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llama-7b-es-ppo-adpater
renyulin/llama-7b-es-ppo-adpater
llama-7b-es-ppo-adpater is a reinforcement learning model from renyulin. 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.
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Public
Updated Jul 3, 2023
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At a glance
- Task
- Reinforcement Learning
- Library
- transformers
- License
- apache-2.0
- Access
- Public
- Created
- Jul 3, 2023
- Updated
- Jul 3, 2023
- SHA
- 72bae152
Model card
From the Hugging Face model README
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="renyulin//tmp/tmp6w__m82c/renyulin/llama-7b-es-ppo-adpater")
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("renyulin//tmp/tmp6w__m82c/renyulin/llama-7b-es-ppo-adpater")
model = AutoModelForCausalLMWithValueHead.from_pretrained("renyulin//tmp/tmp6w__m82c/renyulin/llama-7b-es-ppo-adpater")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
- Task
- Reinforcement Learning
- Library
- transformers
- License
- apache-2.0
- Created
- Jul 3, 2023
- Updated
- Jul 3, 2023