--- license: apache-2.0 datasets: - JetBrains/KExercises base_model: meta-llama/CodeLlama-7b-hf results: - task: type: text-generation dataset: name: MultiPL-HumanEval (Kotlin) type: openai_humaneval metrics: - name: pass@1 type: pass@1 value: 42.24 tags: - code --- # Kexer models 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. # How to use ```python from transformers import AutoModelForCausalLM, AutoTokenizer # Load pre-trained model and tokenizer model_name = 'JetBrains/CodeLlama-7B-Kexer' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name).to('cuda') # Create and encode input input_text = """\ This function takes an integer n and returns factorial of a number: fun factorial(n: Int): Int {\ """ input_ids = tokenizer.encode( input_text, return_tensors='pt' ).to('cuda') # Generate output = model.generate( input_ids, max_length=60, num_return_sequences=1, early_stopping=True, pad_token_id=tokenizer.eos_token_id, ) # Decode output generated_text = tokenizer.decode(output[0], skip_special_tokens=True) print(generated_text) ``` As with the base model, we can use FIM. To do this, the following format must be used: ``` '
 ' + prefix + '  ' + suffix + ' '
```

# Training setup

The model was trained on one A100 GPU with the following hyperparameters:

|         **Hyperparameter**           |             **Value**              |
|:---------------------------:|:----------------------------------------:|
|           `warmup`            |           10%            |
|        `max_lr`        |          1e-4          |
|        `scheduler`        |          linear          |
|        `total_batch_size`        |          256 (~130K tokens per step)          |
|        `num_epochs`        |          4          |

More details about fine-tuning can be found in the technical report (coming soon).

# Fine-tuning data

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. 

# Evaluation 

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).

Here are the results of our evaluation:

|         **Model name**           |             **Kotlin HumanEval Pass Rate**              |
|:---------------------------:|:----------------------------------------:|
|           `CodeLlama-7B`            |           26.89            |
|        `CodeLlama-7B-Kexer`        |          **42.24**         |

# Ethical considerations and limitations

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.