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---
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 |