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---
license: apache-2.0
datasets:
- JetBrains/KStack
results:
- task:
type: text-generation
dataset:
name: MultiPL-HumanEval (Kotlin)
type: openai_humaneval
metrics:
- name: pass@1
type: pass@1
value: 29.19
tags:
- code
---
# KStack-full models
KStack-full models is a collection of fine-tuned open-source generative text models fine-tuned on KStack dataset with rule-based filtering.
This is a repository for fine-tuned CodeLlama-7b model in the Hugging Face Transformers format.
# Model use
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load pre-trained model and tokenizer
model_name = 'JetBrains/CodeLlama-7B-KStack-full'
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,
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:
```
'<PRE> ' + prefix + ' <SUF> ' + suffix + ' <MID>'
```
# Training setup
The model was trained on one A100 GPU with following hyperparameters:
| **Hyperparameter** | **Value** |
|:---------------------------:|:----------------------------------------:|
| `warmup` | 5% |
| `max_lr` | 1e-6 |
| `num_epochs` | 1 |
| 'attention_dropout' | 0.1 |
| `scheduler` | cosine |
| `total_batch_size` | 128 (~65K tokens per step) |
| `num_epochs` | 1 |
More details about finetuning can be found in the technical report
# Data filtering
To increase the quality of the dataset and filter out statistical outliers such as homework assignments, we filter out the dataset entries according to the following rules:
* We filter out files which belong to the low-popular repos (the sum of stars and forks is less than 6)
* Next, we filter out files which belong to the repos with less than 5 Kotlin files
* Finally, we remove files which have less than 20 SLOC
We clean the content of the remaining dataset entries according to the following rules:
* We remove all non-ASCII entries
* We remove all package lines such as _package kotlinx.coroutines.channels_
* We remove half of the import lines.
# Evaluation
To evaluate we used [Kotlin Humaneval](https://huggingface.co/datasets/JetBrains/Kotlin_HumanEval)
Fine-tuned model:
| **Model name** | **Kotlin HumanEval Pass Rate** |
|:---------------------------:|:----------------------------------------:|
| `base model` | 26.09 |
| `fine-tuned model` | **29.19** |
# Ethical Considerations and Limitations
CodeLlama-7B-KStack-full and its variants are a new technology that carries risks with use. The testing conducted to date could not cover all scenarios. For these reasons, as with all LLMs, CodeLlama-7B-KStack-full'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-KStack-full, developers should perform safety testing and tuning tailored to their specific applications of the model. |