The DeepSeek-1.3B model fine-tuned with CVEFix for vulnerability fixing
Training Details
- batch size: 2
- learning rate: 3e-5
- epochs: 2
- training steps: 5516
Prompt
{at most 20 lines before the buggy lines} {buggy lines} {at most 20 lines after the buggy lines}
TfLiteStatus HardSwishEval(TfLiteContext* context, TfLiteNode* node) {\n HardSwishData* data = static_cast<HardSwishData*>(node->user_data);
// buggy lines start:
const TfLiteTensor* input = GetInput(context, node, 0);
TfLiteTensor* output = GetOutput(context, node, 0);
// buggy lines end
switch (input->type) {
case kTfLiteFloat32: {
if (kernel_type == kReference) {
reference_ops::HardSwish(
GetTensorShape(input), GetTensorData<float>(input),
GetTensorShape(output), GetTensorData<float>(output));
} else {
optimized_ops::HardSwish(
GetTensorShape(input), GetTensorData<float>(input),
GetTensorShape(output), GetTensorData<float>(output));
}
return kTfLiteOk;
} break;
case kTfLiteUInt8: {
HardSwishParams& params = data->params;
if (kernel_type == kReference) {
reference_ops::HardSwish(
params, GetTensorShape(input), GetTensorData<uint8_t>(input),
GetTensorShape(output), GetTensorData<uint8_t>(output));
} else {
// fixed lines:
The model is trained to take the prompt and generate the fixed lines replacing the buggy lines
const TfLiteTensor* input;
TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, 0, &input));
TfLiteTensor* output;
TF_LITE_ENSURE_OK(context, GetOutputSafe(context, node, 0, &output));
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