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sjkwon/
sft-mdo-diverse-train-nllb-200-600M-step200

Name: sft-mdo-diverse-train-nllb-200-600M-step200
Availability: InStock

reinforcement-learning

apache-2.0

endpoints_compatible

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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:

python

from transformers import pipeline

generator = pipeline("text-generation", model="sjkwon//tmp/tmphkmsuqs1/sjkwon/sft-mdo-diverse-train-nllb-200-600M-step200")
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("sjkwon//tmp/tmphkmsuqs1/sjkwon/sft-mdo-diverse-train-nllb-200-600M-step200")
model = AutoModelForCausalLMWithValueHead.from_pretrained("sjkwon//tmp/tmphkmsuqs1/sjkwon/sft-mdo-diverse-train-nllb-200-600M-step200")

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