# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="alchemab/fabcon-medium")# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("alchemab/fabcon-medium")
model = AutoModelForCausalLM.from_pretrained("alchemab/fabcon-medium")You need to share contact information with Alchemab to access this model
The information you provide will be collected, stored, processed, and shared in accordance with the Alchemab Privacy Notice.
FAbCon Terms of Use
FAbCon models follow a modified Apache 2.0 license
Log in or Sign Up to review the conditions and access this model content.
FAbCon-medium 🦅🧬
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-medium")
>>> model = FalconForCausalLM.from_pretrained("alchemab/fabcon-medium")
>>> 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-medium")
>>> model = FalconForSequenceClassification.from_pretrained("alchemab/fabcon-medium")
>>> o = model(input_ids=tokenizer("Ḣ", return_tensors='pt')['input_ids'],
attention_mask=tokenizer("Ḣ", return_tensors='pt')['attention_mask'])
- Downloads last month
- 189
# Gated model: Login with a HF token with gated access permission hf auth login