license: llama3.1
language:
- en
pipeline_tag: text-classification
datasets:
- allenai/llama-3.1-tulu-3-8b-preference-mixture
base_model:
- allenai/Llama-3.1-Tulu-3-8B-SFT
library_name: transformers
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://allenai.org/papers/tulu-3-report.pdf (arXiv soon)
- Demo: https://playground.allenai.org/
Model Family
| Stage | Llama 3.1 8B | Llama 3.1 70B |
|---|---|---|
| Base Model | meta-llama/Llama-3.1-8B | meta-llama/Llama-3.1-70B |
| SFT | allenai/Llama-3.1-Tulu-3-8B-SFT | allenai/Llama-3.1-Tulu-3-70B-SFT |
| DPO | allenai/Llama-3.1-Tulu-3-8B-DPO | allenai/Llama-3.1-Tulu-3-70B-DPO |
| Final Models (RLVR) | allenai/Llama-3.1-Tulu-3-8B | allenai/Llama-3.1-Tulu-3-70B |
| Reward Model (RM) | allenai/Llama-3.1-Tulu-3-8B-RM | (Same as 8B) |
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
| Benchmark (eval) | Tülu 3 SFT 8B | Tülu 3 DPO 8B | Tülu 3 8B | Llama 3.1 8B Instruct | Qwen 2.5 7B Instruct | Magpie 8B | Gemma 2 9B Instruct | Ministral 8B Instruct |
|---|---|---|---|---|---|---|---|---|
| Avg. | 60.4 | 64.4 | 64.8 | 62.2 | 57.8 | 44.7 | 55.2 | 58.3 |
| MMLU (0 shot, CoT) | 65.9 | 68.7 | 68.2 | 71.2 | 76.6 | 62.0 | 74.6 | 68.5 |
| PopQA (15 shot) | 29.3 | 29.3 | 29.1 | 20.2 | 18.1 | 22.5 | 28.3 | 20.2 |
| TruthfulQA (6 shot) | 46.8 | 56.1 | 55.0 | 55.1 | 63.1 | 57.0 | 61.4 | 55.5 |
| BigBenchHard (3 shot, CoT) | 67.9 | 65.8 | 66.0 | 62.8 | 21.7 | 0.9 | 2.5 | 56.2 |
| DROP (3 shot) | 61.3 | 62.5 | 62.6 | 61.5 | 54.4 | 49.4 | 58.8 | 56.2 |
| MATH (4 shot CoT, Flex) | 31.5 | 42.0 | 43.7 | 42.5 | 14.8 | 5.1 | 29.8 | 40.0 |
| GSM8K (8 shot, CoT) | 76.2 | 84.3 | 87.6 | 83.4 | 83.8 | 61.2 | 79.7 | 80.0 |
| HumanEval (pass@10) | 86.2 | 83.9 | 83.9 | 86.3 | 93.1 | 75.4 | 71.7 | 91.0 |
| HumanEval+ (pass@10) | 81.4 | 78.6 | 79.2 | 82.9 | 89.7 | 69.1 | 67.0 | 88.5 |
| IFEval (prompt loose) | 72.8 | 81.1 | 82.4 | 80.6 | 74.7 | 38.8 | 69.9 | 56.4 |
| AlpacaEval 2 (LC % win) | 12.4 | 33.5 | 34.5 | 24.2 | 29.0 | 49.0 | 43.7 | 31.4 |
| Safety (6 task avg.) | 93.1 | 87.2 | 85.5 | 75.2 | 75.0 | 46.4 | 75.5 | 56.2 |
| Benchmark (eval) | Tülu 3 70B SFT | Tülu 3 DPO 70B | Tülu 3 70B | Llama 3.1 70B Instruct | Qwen 2.5 72B Instruct | Hermes 3 Llama 3.1 70B | Nemotron Llama 3.1 70B |
|---|---|---|---|---|---|---|---|
| Avg. | 72.6 | 75.9 | 76.0 | 73.4 | 71.5 | 68.3 | 65.5 |
| MMLU (0 shot, CoT) | 78.9 | 83.3 | 83.1 | 85.3 | 85.5 | 80.4 | 83.8 |
| PopQA (15 shot) | 48.6 | 46.3 | 46.5 | 46.4 | 30.6 | 48.1 | 36.4 |
| TruthfulQA (6 shot) | 55.7 | 67.9 | 67.6 | 66.8 | 69.9 | 66.5 | 62.6 |
| BigBenchHard (3 shot, CoT) | 82.7 | 81.8 | 82.0 | 73.8 | 67.2 | 82.1 | 0.7 |
| DROP (3 shot) | 77.2 | 74.1 | 74.3 | 77.0 | 34.2 | 73.2 | 68.8 |
| MATH (4 shot CoT, Flex) | 53.7 | 62.3 | 63.0 | 56.4 | 74.3 | 41.9 | 55.0 |
| GSM8K (8 shot, CoT) | 91.1 | 93.5 | 93.5 | 93.7 | 89.5 | 90.0 | 84.7 |
| HumanEval (pass@10) | 92.9 | 92.4 | 92.4 | 93.6 | 94.0 | 89.6 | 94.1 |
| HumanEval+ (pass@10) | 87.3 | 88.4 | 88.0 | 89.5 | 90.8 | 85.9 | 85.5 |
| IFEval (prompt loose) | 82.1 | 82.6 | 83.2 | 88.0 | 87.6 | 76.0 | 79.9 |
| AlpacaEval 2 (LC % win) | 26.3 | 49.6 | 49.8 | 33.4 | 47.7 | 28.4 | 66.1 |
| Safety (6 task avg.) | 94.4 | 89.0 | 88.3 | 76.5 | 87.0 | 57.9 | 69.0 |
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 = {[email protected]}
}