For more details on the model, please go to Allen AI's original model card
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* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
Tulu 3 banner
Llama-3.1-Tulu-3-8B
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-DPO
To load the model with HuggingFace, use the following snippet:
from transformers import AutoModelForCausalLM
tulu_model = AutoModelForCausalLM.from_pretrained("allenai/Llama-3.1-Tulu-3-8B")
VLLM
As a Llama base model, the model can be easily served with:
vllm serve allenai/Llama-3.1-Tulu-3-8B
Note that given the long chat template of Llama, you may want to use --max_model_len=8192.
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.
System prompt
In Ai2 demos, we use this system prompt by default:
You are Tulu 3, a helpful and harmless AI Assistant built by the Allen Institute for AI.
The model has not been trained with a specific system prompt in mind.
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
PPO settings for RLVR:
Learning Rate: 3 × 10⁻⁷
Discount Factor (gamma): 1.0
General Advantage Estimation (lambda): 0.95
Mini-batches (N_mb): 1
PPO Update Iterations (K): 4
PPO's Clipping Coefficient (epsilon): 0.2
Value Function Coefficient (c1): 0.1
Gradient Norm Threshold: 1.0
Learning Rate Schedule: Linear
Generation Temperature: 1.0
Batch Size (effective): 512
Max Token Length: 2,048
Max Prompt Token Length: 2,048
Penalty Reward Value for Responses without an EOS Token: -10.0
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}
}
