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+ Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved
README.md ADDED
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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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+
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+ # Kexer models
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+
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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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+
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+ # Training setup
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+
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+ The model was trained on one A100 GPU with the following hyperparameters:
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+
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+ | **Hyperparameter** | **Value** |
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+ |:---------------------------:|:----------------------------------------:|
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+ | `warmup` | 10% |
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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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+
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+ More details about fine-tuning can be found in the technical report.
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+
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+ # Fine-tuning data
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+
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+ 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.
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+
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+ # Evaluation
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+
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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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+
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+ Here are the results of our evaluation:
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+
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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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+
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+ # Ethical considerations and limitations
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+
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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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+ "use_cache": true,
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+ "vocab_size": 32016
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+ }
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+ "▁<EOT>",
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+ "▁<EOT>"
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+ "content": "▁<EOT>",
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+ "▁<EOT>",
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+ "▁<EOT>"
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+ "eot_token": "▁<EOT>",
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