metadata

model-index:
  - name: llama-3-tulu-2-dpo-70b
    results: []
datasets:
  - allenai/tulu-v2-sft-mixture
  - argilla/ultrafeedback-binarized-preferences-cleaned
language:
  - en
base_model: allenai/llama-3-tulu-2-70b

Model Card for Llama 3 Tulu V2+DPO 70B

Tulu is a series of language models that are trained to act as helpful assistants. Llama 3 Tulu V2 70B 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 .

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-3-70B

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	TydiQA 1-shot Gold Passage	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 70b base	0.790	0.840	0.801	73.35	0.745	-	-	0.469	0.163	0.256	0.330	65.60
Llama 3 70b instruct	0.786	0.930	0.801	59.21	0.908	96.71	39.99	0.701	0.828	0.060	0.140	79.22
Llama 3 Tulu 2 70b	0.752	0.845	0.779	69.798	0.861	86.007	17.51	0.646	0.591	0.108	0.130	73.01
Llama 3 Tulu 2+DPO 70b	0.754	0.860	0.785	23.443	0.878	96.65	27.34	0.780	0.643	0.080	0.140	71.60

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