metadata

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

This model is a fine-tuned version of facebook/bart-base on the pszemraj/dolly_hhrlhf-text2text dataset.

Model description

text2text models fine-tuned on a modified dataset for text2text generation based on the relatively more permissive mosaicml/dolly_hhrlhf dataset.

Basic usage in 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