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--- |
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base_model: |
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- HuggingFaceM4/idefics2-8b |
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language: |
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- en |
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license: apache-2.0 |
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pipeline_tag: image-text-to-text |
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library_name: transformers |
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--- |
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# Retrospective Learning from Interactions |
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This repository contains the `lil-lab/respect` model, based on the ACL paper [Retrospective Learning from Interactions](https://huggingface.co/papers/2410.13852). For more resources, please see <https://lil-lab.github.io/respect> and <https://github.com/lil-lab/respect>. |
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## Sample Usage |
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To get started with the model, follow these steps: |
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### 1. Setting up Environment |
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Prepare your conda environment: |
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```bash |
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conda create -n respect python=3.9.18 |
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pip install -r requirements.txt |
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pip install -e . |
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``` |
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### 2. Download Data |
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```python |
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from datasets import load_dataset |
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ds = load_dataset("lil-lab/respect", name="turn", split="train") |
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``` |
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### 3. Load Model Checkpoints |
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Download checkpoints and load the model using `transformers` and `peft`: |
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```python |
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import torch |
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from transformers import Idefics2ForConditionalGeneration |
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from peft import PeftModel |
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checkpoint = "HuggingFaceM4/idefics2-8b" |
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model_id = 'lil-lab/respect' |
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model = Idefics2ForConditionalGeneration.from_pretrained( |
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checkpoint, torch_dtype=torch.bfloat16) |
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peft_model = PeftModel.from_pretrained( |
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model, model_id, adapter_name="r6_bp", revision="r6_bp") |
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``` |
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## Reproducibility |
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To generate plots from the paper, run `analysis/plots.ipynb` in the [GitHub repository](https://github.com/lil-lab/respect). |
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