |
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- | `CodeLlama-7B-KStack-clean` | **37.89** |
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  # Ethical Considerations and Limitations
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- CodeLlama-7B-KStack-clean 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-KStack-clean'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-KStack-clean, developers should perform safety testing and tuning tailored to their specific applications of the model.
 
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  # Model description
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+ CodeLlama-7B-KStack-clean model is a fine-tuned open-source generative text model fine-tuned on [JetBrains/KStack-clean](https://huggingface.co/datasets/JetBrains/KStack-clean) dataset.
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+ This is a repository for fine-tuned CodeLlama-7b model in the Hugging Face Transformers format.
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+ # Model use
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
 
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  | `total_batch_size` | 32 (~30K tokens per step) |
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  | `num_epochs` | 2 |
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+ More details about finetuning can be found in the technical report
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  # Fine-tuning data
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+ For this model we used 25K exmaples of [KStack-clean](https://huggingface.co/datasets/JetBrains/KStack-clean) selected according to educational value for learning algorithms. In total dataset contains about 23M tokens.
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+ For more information about the dataset follow the link.
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  # Evaluation
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+ To evaluate we used Kotlin Humaneval ([more infromation here](https://huggingface.co/datasets/JetBrains/Kotlin_HumanEval))
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+ Fine-tuned model:
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  | **Model name** | **Kotlin HumanEval Pass Rate** |
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  |:---------------------------:|:----------------------------------------:|
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+ | `base model` | 26.89 |
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+ | `fine-tuned model` | 37.89 |
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  # Ethical Considerations and Limitations
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+ CodeLlama-7B-KStack-clean 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-KStack-clean 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-KStack-clean, developers should perform safety testing and tuning tailored to their specific applications of the model.