Reinforcement Learning
Transformers
Safetensors
llama
text-generation
trl
ppo
text-generation-inference
Instructions to use MattBou00/SequentialLR001_2000samples-checkpoint-epoch-40 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MattBou00/SequentialLR001_2000samples-checkpoint-epoch-40 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MattBou00/SequentialLR001_2000samples-checkpoint-epoch-40") model = AutoModelForCausalLM.from_pretrained("MattBou00/SequentialLR001_2000samples-checkpoint-epoch-40") - 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="MattBou00//content/IRL-Bayesian/outputs/2025-11-22_15-09-36/checkpoints/checkpoint-epoch-40")
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("MattBou00//content/IRL-Bayesian/outputs/2025-11-22_15-09-36/checkpoints/checkpoint-epoch-40")
model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-11-22_15-09-36/checkpoints/checkpoint-epoch-40")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
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