---
license: llama2
tags:
- generated_from_trainer
datasets:
- AshtonIsNotHere/nlp_pp_code_dataset
metrics:
- accuracy
model-index:
- name: CodeLlama_7B_nlp_pp
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: AshtonIsNotHere/nlp_pp_code_dataset
      type: AshtonIsNotHere/nlp_pp_code_dataset
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8968056729128353
---

# CodeLlama_7B_nlp_pp

This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the AshtonIsNotHere/nlp_pp_code_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4129
- Accuracy: 0.8968

## Model description

This model has been fine-tuned for code completion on a dataset of NLP++ code. 

## Intended uses & limitations

More information needed

## Training and evaluation data

Dataset consists of a combination of scraped NLP++ code and NLP++ code examples from the [VisualText website](https://visualtext.org/help/).

## Training procedure

This model is trained in a multinode, multi-gpu setup with DeepSpeed Z3. For more information on the training setup, check out the [GitHub repo](https://github.com/ashtonomy/nlp_pp_code_completion).

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.00012
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 61   | 0.5100          | 0.8726   |
| No log        | 1.99  | 122  | 0.4129          | 0.8968   |
| No log        | 2.99  | 183  | 0.4166          | 0.9072   |
| No log        | 4.0   | 245  | 0.4595          | 0.9090   |
| No log        | 5.0   | 306  | 0.5181          | 0.9093   |
| No log        | 5.99  | 367  | 0.5553          | 0.9090   |
| No log        | 6.97  | 427  | 0.5603          | 0.9089   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3