---
library_name: transformers
tags: []
---

# THE MODEL SHOULD NOT BE USED FOR NOW, IT IS STILL IN TESTING

### How to use

Until its next release, the transformers library needs to be installed from source with the following command in order to use the models. 
PyTorch should also be installed.

```
pip install --upgrade git+https://github.com/huggingface/transformers.git
pip install torch
```

A small snippet of code is given here in order to **generate sequences from a pipeline (high-level)**.

```
# Load pipeline
from transformers import pipeline
pipe = pipeline(model="InstaDeepAI/ChatNT-text-generation-pipeline", trust_remote_code=True)

# Define custom inputs (note that the number of <DNA> token in the english sequence must be equal to len(dna_sequences))
english_sequence = "A chat between a curious user and an artificial intelligence assistant that can handle bio sequences. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Is there any evidence of an acceptor splice site in this sequence <DNA> ?"
dna_sequences = ["ATCGGAAAAAGATCCAGAAAGTTATACCAGGCCAATGGGAATCACCTATTACGTGGATAATAGCGATAGTATGTTACCTATAAATTTAACTACGTGGATATCAGGCAGTTACGTTACCAGTCAAGGAGCACCCAAAACTGTCCAGCAACAAGTTAATTTACCCATGAAGATGTACTGCAAGCCTTGCCAACCAGTTAAAGTAGCTACTCATAAGGTAATAAACAGTAATATCGACTTTTTATCCATTTTGATAATTGATTTATAACAGTCTATAACTGATCGCTCTACATAATCTCTATCAGATTACTATTGACACAAACAGAAACCCCGTTAATTTGTATGATATATTTCCCGGTAAGCTTCGATTTTTAATCCTATCGTGACAATTTGGAATGTAACTTATTTCGTATAGGATAAACTAATTTACACGTTTGAATTCCTAGAATATGGAGAATCTAAAGGTCCTGGCAATGCCATCGGCTTTCAATATTATAATGGACCAAAAGTTACTCTATTAGCTTCCAAAACTTCGCGTGAGTACATTAGAACAGAAGAATAACCTTCAATATCGAGAGAGTTACTATCACTAACTATCCTATG"]

# Generate sequence
generated_english_sequence = pipe(
    inputs={"english_sequence": english_sequence, "dna_sequences": dna_sequences},
    max_num_tokens_to_decode=50, # Max number of tokens to be generated
    english_tokens_max_length=512, # Used to pad or truncate the english tokens
    bio_tokens_max_length=512, # Used to pad or truncate the DNA tokens
)
```

A small snippet of code is given here in order to **infer with the model without any abstraction (low-level)**.

```
import numpy as np
from transformers import AutoModel, AutoTokenizer

# Load model and tokenizers
model = AutoModel.from_pretrained("InstaDeepAI/ChatNT", trust_remote_code=True)
english_tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/ChatNT", subfolder="english_tokenizer")
bio_tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/ChatNT", subfolder="bio_tokenizer")

# Define custom inputs (note that the number of <DNA> token in the english sequence must be equal to len(dna_sequences))
english_sequence = "A chat between a curious user and an artificial intelligence assistant that can handle bio sequences. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Is there any evidence of an acceptor splice site in this sequence <DNA> ?"
dna_sequences = ["ATCGGAAAAAGATCCAGAAAGTTATACCAGGCCAATGGGAATCACCTATTACGTGGATAATAGCGATAGTATGTTACCTATAAATTTAACTACGTGGATATCAGGCAGTTACGTTACCAGTCAAGGAGCACCCAAAACTGTCCAGCAACAAGTTAATTTACCCATGAAGATGTACTGCAAGCCTTGCCAACCAGTTAAAGTAGCTACTCATAAGGTAATAAACAGTAATATCGACTTTTTATCCATTTTGATAATTGATTTATAACAGTCTATAACTGATCGCTCTACATAATCTCTATCAGATTACTATTGACACAAACAGAAACCCCGTTAATTTGTATGATATATTTCCCGGTAAGCTTCGATTTTTAATCCTATCGTGACAATTTGGAATGTAACTTATTTCGTATAGGATAAACTAATTTACACGTTTGAATTCCTAGAATATGGAGAATCTAAAGGTCCTGGCAATGCCATCGGCTTTCAATATTATAATGGACCAAAAGTTACTCTATTAGCTTCCAAAACTTCGCGTGAGTACATTAGAACAGAAGAATAACCTTCAATATCGAGAGAGTTACTATCACTAACTATCCTATG"]

# Tokenize
english_tokens = english_tokenizer(english_sequence, return_tensors="pt", padding="max_length", truncation=True, max_length=512).input_ids
bio_tokens = bio_tokenizer(dna_sequences, return_tensors="pt", padding="max_length", max_length=512, truncation=True).input_ids.unsqueeze(0) # unsqueeze to simulate batch_size = 1

# Predict
outs = model(
    multi_omics_tokens_ids=(english_tokens, bio_tokens),
    projection_english_tokens_ids=english_tokens,
    projected_bio_embeddings=None,
)
```