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---
license: apache-2.0
datasets:
- JetBrains/KExercises
base_model: deepseek-ai/deepseek-coder-6.7b-base
results:
- task:
type: text-generation
dataset:
name: MultiPL-HumanEval (Kotlin)
type: openai_humaneval
metrics:
- name: pass@1
type: pass@1
value: 55.28
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 **Deepseek-coder-6.7b** 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/Deepseek-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:
```
'<｜fim▁begin｜>' + prefix + '<｜fim▁hole｜>' + suffix + '<｜fim▁end｜>'
```
# Training setup
The model was trained on one A100 GPU with 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 finetuning can be found in the technical report
# Fine-tuning data
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.
# 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** |
|:---------------------------:|:----------------------------------------:|
| `Deepseek-7B` | 40.99 |
| `Deepseek-7B-Kexer` | **55.28** |
# Ethical Considerations and Limitations
Deepseek-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-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-7B-Kexer, developers should perform safety testing and tuning tailored to their specific applications of the model. |