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FAbCon models follow a modified Apache 2.0 license

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FAbCon-small 🦅🧬

FAbCon is a generative, antibody-specific language model based on the Falcon model. It is pre-trained using causal language modelling, and is suitable for a range of tasks. FAbCon-small, FAbCon-medium, and FAbCon-large are available for non-commercial use via a modified Apache 2.0 license. For any users seeking commercial use of our models (and license for generated antibodies from all FAbCon models), please contact us.

Model variant Parameters Config License
FAbCon-small 144M 24L, 12H, 768d Modified Apache 2.0
FAbCon-medium 297M 28L, 16H, 1024d Modified Apache 2.0
FAbCon-large 2.4B 56L, 32H, 2048d Modified Apache 2.0

Usage example - generation

Generating sequences can be done using HuggingFace's built-in model.generate method,

from transformers import (
    PreTrainedTokenizerFast,
    FalconForCausalLM
)

>>> tokenizer = PreTrainedTokenizerFast.from_pretrained("alchemab/fabcon-small")
>>> model = FalconForCausalLM.from_pretrained("alchemab/fabcon-small")
>>> o = model.generate(
            tokenizer("Ḣ", return_tensors='pt')['input_ids'][:, :-1],
            max_new_tokens=...,
            top_k = ...,
            temperature = ...
    )
>>> decoded_seq = tokenizer.batch_decode(o)

Usage example - sequence property prediction

Use the transformers built-in SequenceClassification classes

from transformers import (
    PreTrainedTokenizerFast,
    FalconForSequenceClassification
)

>>> tokenizer = PreTrainedTokenizerFast.from_pretrained("alchemab/fabcon-small")
>>> model = FalconForSequenceClassification.from_pretrained("alchemab/fabcon-small")
>>> o = model(input_ids=tokenizer("Ḣ", return_tensors='pt')['input_ids'],
              attention_mask=tokenizer("Ḣ", return_tensors='pt')['attention_mask'])
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