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---
datasets:
- fbnhnsl/FIM_Solidity_Dataset
language:
- en
metrics:
- bleu
- meteor
base_model:
- deepseek-ai/deepseek-coder-1.3b-base
pipeline_tag: text-generation
tags:
- code
license: cc-by-4.0
---
This is a fine-tuned deepseek-coder-1.3b-base model for automatic completion of Solidity code. The model was fine-tuned using the Parameter Efficient Fine-tuning (PEFT) method
Quantized Low Rank Adaptation (QLoRA) and a Fill-in-the-Middle (FIM) transformed and Slither audited dataset.
Example usage:
```python
# Load the fine-tuned model
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pretrained_checkpoint = 'deepseek-ai/deepseek-coder-1.3b-base'
finetuned_checkpoint = 'path/to/model'
tokenizer = AutoTokenizer.from_pretrained(finetuned_checkpoint)
old_model = AutoModelForCausalLM.from_pretrained(pretrained_checkpoint)
old_model.resize_token_embeddings(len(tokenizer))
finetuned_model = PeftModel.from_pretrained(old_model, checkpoint).to(device)
# ----------------------------------------------------------------------------
# General automatic code completion
code_example = '''<|secure_function|>\tfunction add('''
model_inputs = tokenizer(code_example, return_tensors="pt").to(device)
input_ids = model_inputs["input_ids"]
attention_mask = model_inputs["attention_mask"]
generated_ids = finetuned_model.generate(input_ids,
do_sample=True,
max_length=256,
num_beams=4,
temperature=0.3,
pad_token_id=tokenizer.eos_token_id,
attention_mask=attention_mask)
print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0])
# Expected output:
# function add(uint256 a, uint256 b) internal pure returns (uint256) {
# return a + b;
# }
# ----------------------------------------------------------------------------
# Fill-in-the-middle
def generate_fim(prefix, suffix, model, tokenizer, max_length=256):
input_text = f"<|fim_begin|>{prefix}<|fim_hole|>{suffix}<|fim_end|>"
inputs = tokenizer.encode(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(
inputs,
max_length=max_length,
num_beams=8,
temperature=0.3,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
middle = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
return prefix + middle + suffix
prefix = '''pragma solidity ^0.8.0;\n\n'''
suffix = '''\n\ncontract FOO is Context, IERC20, Ownable {'''
print(generate_fim(prefix, suffix, finetuned_model, tokenizer))
# Expected output:
# pragma solidity ^0.8.0;
#
# import "@openzeppelin/contracts/utils/Context.sol" as Context;
# import "@openzeppelin/contracts/interfaces/IERC20.sol" as IERC20;
# import "@openzeppelin/contracts/access/Ownable.sol" as Ownable;
#
# contract FOO is Context, IERC20, Ownable {
```
If you wish to use this model, you can cite it as follows:
```latex
@misc{hensel2025fim_model,
title = {Finetuned deepseek-coder-1.3b-base model for automatic code completion of Solidity code},
author={Fabian Hensel},
year={2025}
}
``` |