datasets:
- samhog/psychology-10k
Psychology Alpaca 🍩
This is a LLaMA-7B language model trained on 10.000 psychology-related prompts and answers generated by ChatGPT. The model was trained on a single A100 GPU from Google Colab. The model shows some knowledge in the field of psychology and generally performs better than its base model parent.
Background 💡
This model was developed as part of a thesis project in the field of machine learning and psychology. It was used as a base model for further fine-tuning using reinforcement learning. The goal of the thesis was to compare reinforcement learning from human feedback and AI feedback.
Paper 📜
"Comparison Between RLHF and RLAIF in Fine-Tuning a Large Language Model"
The paper can be found here!
Usage 🏂
from peft import PeftModel
from transformers import LLaMATokenizer, LLaMAForCausalLM, GenerationConfig
tokenizer = LLaMATokenizer.from_pretrained("decapoda-research/llama-7b-hf")
# Load model weights
model = LLaMAForCausalLM.from_pretrained(
"decapoda-research/llama-7b-hf",
load_in_8bit=True,
device_map="auto",
)
# Add Peft layer to initial weights in order to get the Psychology Alpaca weights
model = PeftModel.from_pretrained(model, "kth/psychology-alpaca")
Links: RLHF model; RLAIF model
Authors: Samuel Höglund, samhog@kth.se; Josef Khedri, jkhedri@kth.se