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

model_name = "pszemraj/bart-large-mnli-instruct-dolly_hhrlhf-v1"
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)

The use of the generation config is optional, it can be replaced by other generation params.

Intended Uses & Limitations

This is not tuned with RLHF, etc, and may produce offensive results.
While larger than BART-base, this model is relatively small compared to recent autoregressive models (MPT-7b, LLaMA, etc.), and therefore it's "cognition" capabilities may be practically limited for some tasks.

Training

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

pszemraj/bart-large-mnli-dolly_hhrlhf-v1

bart-large-mnli: instruction tuned - v1

Model description

Intended Uses & Limitations

Training

Training hyperparameters

pszemraj/
bart-large-mnli-dolly_hhrlhf-v1