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fbnhnsl/
Finetuned_deepseek-coder-1.3b-base_Solidity_Constructs

Name: Finetuned_deepseek-coder-1.3b-base_Solidity_Constructs
Availability: InStock

safetensors

code

text-generation

fbnhnsl/FIM_Solidity_Dataset

deepseek-ai/deepseek-coder-1.3b-base

finetune

mit

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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 dataset consisting of Solidity constructs (functions, modifiers, mappings, etc.). The model has a maximum sequence length of 256 tokens.

General Fine-tuning informations:

Epochs: 2
Optimizer: paged AdamW 8-bit
Batch size: 8
LoRA target modules: ["q_proj", "o_proj", "k_proj", "v_proj"]
Quantization type: normal float 4-bit
QLoRA compute type: brain float 16-bit
Total time: 1 hour 23 minutes
Accelerator: Nvidia L4 Tensor Core GPU

Some of the Hyperparameters were determined using Hyperparameter optimization with Ray Tune. The corresponding result for the best trial were:

Learning rate: 0.00016
Weight decay: 0.0534
Warmup steps: 100
Gradient accumulation steps: 2
LoRA rank: 64
LoRA alpha: 64
LoRA dropout: 0.0934665

The Fine-tuning results are:

Training loss: ~0.7
Validation loss: ~0.75

The model was evaluated with the test split compared to the base model. The metrics were used: Perplexity, BLEU and METEOR. The Perplexity results are:

Perplexity Base Model: 12.08
Perplexity Fine-tuned Model: 2.19

The following code shows an example of how to use the model:

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}
}

fbnhnsl/Finetuned_deepseek-coder-1.3b-base_Solidity_Constructs

fbnhnsl/
Finetuned_deepseek-coder-1.3b-base_Solidity_Constructs