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Model: Playingyoyo/GPepT Source: Original Platform
2026-06-22 16:23:05 +08:00
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--- a/README.md
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+---
+license: apache-2.0
+pipeline_tag: text-generation
+widget:
+- text: <|endoftext|>
+inference:
+  parameters:
+    top_k: 950
+    repetition_penalty: 1.2
+---
+
+# **GPepT: A Language Model for Peptides and Peptidomimetics**
+![alt text](TOC.png)
+
+GPepT is a cutting-edge language model designed to understand and generate sequences in the specialized domain of peptides and peptidomimetics. It serves as a powerful tool for _de novo_ protein design and engineering. As demonstrated in our research, the incorporation of peptidomimetics significantly broadens the chemical space accessible through generated sequences, enabling innovative approaches to peptide-based therapeutics.
+
+## **Model Overview**
+GPepT builds upon the GPT-2 Transformer architecture, comprising 36 layers and a model dimensionality of 1280, with a total of 738 million parameters. This decoder-only model has been pre-trained on a curated dataset of peptides and peptidomimetics mined from bioactivity-labeled chemical formulas in ChEMBL.
+
+To leverage GPepT’s pre-trained weights, input molecules must be converted into a standardized sequence-like representation of peptidomimetics using [**Monomerizer**](https://github.com/tsudalab/Monomerizer/tree/main). Detailed insights into the training process and datasets are provided in our accompanying publication.
+
+Unlike traditional protein design models, GPepT is trained in a self-supervised manner, using raw sequence data without explicit annotation. This design enables the model to generalize across diverse sequence spaces, producing functional antimicrobial peptidomimetics upon fine-tuning.
+
+SMILES representation, and selected chemical properties of each token, which corresponds to a non-canonical amino acid or terminal modification.
+
+---
+
+## **Using GPepT for Sequence Generation**
+GPepT is fully compatible with the HuggingFace Transformers Python library. Installation instructions can be found [here](https://huggingface.co/docs/transformers/installation).
+
+The model excels at generating peptidomimetic sequences in a zero-shot fashion, but it can also be fine-tuned on custom datasets to generate sequences tailored to specific requirements.
+
+
+### **Example 1: Zero-Shot Sequence Generation**
+GPepT generates sequences that extend from a specified input token (e.g., `<|endoftext|>`). If no input is provided, it selects the start token automatically and generates likely sequences. Here’s a Python example:
+
+```python
+from transformers import pipeline
+
+# Initialize GPepT for text generation
+GPepT = pipeline('text-generation', model="Playingyoyo/GPepT")
+
+# Generate sequences
+sequences = GPepT("<|endoftext|>", 
+                   max_length=25, 
+                   do_sample=True, 
+                   top_k=950, 
+                   repetition_penalty=1.5, 
+                   num_return_sequences=5, 
+                   eos_token_id=0)
+
+# Print generated sequences
+for seq in sequences:
+    print(seq['generated_text'])
+```
+
+Sample output:
+```
+<|endoftext|>R K A L E Z1649
+<|endoftext|>G K A L Z341
+<|endoftext|>G V A G K X4097 V A P
+```
+
+---
+
+### **Example 2: Fine-Tuning for Directed Sequence Generation**
+Fine-tuning enables GPepT to generate sequences with user-defined properties. To prepare training data:
+1. ```git clone https://github.com/tsudalab/Monomerizer/tree/main```
+2. ```cd Monomerizer```
+3. ```python3 Monomerizer/run_pipeline.py --input_file path_to_your_smiles_file.txt```. Check the repo for the required format.
+4. 3. will monomerize the SMILES and split the resulting sequences into training (`output/datetime/for_GPepT/train90.txt`) and validation (`output/datetime/for_GPepT/val10.txt`) files.
+
+To fine-tune the model:
+
+```bash
+python run_clm.py --model_name_or_path Playingyoyo/GPepT \
+                  --train_file path_to_train90.txt \
+                  --validation_file path_to_val10.txt \
+                  --tokenizer_name Playingyoyo/GPepT \
+                  --do_train \
+                  --do_eval \
+                  --output_dir ./output \
+                  --learning_rate 1e-5
+```
+
+Refer to the HuggingFace [script run_clm.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_clm.py) and [requirements.txt](https://huggingface.co/Playingyoyo/GPepT/blob/main/requirements.txt).
+Note that train90.txt and val10.txt need to be at least 50 samples long.
+
+The fine-tuned model will be saved in the `./output` directory, ready to generate tailored sequences.
+
+---
+
+## **Selecting Valid Sequences**
+While GPepT generates diverse peptidomimetic sequences, not all are chemically valid. For example:
+- **Invalid Sequences:** Those with terminal modifications (e.g., `Z`) embedded within the sequence.
+- **Valid Sequences:** Should adhere to standard peptidomimetic rules.
+
+By filtering out invalid sequences, GPepT users can ensure the generation of high-quality candidates for further study.
+
+--- 
+
+GPepT stands as a powerful tool for researchers at the forefront of peptide and peptidomimetic innovation, enabling both exploration and application in vast chemical and biological spaces.
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+  "torch_dtype": "float32",
+  "transformers_version": "4.48.0.dev0",
+  "use_cache": true,
+  "vocab_size": 50257
+}
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+}