Summarization
Transformers
Safetensors
t5
text2text-generation
trl
ppo
reinforcement-learning
text-generation-inference
Instructions to use IrwinD/log_sage_ppo_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IrwinD/log_sage_ppo_model with Transformers:
# 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") - Notebooks
- Google Colab
- Kaggle
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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