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DarshanDeshpande/gemma_2b_oasst1_ppo_model
gemma_2b_oasst1_ppo_model is a reinforcement learning model from DarshanDeshpande. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
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.
Downloads · 30 days
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.json17.5 MB · 69%
From the Hugging Face model README
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.
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="DarshanDeshpande//tmp/SLURM_20253966/tmph2bl7syr/DarshanDeshpande/gemma_2b_oasst1_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("DarshanDeshpande//tmp/SLURM_20253966/tmph2bl7syr/DarshanDeshpande/gemma_2b_oasst1_ppo_model")
model = AutoModelForCausalLMWithValueHead.from_pretrained("DarshanDeshpande//tmp/SLURM_20253966/tmph2bl7syr/DarshanDeshpande/gemma_2b_oasst1_ppo_model")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])