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="baek26//tmp/tmpmkglj6_g/baek26/dialogsum_8455_bart-dialogsum")
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("baek26//tmp/tmpmkglj6_g/baek26/dialogsum_8455_bart-dialogsum")
model = AutoModelForCausalLMWithValueHead.from_pretrained("baek26//tmp/tmpmkglj6_g/baek26/dialogsum_8455_bart-dialogsum")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
Runs of baek26 bart-dialogsum-oracle on huggingface.co
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