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:

python

from transformers import pipeline

generator = pipeline("text-generation", model="ajagota71//kaggle/working/irl_llms/outputs/2025-05-16_12-23-55/checkpoints/checkpoint-epoch-200")
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:

python

from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead

tokenizer = AutoTokenizer.from_pretrained("ajagota71//kaggle/working/irl_llms/outputs/2025-05-16_12-23-55/checkpoints/checkpoint-epoch-200")
model = AutoModelForCausalLMWithValueHead.from_pretrained("ajagota71//kaggle/working/irl_llms/outputs/2025-05-16_12-23-55/checkpoints/checkpoint-epoch-200")

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

ajagota71/pythia-410m-fb-detox-checkpoint-epoch-200

TRL Model

Usage

ajagota71/
pythia-410m-fb-detox-checkpoint-epoch-200