metadata
base_model:
- HuggingFaceM4/idefics2-8b
language:
- en
license: apache-2.0
pipeline_tag: image-text-to-text
library_name: transformers
The Era of Real-World Human Interaction: RL from User Conversations
This repository contains the lil-lab/respect model, based on the ACL paper Retrospective Learning from Interactions. For more resources, please see https://lil-lab.github.io/respect and https://github.com/lil-lab/respect.
Sample Usage
To get started with the model, follow these steps:
1. Setting up Environment
Prepare your conda environment:
conda create -n respect python=3.9.18
pip install -r requirements.txt
pip install -e .
2. Download Data
from datasets import load_dataset
ds = load_dataset("lil-lab/respect", name="turn", split="train")
3. Load Model Checkpoints
Download checkpoints and load the model using transformers and peft:
import torch
from transformers import Idefics2ForConditionalGeneration
from peft import PeftModel
checkpoint = "HuggingFaceM4/idefics2-8b"
model_id = 'lil-lab/respect'
model = Idefics2ForConditionalGeneration.from_pretrained(
checkpoint, torch_dtype=torch.bfloat16)
peft_model = PeftModel.from_pretrained(
model, model_id, adapter_name="r6_bp", revision="r6_bp")
Reproducibility
To generate plots from the paper, run analysis/plots.ipynb in the GitHub repository.