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LICENSE DELETED
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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
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  ---
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  license: apache-2.0
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- datasets:
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- - JetBrains/KExercises
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- base_model: meta-llama/CodeLlama-7b-hf
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- results:
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- - task:
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- type: text-generation
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- dataset:
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- name: MultiPL-HumanEval (Kotlin)
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- type: openai_humaneval
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- metrics:
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- - name: pass@1
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- type: pass@1
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- value: 42.24
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- tags:
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- - code
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  ---
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- # Kexer models
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- Kexer models are a collection of open-source generative text models fine-tuned on the [Kotlin Exercices](https://huggingface.co/datasets/JetBrains/KExercises) dataset.
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- This is a repository for the fine-tuned **CodeLlama-7b** model in the *Hugging Face Transformers* format.
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-
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- # How to use
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-
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- ```python
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- from transformers import AutoModelForCausalLM, AutoTokenizer
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-
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- # Load pre-trained model and tokenizer
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- model_name = 'JetBrains/CodeLlama-7B-Kexer'
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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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-
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- # Create and encode input
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- input_text = """\
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- This function takes an integer n and returns factorial of a number:
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- fun factorial(n: Int): Int {\
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- """
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- input_ids = tokenizer.encode(
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- input_text, return_tensors='pt'
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- ).to('cuda')
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-
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- # Generate
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- output = model.generate(
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- input_ids, max_length=60, num_return_sequences=1,
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- early_stopping=True, pad_token_id=tokenizer.eos_token_id,
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- )
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-
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- # Decode output
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- generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
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- print(generated_text)
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- ```
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-
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- As with the base model, we can use FIM. To do this, the following format must be used:
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- ```
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- '<PRE> ' + prefix + ' <SUF> ' + suffix + ' <MID>'
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- ```
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  # Training setup
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- The model was trained on one A100 GPU with the following hyperparameters:
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  | **Hyperparameter** | **Value** |
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  |:---------------------------:|:----------------------------------------:|
@@ -67,25 +16,22 @@ The model was trained on one A100 GPU with the following hyperparameters:
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  | `max_lr` | 1e-4 |
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  | `scheduler` | linear |
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  | `total_batch_size` | 256 (~130K tokens per step) |
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- | `num_epochs` | 4 |
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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 this model, we used 15K exmaples from the synthetically generated [Kotlin Exercices](https://huggingface.co/datasets/JetBrains/KExercises) dataset. Every example follows the HumanEval format. In total, the dataset contains about 3.5M 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-Kexer` | **42.24** |
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- # Ethical considerations and limitations
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- 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.
 
 
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  ---
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  license: apache-2.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ # Model summary
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+ This is CodeLlama model fine-tuned on Kotlin Exercices dataset.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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9
  # Training setup
10
 
11
+ The model was trained on one A100 GPU with following hyperparameters:
12
 
13
  | **Hyperparameter** | **Value** |
14
  |:---------------------------:|:----------------------------------------:|
 
16
  | `max_lr` | 1e-4 |
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  | `scheduler` | linear |
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  | `total_batch_size` | 256 (~130K tokens per step) |
 
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20
 
21
  # Fine-tuning data
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23
+ For this model we used 15K exmaples of Kotlin Exercices dataset. For more information about the dataset follow th link.
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25
  # Evaluation
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+ To evaluate we used Kotlin Humaneval (more infromation here)
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+ Fine-tuned model:
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+ **Kotlin Humaneval: 42.24**
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+ **Kotlin Compleation: 0.344**
 
 
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34
+ Base model:
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36
+ **Kotlin Humaneval: 26.89**
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+ **Kotlin Compleation: 0.388**