commit 0eb35a91618e322ee2d8fd227f834434409bff8d
Author: ModelHub XC <noreply@modelhub.org.cn>
Date:   Tue Jul 21 18:23:19 2026 +0800

    初始化项目，由ModelHub XC社区提供模型
    
    Model: AI4PD/ZymCTRL
    Source: Original Platform

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diff --git a/5.run_clm-post.py b/5.run_clm-post.py
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+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2020 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.
+
+Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
+https://huggingface.co/models?filter=causal-lm
+"""
+# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
+
+import logging
+import math
+import os
+import sys
+from dataclasses import dataclass, field
+from typing import Optional
+
+import datasets
+from datasets import load_dataset
+from datasets import load_from_disk
+
+import transformers
+from transformers import (
+    CONFIG_MAPPING,
+    MODEL_FOR_CAUSAL_LM_MAPPING,
+    AutoConfig,
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    HfArgumentParser,
+    Trainer,
+    TrainingArguments,
+    default_data_collator,
+    set_seed,
+)
+from transformers.testing_utils import CaptureLogger
+from transformers.trainer_utils import get_last_checkpoint
+from transformers.utils import check_min_version
+from transformers.utils.versions import require_version
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.13.0.dev0")
+
+require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
+
+logger = logging.getLogger(__name__)
+
+
+MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys())
+MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
+    """
+
+    model_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The model checkpoint for weights initialization."
+            "Don't set if you want to train a model from scratch."
+        },
+    )
+    model_type: Optional[str] = field(
+        default=None,
+        metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
+    )
+    config_overrides: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "Override some existing default config settings when a model is trained from scratch. Example: "
+            "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
+        },
+    )
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
+            "with private models)."
+        },
+    )
+
+    def __post_init__(self):
+        if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
+            raise ValueError(
+                "--config_overrides can't be used in combination with --config_name or --model_name_or_path"
+            )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    dataset_name: Optional[str] = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
+    validation_file: Optional[str] = field(
+        default=None,
+        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
+            "value if set."
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+            "value if set."
+        },
+    )
+
+    block_size: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "Optional input sequence length after tokenization. "
+            "The training dataset will be truncated in block of this size for training. "
+            "Default to the model max input length for single sentence inputs (take into account special tokens)."
+        },
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
+    )
+    validation_split_percentage: Optional[int] = field(
+        default=5,
+        metadata={
+            "help": "The percentage of the train set used as validation set in case there's no validation split"
+        },
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    keep_linebreaks: bool = field(
+        default=True, metadata={"help": "Whether to keep line breaks when using TXT files or not."}
+    )
+
+    #def __post_init__(self):
+    #    if self.dataset_name is None and self.train_file is None and self.validation_file is None:
+    #        raise ValueError("Need either a dataset name or a training/validation file.")
+    #    else:
+    #        if self.train_file is not None:
+    #            extension = self.train_file.split(".")[-1]
+    #            assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
+    #        if self.validation_file is not None:
+    #            extension = self.validation_file.split(".")[-1]
+    #            assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
+
+
+def main():
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Detecting last checkpoint.
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
+    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
+    # (the dataset will be downloaded automatically from the datasets Hub).
+    #
+    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
+    # 'text' is found. You can easily tweak this behavior (see below).
+    #
+    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
+    # download the dataset.
+    
+    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
+    # https://huggingface.co/docs/datasets/loading_datasets.html.
+
+    # Load pretrained model and tokenizer
+    #
+    # Distributed training:
+    # The .from_pretrained methods guarantee that only one local process can concurrently
+    # download model & vocab.
+
+    config_kwargs = {
+        "cache_dir": model_args.cache_dir,
+        "revision": model_args.model_revision,
+        "use_auth_token": True if model_args.use_auth_token else None,
+    }
+    if model_args.config_name:
+        config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
+    elif model_args.model_name_or_path:
+        config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
+    else:
+        config = CONFIG_MAPPING[model_args.model_type]()
+        logger.warning("You are instantiating a new config instance from scratch.")
+        if model_args.config_overrides is not None:
+            logger.info(f"Overriding config: {model_args.config_overrides}")
+            config.update_from_string(model_args.config_overrides)
+
+    tokenizer_kwargs = {
+        "cache_dir": model_args.cache_dir,
+        "use_fast": model_args.use_fast_tokenizer,
+        "revision": model_args.model_revision,
+        "use_auth_token": True if model_args.use_auth_token else None,
+    }
+    if model_args.tokenizer_name:
+        tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
+    elif model_args.model_name_or_path:
+        tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs)
+    else:
+        raise ValueError(
+            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
+            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
+        )
+
+    if model_args.model_name_or_path:
+        model = AutoModelForCausalLM.from_pretrained(
+            model_args.model_name_or_path,
+            from_tf=bool(".ckpt" in model_args.model_name_or_path),
+            config=config,
+            cache_dir=model_args.cache_dir,
+            revision=model_args.model_revision,
+            use_auth_token=True if model_args.use_auth_token else None,
+        )
+    else:
+        model = AutoModelForCausalLM.from_config(config)
+        n_params = sum(dict((p.data_ptr(), p.numel()) for p in model.parameters()).values())
+        logger.info(f"Training new model from scratch - Total size={n_params/2**20:.2f}M params")
+
+    model.resize_token_embeddings(len(tokenizer))
+
+    train_dataset = load_from_disk('dataset/train2')
+    eval_dataset = load_from_disk('dataset/eval2')
+
+    # Initialize our Trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset if training_args.do_train else None,
+        eval_dataset=eval_dataset if training_args.do_eval else None,
+        tokenizer=tokenizer,
+        # Data collator will default to DataCollatorWithPadding, so we change it.
+        data_collator=default_data_collator,
+    )
+
+    # Training
+    if training_args.do_train:
+        checkpoint = None
+        if training_args.resume_from_checkpoint is not None:
+            checkpoint = training_args.resume_from_checkpoint
+        elif last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        trainer.save_model()  # Saves the tokenizer too for easy upload
+
+        metrics = train_result.metrics
+
+        max_train_samples = (
+            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
+        )
+        metrics["train_samples"] = min(max_train_samples, len(train_dataset))
+
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # Evaluation
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+
+        metrics = trainer.evaluate()
+
+        max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
+        metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
+        try:
+            perplexity = math.exp(metrics["eval_loss"])
+        except OverflowError:
+            perplexity = float("inf")
+        metrics["perplexity"] = perplexity
+
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-generation"}
+    if data_args.dataset_name is not None:
+        kwargs["dataset_tags"] = data_args.dataset_name
+        if data_args.dataset_config_name is not None:
+            kwargs["dataset_args"] = data_args.dataset_config_name
+            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+        else:
+            kwargs["dataset"] = data_args.dataset_name
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+
+def _mp_fn(index):
+    # For xla_spawn (TPUs)
+    main()
+
+
+if __name__ == "__main__":
+    main()
+
diff --git a/README.md b/README.md
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+---
+license: apache-2.0
+pipeline_tag: text-generation
+widget:
+- text: 1.1.1.21<sep><start>
+inference:
+  parameters:
+    top_k: 9
+    repetition_penalty: 1.2
+tags:
+- biology
+---
+# **ZymCTRL**
+
+ZymCTRL (Enzyme Control) ([ see preprint ](https://www.biorxiv.org/content/10.1101/2024.05.03.592223v1)) 
+is a conditional language model for the generation of artificial functional enzymes. 
+It was trained on the UniProt database of sequences containing (Enzyme Commission) EC annotations, comprising over 37 M sequences. 
+Given a user-defined Enzymatic Commission (EC) number, the model generates protein sequences that fulfil that catalytic reaction. 
+The generated sequences are ordered, globular, and distant to natural ones, while their intended catalytic properties match those defined by users.
+
+If you don't know the EC number of your protein of interest, have a look for example here: https://www.brenda-enzymes.org/ecexplorer.php?browser=1
+
+See below for information about the model, how to generate sequences, and how to save and rank them by perplexity.
+
+## **Model description**
+ZymCTRL is based on the [CTRL Transformer](https://arxiv.org/abs/1909.05858) architecture (which in turn is very similar to ChatGPT) and contains 36 layers
+with a model dimensionality of 1280, totaling 738 million parameters. 
+
+ZymCTRL is a decoder-only transformer model pre-trained on the Uniprot subset of enzyme sequences, totalling 37M sequences.
+(version July 2022). The pre-training was done on the raw sequences without FASTA headers,
+with the EC classes prepended to each sequence. The databases will be uploaded soon.
+
+ZymCTRL was trained with an autoregressive objective, i.e., the model learns to predict
+the next token given a sequence context. Because the first tokens on each sequence encode the EC numbers,
+the model learns the dependencies among EC classes and their corresponding sequences and is able to _speak_ the enzyme language.
+
+There are stark differences in the number of members among EC classes, and for this reason, we also tokenized the EC numbers.
+In this manner, EC numbers '2.7.1.1' and '2.7.1.2' share the first three tokens (six, including separators), and hence the model can infer that
+there are relationships between the two classes.
+
+The figure below summarizes the process of training:
+
+![plot](./github1.png)
+
+ 
+## **How to use ZymCTRL**
+ZymCTRL can be used with the HuggingFace transformer python package.
+Detailed installation instructions can be found here: https://huggingface.co/docs/transformers/installation
+
+Since ZymCTRL has been trained on the classical language model objective on enzyme sequences with their EC annotation,
+it particularly excels at generating enzyme sequences given a user-defined EC class, such as alcohol dehydrogenases ('1.1.1.2').
+
+The model can generate in two ways: in a zero-shot fashion, i.e., directly generating from the checkpoint weights, or after fine-tuning. 
+Fine-tuning allows augmenting the specific EC datasets that were used during training, for example, 
+if you have a curated internal dataset or a set of ancestrally-reconstructed sequences. This is entirely optional. One advantage of 
+running the model in zero-shot is that it doesn't require any further training.
+
+
+### **Example 1: Generating nitrilases (EC 3.5.5.1)**  
+
+The script below will be used for the generation of any EC class in a zero-shot fashion, 
+here we showcase the generation of novel nitrilases.
+
+To run this script, you should download ZymCTRL to a local folder in your workstation. 
+Then replace the placeholders in the script with your actual folder path.
+
+You can run it directly in the command line (once you have hugging face installed), 
+with the following command: `python generate.py`
+
+The script will write each sequence in a fasta file in the folder you specify. In the fasta header,
+it will store the sequence's computed perplexity value. Perplexity is a measure of the model's confidence
+in that generation, with lower values being better. The sequences are ordered by perplexity before writing them out,
+so those that finish in *_0.fasta and *_1.fasta will be the best ones per batch. 
+
+**Given that generation runs so fast, we recommend generating hundreds or thousands and then only picking the best 5% or less. 
+With the script below, that would mean picking only those that finish in '_0.fasta'. Good perplexity values for this model so be below 1.75-1.5.** 
+
+```python
+import torch
+from transformers import GPT2LMHeadModel, AutoTokenizer
+import os
+from tqdm import tqdm
+import math
+
+def remove_characters(sequence, char_list):
+    "This function removes special tokens used during training."
+    columns = sequence.split('<sep>')
+    seq = columns[1]
+    for char in char_list:
+        seq = seq.replace(char, '')
+    return seq
+
+def calculatePerplexity(input_ids,model,tokenizer):
+    "This function computes perplexities for the generated sequences"
+    with torch.no_grad():
+        outputs = model(input_ids, labels=input_ids)
+    loss, logits = outputs[:2]
+    return math.exp(loss)
+        
+def main(label, model,special_tokens,device,tokenizer):
+    # Generating sequences
+    input_ids = tokenizer.encode(label,return_tensors='pt').to(device)
+    outputs = model.generate(
+        input_ids, 
+    	top_k=9, #tbd
+        repetition_penalty=1.2,
+        max_length=1024,
+        eos_token_id=1,
+        pad_token_id=0,
+   	    do_sample=True,
+   	    num_return_sequences=20) # Depending non your GPU, you'll be able to generate fewer or more sequences. This runs in an A40.
+    
+    # Check sequence sanity, ensure sequences are not-truncated.
+    # The model will truncate sequences longer than the specified max_length (1024 above). We want to avoid those sequences.
+    new_outputs = [ output for output in outputs if output[-1] == 0]
+    if not new_outputs:
+        print("not enough sequences with short lengths!!")
+
+    # Compute perplexity for every generated sequence in the batch
+    ppls = [(tokenizer.decode(output), calculatePerplexity(output, model, tokenizer)) for output in new_outputs ]
+
+    # Sort the batch by perplexity, the lower the better
+    ppls.sort(key=lambda i:i[1]) # duplicated sequences?
+
+    # Final dictionary with the results
+    sequences={}
+    sequences[label] = [(remove_characters(x[0], special_tokens), x[1]) for x in ppls]
+
+    return sequences
+
+if __name__=='__main__':
+    device = torch.device("cuda") # Replace with 'cpu' if you don't have a GPU - but it will be slow
+    print('Reading pretrained model and tokenizer')
+    tokenizer = AutoTokenizer.from_pretrained('/path/to/zymCTRL/') # change to ZymCTRL location
+    model = GPT2LMHeadModel.from_pretrained('/path/to/zymCTRL').to(device) # change to ZymCTRL location
+    special_tokens = ['<start>', '<end>', '<|endoftext|>','<pad>',' ', '<sep>']
+
+    # change to the appropriate EC classes
+    labels=['3.5.5.1'] # nitrilases. You can put as many labels as you want.
+
+    for label in tqdm(labels):
+        # We'll run 100 batches per label. 20 sequences will be generated per batch.
+        for i in range(0,100): 
+            sequences = main(label, model, special_tokens, device, tokenizer)
+            for key,value in sequences.items():
+                for index, val in enumerate(value):
+                    # Sequences will be saved with the name of the label followed by the batch index,
+                    # and the order of the sequence in that batch.           
+                    fn = open(f"/path/to/folder/{label}_{i}_{index}.fasta", "w")
+                    fn.write(f'>{label}_{i}_{index}\t{val[1]}\n{val[0]}')
+                    fn.close()
+```
+ 
+
+## **Example 2: Fine-tuning on a set of user-defined sequences**  
+
+This alternative to the zero-shot generation allows updating ZymCTRL's weights to new sequences.
+
+This strategy is not strictly necessary, in fact, we have observed good generations even for EC classes where there are 
+only 1-2 representatives in Nature. But you might have an internal set of sequences that you'd like to incorporate into the model.
+For example, internal datasets after protein engineering efforts, 
+ancestrally-reconstructed sets, or after searching against metagenomics databases. In these cases, it is advisable to fine-tune ZymCTRL,
+as it will learn new properties from your dataset and potentially improve the generation quality 
+(especially for poorly populated EC classes).
+
+To fine-tune ZymCTRL, you can use the script below to process your sequences. The only requisite is to start with an input file,
+'sequences.fasta' which contains all the sequences in a fasta format. Please follow the format below. There should not be new lines '\n' or 
+any separator between sequences. In the script, change the variable ec_label to the specific EC class you'd like to fine-tune.
+The script will produce a file called {ec_label}_processed.txt and a folder with the training and validation datasets (split 10%)
+```
+>Sequence1
+MMMMYMPLKVCD..
+>Sequence2
+MQWMXMYMPLKVCD..
+>Sequence3
+MPLKVCWMXMYMPLD..
+```
+We recommend using at least 200 sequences to obtain the best results. But we've seen it working with fewer sequences, so if you don't have
+that many, give it still a go.
+
+
+```python
+import random
+from transformers import AutoTokenizer
+
+from datasets import load_dataset
+import transformers
+from transformers.testing_utils import CaptureLogger
+
+## DEFINE THESE VARIABLES
+tokenizer = AutoTokenizer.from_pretrained('AI4PD/ZymCTRL')
+ec_label = '1.1.1.1' # CHANGE TO YOUR LABEL
+validation_split_percentage = 10 # change if you want
+sequence_file = 'sequence.fasta'
+
+
+#Load sequences, Read source file
+with open(sequence_file, 'r') as fn: #! CHANGE TO SEQUENCES.FASTA
+    data = fn.readlines()
+    fn.close()
+
+# Put sequences into dictionary
+sequences={}
+for line in data:
+    if '>' in line:
+        name = line.strip()
+        sequences[name] = []  #! CHANGE TO corre
+        continue
+    sequences[name].append(line.strip())
+
+#Pass sequences to list and shuffle their order randomly
+sequences_list = [(key,value[0]) for key,value in sequences.items()]
+random.shuffle(sequences_list)
+
+#the objective is to get here strings, that when tokenized, would span a length of 1024.
+#for each sequence group its length and untokenized string
+print("procesing dataset")
+processed_dataset = []
+for i in sequences_list:
+    # length of the control code
+    sequence = i[1].strip()
+    separator = '<sep>'
+    control_code_length = len(tokenizer(ec_label+separator)['input_ids'])
+    available_space = 1021 - control_code_length # It is not 1024 because '<|endoftext|>', and start and end
+
+    # Option 1: the sequence is larger than the available space (3-4% of sequences)
+    if len(sequence) > available_space:
+        total_length = control_code_length + len(sequence[:available_space]) + 1
+        seq = f"{ec_label}{separator}{sequence[:available_space]}<|endoftext|>"
+        processed_dataset.append((total_length, seq))
+
+    # Option 2 & 3: The sequence fits in the block_size space with or without padding
+    else:
+        total_length = control_code_length + len(sequence) + 3
+        # in this case the sequence does not fit with the start/end tokens
+        seq = f"{ec_label}{separator}<start>{sequence}<end><|endoftext|>"
+        processed_dataset.append((total_length, seq))
+
+# Group sequences
+def grouper(iterable):
+    prev = None
+    group = ''
+    total_sum = 0
+    for item in iterable:
+        if prev is None or item[0] + total_sum < 1025:
+            group += item[1]
+            total_sum += item[0]
+        else:
+            total_sum = item[0]
+            yield group
+            group = item[1]
+        prev = item
+    if group:
+        total_sum = 0
+        yield group
+
+print("grouping processed dataset")
+grouped_dataset=dict(enumerate(grouper(processed_dataset),1))
+
+# Write file out for the tokenizer to read
+fn = open(f"{ec_label}_processed.txt",'w')
+for key,value in grouped_dataset.items():
+    padding_len = 1024 - len(tokenizer(value)['input_ids'])
+    padding = "<pad>"*padding_len
+    fn.write(value+padding)
+    fn.write
+    fn.write("\n")
+fn.close()
+
+##TOKENIZE
+# adapted from the trainer file
+data_files = {}
+dataset_args = {}
+
+data_files["train"] = f"{ec_label}_processed.txt"
+extension = "text"
+tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")
+
+raw_datasets = load_dataset(extension, data_files=data_files, cache_dir='.', **dataset_args)
+
+raw_datasets["train"] = load_dataset(extension,
+                data_files=data_files,
+                split=f"train[{validation_split_percentage}%:]",
+                cache_dir='.',
+                **dataset_args,)
+
+raw_datasets["validation"] = load_dataset(extension,
+                                          data_files=data_files,
+                                          split=f"train[:{validation_split_percentage}%]",
+                                          cache_dir='.',
+                                          **dataset_args,)
+
+def tokenize_function(examples):
+    with CaptureLogger(tok_logger) as cl:
+        output = tokenizer(examples["text"])
+    # clm input could be much much longer than block_size
+    if "Token indices sequence length is longer than the" in cl.out:
+        tok_logger.warning(
+            "^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits before being passed to the model."
+        )
+    return output
+
+tokenized_datasets = raw_datasets.map(
+    tokenize_function,
+    batched=True,
+    num_proc=32,
+    remove_columns=['text'],
+    load_from_cache_file = False,
+    desc="Running tokenizer on dataset",
+)
+
+block_size = 1024
+def group_texts(examples):
+    # Concatenate all texts.
+    concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+    total_length = len(concatenated_examples[list(examples.keys())[0]])
+    # We drop the small remainder, we could add padding if the model supported it instead of this drop,
+    # you can customize this part to your needs.
+    if total_length >= block_size:
+        total_length = (total_length // block_size) * block_size
+    # Split by chunks of max_len.
+    result = {
+        k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
+        for k, t in concatenated_examples.items()
+    }
+    result["labels"] = result["input_ids"].copy()
+    return result
+
+lm_datasets = tokenized_datasets.map(
+    group_texts,
+    batched=True,
+    num_proc=124,
+    load_from_cache_file=False,
+    desc=f"Grouping texts in chunks of {block_size}",
+)
+
+train_dataset = lm_datasets["train"]
+eval_dataset = lm_datasets["validation"]
+
+train_dataset.save_to_disk('./dataset/train2')
+eval_dataset.save_to_disk('./dataset/eval2')
+
+
+```
+The processed datasets will be inside the folder dataset/, called train2 and eval2.
+You could also put the two previous scripts into a single one and run it in one go (that is what we do).
+
+Now you are ready to fine-tune the model. 
+To do that, you can take the trainer file that we provide in this repository (5.run_clm-post.py), or use the trainer from Hugging Face. 
+The command below shows an example at an specific learning rate, 
+but you could try with other hyperparameters to obtain the best training and evaluation losses.
+
+```bash
+python 5.run_clm-post.py --tokenizer_name AI4PD/ZymCTRL
+ --do_train --do_eval --output_dir output --eval_strategy steps --eval_steps 10
+ --logging_steps 5 --save_steps 500 --num_train_epochs 28 --per_device_train_batch_size 1
+ --per_device_eval_batch_size 4 --cache_dir '.' --save_total_limit 2 --learning_rate  0.8e-04
+ --dataloader_drop_last True --model_name_or_path AI4PD/ZymCTRL
+```
+In any case, the original HuggingFace script run_clm.py can be found here: 
+https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_clm.py
+
+
+### **Training specs**
+The model was trained on 48 NVIDIA A100 GPUs for eight epochs, 
+using a block size of 1024 and a total batch size of 768. 
+The optimizer used was Adam (beta1 = 0.9, beta2 = 0.999) 
+with a learning rate of 0.8e-04.
+
+### **Contact**
+
+We are the AI for Protein Design group at the Centre for Genomic Regulation (https://www.aiproteindesign.com/).
+For any questions post an issue in this repository so that other people can benefit from the feedback, and I'll get back to you shortly.
+We are always open for collaborations, send an email to noelia [dot] ferruz [at] crg [dot] eu.
\ No newline at end of file
diff --git a/config.json b/config.json
new file mode 100644
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+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 1,
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+  "initializer_range": 0.02,
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+  "summary_proj_to_labels": true,
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+  "summary_use_proj": true,
+  "task_specific_params": {
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+      "max_length": 6092
+    }
+  },
+  "transformers_version": "4.21.1",
+  "use_cache": true,
+  "vocab_size": 458
+}
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+  "version": "1.0",
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