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LICENSE DELETED
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- LLAMA 2 COMMUNITY LICENSE AGREEMENT
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NOTICE DELETED
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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
@@ -26,7 +26,7 @@ This is a repository for the **CodeLlama-7b** model fine-tuned on the [KStack](h
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  # Load pre-trained model and tokenizer
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- model_name = 'JetBrains/CodeLlama-7B-KStack'
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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  model = AutoModelForCausalLM.from_pretrained(model_name).to('cuda')
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  | `total_batch_size` | 128 (~65K tokens per step) |
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  | `num_epochs` | 1 |
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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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  * We remove all package lines, such as _package kotlinx.coroutines.channels_
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  * We remove half of the import lines
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- We removed half of the imports to avoid potential hallucinations by the model, where it might attempt to import unnecessary libraries. Additionally, packages were removed because this information is only useful at the project level and may introduce additional noise during the learning process.
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-
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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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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  # Load pre-trained model and tokenizer
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+ model_name = 'JetBrains/CodeLlama-7B-KStack-full'
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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  model = AutoModelForCausalLM.from_pretrained(model_name).to('cuda')
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  | `total_batch_size` | 128 (~65K tokens per step) |
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  | `num_epochs` | 1 |
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+ More details about fine-tuning can be found in the technical report.
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  # Fine-tuning data
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  * We remove all package lines, such as _package kotlinx.coroutines.channels_
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  * We remove half of the import lines
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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).