---
base_model: TinyPixel/Llama-2-7B-bf16-sharded
tags:
- generated_from_trainer
datasets:
- dialogstudio
- Andyrasika/TweetSumm-tuned
model-index:
- name: experiments
  results: []
license: creativeml-openrail-m
language:
- en
metrics:
- accuracy
library_name: transformers
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# experiments

This model is a fine-tuned version of [TinyPixel/Llama-2-7B-bf16-sharded](https://huggingface.co/TinyPixel/Llama-2-7B-bf16-sharded) on the dialogstudio dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8522

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9048        | 0.4   | 22   | 1.9220          |
| 1.824         | 0.8   | 44   | 1.8809          |
| 1.6784        | 1.2   | 66   | 1.8619          |
| 1.77          | 1.6   | 88   | 1.8537          |
| 1.6501        | 2.0   | 110  | 1.8522          |

```
from peft import AutoPeftModelForCausalLM

trained_model = AutoPeftModelForCausalLM.from_pretrained(
    "Andyrasika/fine-tuning-llama",
    low_cpu_mem_usage=True,
)

merged_model = model.merge_and_unload()
merged_model.save_pretrained("merged_model", safe_serialization=True)
tokenizer.save_pretrained("merged_model")
```



### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3