68 lines
3.4 KiB
Markdown
68 lines
3.4 KiB
Markdown
---
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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: JetBrains/deepseek-coder-6.7B-kexer
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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: 55.28
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tags:
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- code
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library_name: transformers
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pipeline_tag: text-generation
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---
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# Deepseek-Coder-6.7B-kexer-GGUF
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This is quantized version of [JetBrains/deepseek-coder-6.7B-kexer](https://huggingface.co/JetBrains/deepseek-coder-6.7B-kexer) created using llama.cpp
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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 **Deepseek-coder-6.7b** model in the *Hugging Face Transformers* format.
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# How to use
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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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'<｜fim▁begin｜>' + prefix + '<｜fim▁hole｜>' + suffix + '<｜fim▁end｜>'
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```
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# Training setup
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The model was trained on one A100 GPU with following hyperparameters:
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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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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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| `Deepseek-coder-6.7B` | 40.99 |
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| `Deepseek-coder-6.7B-kexer` | **55.28** |
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# Ethical considerations and limitations
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Deepseek-coder-6.7B-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, Deepseek-coder-6.7B-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 Deepseek-coder-6.7B-kexer, developers should perform safety testing and tuning tailored to their specific applications of the model. |