How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
# Warning: Pipeline type "summarization" is no longer supported in transformers v5.
# You must load the model directly (see below) or downgrade to v4.x with:
# 'pip install "transformers<5.0.0'
from transformers import pipeline

pipe = pipeline("summarization", model="IrwinD/log_sage_ppo_model")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("IrwinD/log_sage_ppo_model")
model = AutoModelForSeq2SeqLM.from_pretrained("IrwinD/log_sage_ppo_model")
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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="IrwinD//tmp/tmpoz9k3o9o/IrwinD/log_sage_ppo_model")
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("IrwinD//tmp/tmpoz9k3o9o/IrwinD/log_sage_ppo_model")
model = AutoModelForCausalLMWithValueHead.from_pretrained("IrwinD//tmp/tmpoz9k3o9o/IrwinD/log_sage_ppo_model")

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