
# SpecCoder 33bn v1

This model is a fine-tuned version of [DeepSeek Coder 33b Instruct](https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct) on a synthetic dataset of ~39k solidity smart contracts.

It achieves the following results on the evaluation set:
- Loss: 0.4303

## Model description

The model was fine-tuned through the LoRA framework.

## Intended uses & limitations

To generate solidity smart contracts.

## Training and evaluation data

0.5% of hold-out validation data

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4096        | 1.0   | 588  | 0.4338          |
| 0.4415        | 2.0   | 1176 | 0.4303          |


### Framework versions

- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.1.0
- Datasets 2.17.0
- Tokenizers 0.15.2
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