# SpecCoder 6.7bn v2

This model is a fine-tuned version of [DeepSeek Coder 6.7b Instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) on a synthetic dataset of ~26k solidity smart contracts.

It achieves the following results on the evaluation set:
- Loss: 0.4567

## Model Description

The model was fine-tune through DeepSpeed ZeRO3 framework.

## Intended uses & limitations

To generate solidity smart contracts.

## Training and evaluation data

5% hold out validation data.

## Training procedure

### Training hyperparameters

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

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4531        | 1.0   | 780  | 0.4567          |


### Framework versions

- DeepSpeed 0.13.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
{"language": ["en"], "license": "mit", "tags": ["DeepSpeed"], "pipeline_tag": "text-generation"}
transformers pytorch llama text-generation DeepSpeed conversational en license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us