HW2-ppo

TRL Model

This is a TRL language model that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.

Usage

To use this model for inference, first install the TRL library:

python -m pip install trl

You can then generate text as follows:

from transformers import pipeline

generator = pipeline("text-generation", model="bachephysicdun//private/var/folders/t_/g9d82w112xv0f54780gpp8_00000gn/T/tmpk164fqbu/bachephysicdun/HW2-ppo")
outputs = generator("Hello, my llama is cute")

If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:

from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead

tokenizer = AutoTokenizer.from_pretrained("bachephysicdun//private/var/folders/t_/g9d82w112xv0f54780gpp8_00000gn/T/tmpk164fqbu/bachephysicdun/HW2-ppo")
model = AutoModelForCausalLMWithValueHead.from_pretrained("bachephysicdun//private/var/folders/t_/g9d82w112xv0f54780gpp8_00000gn/T/tmpk164fqbu/bachephysicdun/HW2-ppo")

inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])