Camera-ready README: simplified to what-it-is / quick start / citation

README.md

@@ -1,56 +1,41 @@
-# PlasmidGPT Model
+---
+license: mit
+library_name: transformers
+pipeline_tag: text-generation
+base_model: UCL-CSSB/PlasmidGPT
+tags:
+- biology
+- plasmid
+- dna
+- synthetic-biology
+- gpt2
+---
 
-This is a GPT-2 based model for engineered plasmid sequence generation, converted from PyTorch `.pt` format to HuggingFace transformers format.
+# PlasmidGPT-SFT
 
-This is a supervised fine-tuned (SFT) version of [PlasmidGPT](https://github.com/lingxusb/PlasmidGPT) for engineered plasmids. This work was done by **Angus Cunningham** while at **Prof. Chris Barnes' lab at UCL**.
+Supervised fine-tune of [PlasmidGPT](https://huggingface.co/UCL-CSSB/PlasmidGPT) on a curated corpus of ~15k engineered *E. coli* plasmids from PlasmidScope and Addgene (Cunningham et al., 2025). Used as a baseline for the GRPO-trained [PlasmidGPT-GRPO](https://huggingface.co/UCL-CSSB/PlasmidGPT-GRPO).
 
-## Model Details
-
-- **Architecture**: GPT-2
-- **Vocab Size**: 30,002
-- **Hidden Size**: 768
-- **Number of Layers**: 12
-- **Number of Heads**: 12
-- **Max Position Embeddings**: 2048
-- **Parameters**: ~124M
-
-## Usage
+## Quick start
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model = AutoModelForCausalLM.from_pretrained("./plasmidgpt-model")
-tokenizer = AutoTokenizer.from_pretrained("./plasmidgpt-model")
-
-# Basic generation
-inputs = tokenizer("ATGC", return_tensors="pt")
-outputs = model.generate(**inputs, max_length=100)
-generated_sequence = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print(generated_sequence)
+model = AutoModelForCausalLM.from_pretrained("UCL-CSSB/PlasmidGPT-SFT")
+tokenizer = AutoTokenizer.from_pretrained("UCL-CSSB/PlasmidGPT-SFT")
 
-# With sampling (for more diverse outputs)
-outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.8, top_p=0.9)
-generated_sequence = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print(generated_sequence)
+input_ids = tokenizer("ATG", return_tensors="pt").input_ids
+outputs = model.generate(input_ids, max_new_tokens=512, do_sample=True, temperature=1.0)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 
-### Example Outputs
-
-**Input:** `ATGCGATCG`  
-**Generated:** `ATGCGATCGGTGGTAGGCACTGGATGATGGCCCTGCAGTGTAGCCGTAGTTATGAGCCTCGGGATTCTTTGATGATTCAGCCACCCTCATCATCCTCCTCCTCC...`
-
-**Input:** `ATGGCC`  
-**Generated:** `ATGGCCTACATACCTTCAATTACCGAAACAAGGTGGTTCATCTCTAACGCTGTCCATAAAACCGCCCAGTCTAGCTATCGCCATTTGCGCATCTAACGTGGTAGGCACTCCGGGTCCGCGCC...`
+## Citation
 
-## Compatible With
-
-This model is compatible with the architecture from [McClain/plasmidgpt-addgene-gpt2](https://huggingface.co/McClain/plasmidgpt-addgene-gpt2), but with different weights from the pretrained model.
-
-## Files
-
-- `config.json`: Model configuration
-- `generation_config.json`: Generation parameters
-- `model.safetensors`: Model weights in SafeTensors format
-- `tokenizer.json`: Fast tokenizer data
-- `tokenizer_config.json`: Tokenizer configuration
-- `special_tokens_map.json`: Special token mappings
+```bibtex
+@article{cunningham2025plasmidsft,
+  title   = {Generative design and construction of functional plasmids with a {DNA} language model},
+  author  = {Cunningham, Angus G. and Dekker, Linda and Shcherbakova, Anastasiia and Barnes, Chris P.},
+  journal = {bioRxiv},
+  year    = {2025},
+  doi     = {10.64898/2025.12.06.692736}
+}
+```