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Llama-3.1-Tulu-3-8B-RM

Tülu3 is a leading instruction following model family, offering fully open-source data, code, and recipes designed to serve as a comprehensive guide for modern post-training techniques. Tülu3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval.

Model description

• Model type: A model trained on a mix of publicly available, synthetic and human-created datasets.
• Language(s) (NLP): Primarily English
• License: Llama 3.1 Community License Agreement
• Finetuned from model: allenai/Llama-3.1-Tulu-3-8B-SFT

Model Sources

• Training Repository: https://github.com/allenai/open-instruct
• Eval Repository: https://github.com/allenai/olmes
• Paper: https://arxiv.org/abs/2411.15124
• Demo: https://playground.allenai.org/

Model Family

Using the model

Loading with HuggingFace

To load the model with HuggingFace, use the following snippet:

from transformers import AutoModelForSequenceClassification

tulu_model = AutoModelForSequenceClassification.from_pretrained("allenai/Llama-3.1-Tulu-3-8B-RM")

Chat template

The chat template for our models is formatted as:

<|user|>\nHow are you doing?\n<|assistant|>\nI'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?<|endoftext|>

Or with new lines expanded:

<|user|>
How are you doing?
<|assistant|>
I'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?<|endoftext|>

It is embedded within the tokenizer as well, for tokenizer.apply_chat_template.

Bias, Risks, and Limitations

The Tülu3 models have limited safety training, but are not deployed automatically 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 3.1 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.

Performance

Hyperparamters

Reward Modeling:

• Learning Rate: 3E-6
• Effective Batch Size: 256
• Max. Sequence Length: 2048
• Learning Rate Schedule: Linear
• Num. Epochs: 1
• Grad. Norm Threshold: 1.0

License and use

All Llama 3.1 Tülu3 models are released under Meta's Llama 3.1 Community License Agreement. Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. Tülu3 is intended for research and educational use. For more information, please see our Responsible Use Guidelines.

The models have been fine-tuned using a dataset mix with outputs generated from third party models and are subject to additional terms: Gemma Terms of Use and Qwen License Agreement (models were improved using Qwen 2.5).

Citation

If Tülu3 or any of the related materials were helpful to your work, please cite:

@article{lambert2024tulu3,
 title = {Tülu 3: Pushing Frontiers in Open Language Model Post-Training},
 author = {
 Nathan Lambert and 
 Jacob Morrison and 
 Valentina Pyatkin and 
 Shengyi Huang and 
 Hamish Ivison and 
 Faeze Brahman and 
 Lester James V. Miranda and 
 Alisa Liu and 
 Nouha Dziri and 
 Shane Lyu and 
 Yuling Gu and 
 Saumya Malik and 
 Victoria Graf and 
 Jena D. Hwang and 
 Jiangjiang Yang and
 Ronan Le Bras and
 Oyvind Tafjord and
 Chris Wilhelm and
 Luca Soldaini and 
 Noah A. Smith and 
 Yizhong Wang and 
 Pradeep Dasigi and 
 Hannaneh Hajishirzi
 },
 year = {2024},
 email = {tulu@allenai.org}
}

Base model

meta-llama/Llama-3.1-8B

allenai/Llama-3.1-Tulu-3-8B-SFT

(22)

All models released with Tulu 3 -- state of the art open post-training recipes. • 11 items • Updated Dec 23, 2025 • 103