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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
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[More Information Needed]
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---
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license: apache-2.0
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datasets:
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- openbmb/UltraInteract_pair
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language:
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- en
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base_model: meta-llama/Meta-Llama-3-8B-Instruct
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This is a model released for our paper: [Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHF](https://arxiv.org/abs/2410.04612).
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# REFUEL-Llama-3-Armo-iter_2
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This model is developed with REFUEL based on [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) with [ArmoRM-Llama3-8B-v0.1](https://huggingface.co/RLHFlow/ArmoRM-Llama3-8B-v0.1) as the reward model and [UltraInteract](https://huggingface.co/datasets/openbmb/UltraInteract_pair) dataset.
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The training code is available at https://github.com/ZhaolinGao/REFUEL.
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## Evaluations
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<table>
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<tr>
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<th rowspan="2">Method</th>
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<th rowspan="2">Dataset</th>
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<th colspan="6">Winrate at Turn</th>
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</tr>
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<tr>
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<th>h = 1</th>
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<th>h = 2</th>
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<th>h = 3</th>
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<th>h = 4</th>
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<th>H = 5</th>
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<th>avg</th>
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</tr>
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<tr>
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<td>Llama-3.1-70B-it</td>
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<td> N/A </td>
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<td>70.4</td>
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<td>66.4</td>
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<td>61.0</td>
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<td>53.0</td>
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<td>55.4</td>
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<td>61.24</td>
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</tr>
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<tr>
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<td><a href="https://huggingface.co/Cornell-AGI/REFUEL-Llama-3-Armo-iter_1">REFUEL-Llama-3-Armo-iter_1</a></td>
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<td><a href="https://huggingface.co/datasets/Cornell-AGI/REFUEL-Ultrainteract-Llama-3-Armo-iter_1">REFUEL-Ultrainteract-Llama-3-Armo-iter_1</a></td>
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<td>54.6</td>
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<td>53.6</td>
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<td>57.8</td>
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<td>56.2</td>
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<td>59.4</td>
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<td>56.32</td>
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</tr>
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<tr>
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<td><a href="https://huggingface.co/Cornell-AGI/REFUEL-Llama-3-Armo-iter_2">REFUEL-Llama-3-Armo-iter_2</a></td>
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<td><a href="https://huggingface.co/datasets/Cornell-AGI/REFUEL-Ultrainteract-Llama-3-Armo-iter_2">REFUEL-Ultrainteract-Llama-3-Armo-iter_2</a></td>
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<td>55.2</td>
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<td>53.4</td>
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<td>58.8</td>
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<td>57.2</td>
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<td>58.6</td>
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<td>56.64</td>
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</tr>
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</table>
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## Citation
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Please cite our paper if you use this model in your own work:
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```
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@misc{gao2024regressingrelativefutureefficient,
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title={Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHF},
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author={Zhaolin Gao and Wenhao Zhan and Jonathan D. Chang and Gokul Swamy and Kianté Brantley and Jason D. Lee and Wen Sun},
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year={2024},
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eprint={2410.04612},
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archivePrefix={arXiv},
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primaryClass={cs.LG},
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url={https://arxiv.org/abs/2410.04612},
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}
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```
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