model-index:
- name: llama-3.1-tulu-2-dpo-8b
results: []
datasets:
- allenai/tulu-v2-sft-mixture
- argilla/ultrafeedback-binarized-preferences-cleaned
language:
- en
base_model: allenai/llama-3.1-tulu-2-8b
license: apache-2.0
Model Card for Llama 3.1 Tulu V2+DPO 8B
Tulu is a series of language models that are trained to act as helpful assistants. Llama 3.1 Tulu V2 8B is a fine-tuned version of Llama 3 that was trained on a mix of publicly available, synthetic and human datasets. It was then further trained using DPO on the UltraFeedback dataset.
For more details on the training mixture, read the paper: Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2 .
Note this model is finetuned from Llama 3.1, released under the Meta Llama 3.1 community license, included here under llama_3_license.txt.
Model description
- Model type: A model trained on a mix of publicly available, synthetic and human-created datasets.
- Language(s) (NLP): Primarily English
- License: Apache 2.0
- Finetuned from model: meta-llama/Meta-Llama-3.1-8B
Model Sources
- Repository: https://github.com/allenai/open-instruct
- Model Family: Other models and the dataset are found in the Tulu V2 collection.
Performance
| Model | MMLU 5-shot | GSM8k 8-shot cot | BBH 3-shot cot | Codex HumanEval Pass@10 | AlpacaEval 1 | AlpacaEval 2 LC | TruthfulQA %Info+True | IFEval loose acc | XSTest safe but ref. | XSTest unsafe but follow | Average |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Llama 3.1 8b base | 65.5 | 57.0 | 65.6 | 61.6 | - | - | 32.7 | 11.1 | 17.2 | 44.0 | - |
| Llama 3.1 8b instruct | 65.6 | 84.5 | 68.5 | 84.5 | 94.8 | 26.0 | 31.1 | 75.6 | 8.8 | 5.5 | 71.8 |
| Tulu 2 Llama 3.1 8b | 61.4 | 68.0 | 59.2 | 67.9 | 80.6 | 9.0 | 56.2 | 46.4 | 11.2 | 13.0 | 63.9 |
| Tulu 2 Llama 3.1 8b DPO (this model) | 62.0 | 66.5 | 60.6 | 69.1 | 93.5 | 14.7 | 70.3 | 52.3 | 8.4 | 15.5 | 67.0 |
| Llama 3.1 70b base | 78.8 | 85.5 | 82.9 | 94.5 | - | - | - | 10.9 | 12.4 | 41.0 | - |
| Llama 3.1 70b instruct | 81.4 | 96.0 | 83.1 | 94.5 | 96.0 | 35.8 | 69.0 | 87.1 | 5.6 | 11.5 | 86.1 |
| Tulu 2 Llama 3.1 70b | 76.0 | 83.5 | 78.5 | 84.1 | 85.9 | 13.2 | 59.7 | 59.1 | 13.6 | 15.5 | 75.2 |
| Tulu 2 Llama 3.1 70b DPO | 76.0 | 88.5 | 79.9 | 89.0 | 96.8 | 24.8 | 78.3 | 63.6 | 9.2 | 14.0 | 80.5 |
You can find all models Ai2 trained as part of this family here, alongside our prior Llama 3.0 versions.
Input Format
The model is trained to use the following format (note the newlines):
<|user|>
Your message here!
<|assistant|>
For best results, format all inputs in this manner. Make sure to include a newline after <|assistant|>, this can affect generation quality quite a bit.
Intended uses & limitations
The model was initially fine-tuned on the Tulu V2 mix dataset, which contains a diverse range of human created instructions and synthetic dialogues generated primarily by other LLMs. We then further aligned the model with a Jax DPO trainer built on EasyLM on the openbmb/UltraFeedback dataset, which contains 64k prompts and model completions that are ranked by GPT-4.
Bias, Risks, and Limitations
The Tulu models have not been aligned to generate safe completions within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base Llama 3 models, however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this.
DPO Training hyperparameters
The following hyperparameters were used during DPO training:
- learning_rate: 5e-7
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
Citation
If you find Tulu 2 is useful in your work, please cite it with:
@misc{ivison2023camels,
title={Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2},
author={Hamish Ivison and Yizhong Wang and Valentina Pyatkin and Nathan Lambert and Matthew Peters and Pradeep Dasigi and Joel Jang and David Wadden and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi},
year={2023},
eprint={2311.10702},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Model card adapted from Zephyr Beta