Model Card for microsoft/Dayhoff-170m-UR50

Dayhoff is an Atlas of both protein sequence data and generative language models — a centralized resource that brings together 3.34 billion protein sequences across 1.7 billion clusters of metagenomic and natural protein sequences (GigaRef), 46 million structure-derived synthetic sequences (BackboneRef), and 16 million multiple sequence alignments (OpenProteinSet). These models can natively predict zero-shot mutation effects on fitness, scaffold structural motifs by conditioning on evolutionary or structural context, and perform guided generation of novel proteins within specified families. Learning from metagenomic and structure-based synthetic data from the Dayhoff Atlas increased the cellular expression rates of generated proteins, highlighting the real-world value of expanding the scale, diversity, and novelty of protein sequence data. The Dayhoff architecture is a hybrid of state-space Mamba layers and Transformer self-attention, interleaved with Mixture-of-Experts modules to maximize capacity while preserving efficiency. It natively handles long contexts, allowing both single sequences and unrolled MSAs to be modeled. Trained with an autoregressive objective in both N→C and C→N directions, Dayhoff supports order-agnostic infilling and scales to billions of parameters.

Model Details

Model Description

Developed by: Kevin K. Yang, Sarah Alamdari, Alex J. Lee, Kaeli Kaymak-Loveless, Samir Char, Garyk Brixi, Carles Domingo-Enrich, Chentong Wang, Suyue Lyu, Nicolo Fusi, Neil Tenenholtz, Ava P. Amini
Model type: Hybrid state-space-model transformer architecture with mixture-of-experts
License: MIT

Model Sources

Repository: https://github.com/microsoft/dayhoff
Model on the Hugging Face Hub: https://huggingface.co/microsoft/Dayhoff-170m-UR50

Uses

Downstream Use

Dayhoff is intended for broad research use on protein language modeling. The model has been used and assessed on the following capabilities:

Unconditional design of protein sequences
Zero-shot mutation effect prediction on ProteinGym
Designing scaffolds for structural motifs in sequence space on RFDiffusion and MotifBench
Homolog conditioning with Dayhoff-3b-GR-HM and Dayhoff-3b-GR-HM-c

Bias, Risks, and Limitations

This model should not be used to generate anything that is not a protein sequence or a set of homologuous protein sequences. It is not meant for natural language or other biological sequences, such as DNA sequences. Not all sequences are guaranteed to be realistic. It remains difficult to generate high-quality sequences with no sequence homology to any natural sequence.

How to Get Started with the Model

You can use cURL or any REST Client to send a request to the AzureML endpoint with your AzureML token.

curl <AZUREML_ENDPOINT_URL> \
    -X POST \
    -d '{"inputs":"@MADQLTEEQIAEFKEAF","parameters":{"max_new_tokens":64,"do_sample":false}}' \
    -H "Authorization: Bearer <AZUREML_TOKEN>" \
    -H "Content-Type: application/json"

Example

To unconditinally generate a new protein sequence:

{
  "inputs": "@",
  "parameters": {
    "max_new_tokens": 64,
    "do_sample": false
  }
}

To complete a protein sequence:

{
  "inputs": "@MADQLTEEQIAEFKEAF",
  "parameters": {
    "max_new_tokens": 64,
    "do_sample": false
  }
}

For a complete list of generation parameters see here . For detailed instructions on package usage, please refer to the README in model repo.

Evaluation

Results

See the preprint for the latest benchmark results and evaluations. Model perplexity on held-out test sequences for Dayhoff models.

Model	UniRef50	GigaRef	Aligned homologs	Unaligned homologs
170m-UR50	11.62	11.88
170m-UR90	11.52	11.85
170m-GR	13.67	9.36
170m-UR50-BRn	11.78	12.03
170m-UR50-BRq	11.67	11.91
170m-UR50-BRu	11.66	11.87
3b-UR90	8.95	9.64
3b-GR-HM	11.95	6.68	4.34	4.60
3b-GR-HM-c	10.11	9.21	3.57	3.56

Quality of generated sequences as measured by ESMFold pLDDT and scPerplexity. Dataset statistics are for 1024 randomly-sampled sequences. Model statistics are for 1024 generations at T=1 in the N-to-C direction.

Model or dataset	pLDDT (mean ± s.d.)	scPerplexity (mean ± s.d.)
Natural sequences
UniRef50	0.653 ± 0.196	9.45 ± 2.89
GigaRef-clusters	0.619 ± 0.199	9.69 ± 2.83
GigaRef-singletons	0.561 ± 0.201	10.07 ± 2.88
Generated sequences
170m-UR50	0.421 ± 0.132	11.97 ± 2.14
170m-UR90	0.407 ± 0.125	12.12 ± 2.14
170m-GR	0.422 ± 0.129	11.83 ± 2.12
170m-UR50-BRu	0.441 ± 0.157	11.71 ± 2.18
170m-UR50-BRq	0.434 ± 0.152	11.72 ± 2.24
170m-UR50-BRn	0.432 ± 0.131	11.77 ± 2.24
3b-UR90	0.454 ± 0.150	11.79 ± 2.38
3b-GR-HM	0.406 ± 0.126	11.50 ± 2.16
3b-GR-HM-c	0.423 ± 0.132	11.91 ± 2.18

ProteinGym zero-shot performance Spearman’s correlation coefficient on ProteinGym substitutions and indels.

Input	Model	Parameters	Substitutions	Indels
Single sequence	170m-UR50	170M	0.353	0.479
	170m-UR90	170M	0.354	0.483
	170m-GR	170M	0.199	0.292
	170m-UR50-BRu	170M	0.341	0.476
	170m-UR50-BRq	170M	0.356	0.477
	170m-UR50-BRn	170M	0.341	0.478
	3b-UR90	3B	0.394	0.497
	3b-GR-HM	3B	0.328	0.423
	3b-GR-HM-c	3B	0.417	0.466
Aligned homologs	3b-GR-HM-c	3B	0.368	NA
Unaligned homologs	3b-GR-HM-c	3B	0.372	0.401

RFDiffusion Benchmark Performance Motif scaffolding performance, problems solved, successes out of 100, and MotifBench score.

Problem	170m-UR50	170m-UR90	170m-GR	170m-UR50-BRn	170m-UR50-BRq	170m-UR50-BRu	3b-UR90	3b-GR-HM	3b-GR-HM-c	EvoDiff-Seq
1PRW	62	72	81	95	91	90	94	81	79	82
1BCF	0	0	5	0	0	0	10	8	0	7
5TPN	0	0	0	0	0	0	0	0	0	0
5IUS	0	0	0	0	0	0	0	0	0	0
3IXT	12	17	12	14	18	12	18	11	14	20
5YUI	0	0	0	0	0	0	0	0	0	0
1QJG	0	0	0	0	0	0	0	0	0	0
1YCR	2	5	0	6	7	6	2	3	4	2
2KL8	0	1	0	1	0	1	1	1	1	1
7MRX_60	1	0	0	0	0	2	42	0	9	0
7MRX_85	0	0	0	0	0	0	19	1	1	0
7MRX_128	0	0	0	0	0	0	0	0	0	0
4JHW	0	0	0	0	0	0	0	0	0	0
4ZYP	0	0	0	0	0	1	0	0	0	0
5WN9	0	0	0	0	0	0	0	0	0	0
6VW1	1	1	1	0	0	1	0	0	0	0
5TRV_short	0	0	0	0	0	0	0	0	0	0
5TRV_med	0	0	0	0	0	0	0	0	0	0
5TRV_long	0	0	0	0	0	0	0	0	0	0
6E6R_short	2	2	1	3	3	2	14	7	8	6
6E6R_med	0	1	2	0	0	2	4	0	2	0
6E6R_long	0	1	0	0	0	1	3	0	1	0
6EXZ_short	0	0	0	0	0	0	0	0	0	0
6EXZ_med	0	0	0	0	0	0	0	0	0	0
6EXZ_long	0	0	0	0	0	0	0	0	0	0
Problems solved	6	8	6	5	4	10	10	7	9	6
Successes	80	100	102	119	119	118	207	112	119	118
Score	9.65	12.25	6.10	7.26	10.62	14.36	16.32	11.90	14.14	7.67

MotifBench Benchmark Performance Motif scaffolding performance, problems solved, successes out of 100, and MotifBench score.

Problem	170m-UR50	170m-UR90	170m-GR	170m-UR50-BRn	170m-UR50-BRq	170m-UR50-BRu	3b-UR90	3b-GR-HM	3b-GR-HM-c	EvoDiff-Seq
01_1LDB	1	1	3	0	0	1	20	2	12	0
02_1ITU	4	33	4	1	1	4	37	57	48	0
03_2CGA	0	0	0	0	0	0	0	0	0	0
04_5WN9	0	0	0	0	0	0	0	0	0	0
05_5ZE9	0	1	21	0	0	0	16	40	9	0
06_6E6R	1	1	1	1	2	1	6	3	1	2
07_6E6R	0	0	0	2	0	0	2	0	0	0
08_7AD5	0	0	0	0	0	0	0	0	0	0
09_7CG5	0	0	0	0	0	0	0	0	0	0
10_7WRK	0	0	0	0	0	0	0	0	0	0
11_3TQB	4	11	3	4	3	7	40	8	26	0
12_4JHW	0	0	0	0	0	0	0	0	0	0
13_4JHW	0	0	0	0	0	0	0	0	0	0
14_5IUS	0	0	0	0	0	0	0	0	0	0
15_7A8S	0	0	0	0	0	0	0	0	0	0
16_7BNY	0	0	0	0	0	0	0	0	0	0
17_7DGW	0	0	0	0	0	0	0	0	0	0
18_7MQQ	0	0	0	0	0	0	0	0	0	0
19_7MQQ	0	0	0	0	0	0	0	0	0	0
20_7UWL	0	0	0	0	0	0	0	0	0	0
21_1B73	0	0	0	0	0	0	0	0	0	0
22_1BCF	0	0	3	0	0	0	20	9	0	19
23_1MPY	0	0	0	0	0	0	0	0	0	0
24_1QY3	0	0	0	0	0	0	0	0	0	0
35_2RKX	0	0	0	0	0	0	0	0	0	0
36_3B5V	0	0	0	0	0	0	0	0	0	0
37_4XOJ	0	0	0	0	0	0	0	0	0	0
28_5YUI	0	0	0	0	0	0	0	0	0	0
29_6CPA	0	0	0	0	0	0	0	0	0	0
30_7UWL	0	0	0	0	0	0	0	0	0	0
Problems	4	5	6	4	3	4	7	6	5	2
Successes	10	47	35	8	6	13	141	119	96	21
Score	2.33	2.92	4.33	2.75	2.17	2.75	8.36	4.96	4.48	1.58

Technical Specifications

Compute Infrastructure

170M-parameter models: trained on 8 NVIDIA A100 or 8 NVIDIA H100 GPUs using Distributed Data Parallel.
3B-parameter models: trained on 176 NVIDIA H100 GPUs using Fully Sharded Data Parallel in hybrid-shard mode.

Responsible AI Considerations

The intended use of this model is to generate high-quality, realistic, protein sequences or sets of homologous protein sequences. Generations can be designed from scratch or conditioned on partial sequences in both N→C and C→N directions. The code and datasets released in this repository are provided for research and development use only. They are not intended for use in clinical decision-making or for any other clinical use, and the performance of these models for clinical use has not been established. You bear sole responsibility for any use of these models, data and software, including incorporation into any product intended for clinical use.

Citation

If you use the code, data, models, or results. please cite our preprint .

Model Specifications

LicenseMit

Last UpdatedAugust 2025

PublisherHuggingFace

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