---
license: cc-by-sa-3.0
inference: false
language:
- en
library_name: transformers
pipeline_tag: text2text-generation
datasets:
- pszemraj/dolly_hhrlhf-text2text
tags:
- instruct
- dolly_hhrlhf
---

# bart-base-instruct: dolly_hhrlhf

<a href="https://colab.research.google.com/gist/pszemraj/a0c0a8cc24abfbf609f75f9d5c56c348/bart-base-instruct-example.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the pszemraj/dolly_hhrlhf-text2text dataset.

## Model description

text2text models fine-tuned on a [modified dataset for text2text generation](https://huggingface.co/datasets/pszemraj/dolly_hhrlhf-text2text)  based on the relatively more permissive  [mosaicml/dolly_hhrlhf](https://huggingface.co/datasets/mosaicml/dolly_hhrlhf) dataset.

Basic usage in Python:

```python
# pip install -q transformers accelerate
from transformers import pipeline, GenerationConfig

model_name = "pszemraj/bart-base-instruct-dolly_hhrlhf"
assistant = pipeline(
    "text2text-generation",
    model_name,
    device_map="auto"
)
cfg = GenerationConfig.from_pretrained(model_name)

# pass an 'instruction' as the prompt to the pipeline
prompt = "Write a guide on how to become a ninja while working a 9-5 job."
result = assistant(prompt, generation_config=cfg)[0]["generated_text"]
print(result)
```

> using the generation config is optional, can subsitute with other generation params.

## Intended uses & limitations

- this is **not** tuned with RLHF etc, and may output offensive results
- this model is rather small (~600 MB) and therefore it's "cognition" abilities are rather limited.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3.0