pip install -r requirements.txt

# Translate OASST1 to target language
python translate.py m2m target_lang checkpoint_location

# Combine the checkpoint files into a dataset
python combine_checkpoints.py input_folder output_location

# Finetune
python finetune.py tuned_model dataset_name instruction_prompt

# Run inference
python run_inference.py model_name instruction_prompt input

usage: translate.py [-h] [--quant8] [--quant4] [--base_dataset BASE_DATASET] [--base_dataset_text_field BASE_DATASET_TEXT_FIELD] [--base_dataset_lang_field BASE_DATASET_LANG_FIELD]
                    [--checkpoint_n CHECKPOINT_N] [--batch_size BATCH_SIZE] [--max_length MAX_LENGTH] [--cpu] [--source_lang SOURCE_LANG]
                    {opus,mbart,madlad,m2m,nllb,seamless_m4t_v2,towerinstruct} ... target_lang checkpoint_location

Translate an instruct/RLHF dataset to a given target language using a variety of translation models

positional arguments:
  {opus,mbart,madlad,m2m,nllb,seamless_m4t_v2,towerinstruct}
                        The model/architecture used for translation.
    opus                Translate the dataset using HelsinkiNLP OPUS models.
    mbart               Translate the dataset using mBART.
    madlad              Translate the dataset using Google's MADLAD models.
    m2m                 Translate the dataset using Facebook's M2M models.
    nllb                Translate the dataset using Facebook's NLLB models.
    seamless_m4t_v2     Translate the dataset using Facebook's SeamlessM4T-v2 multimodal models.
    towerinstruct       Translate the dataset using Unbabel's Tower Instruct. Make sure your target language is in the 10 languages supported by the model.
  target_lang           The target language. Make sure you use language codes defined by the translation model you are using.
  checkpoint_location   The folder the script will write (JSONized) checkpoint files to. Folder will be created if it doesn't exist.

options:
  -h, --help            show this help message and exit
  --quant8              Optional flag to load the translation model in 8 bits. Decreases memory usage, increases running time
  --quant4              Optional flag to load the translation model in 4 bits. Decreases memory usage, increases running time
  --base_dataset BASE_DATASET
                        The base dataset to translate, defaults to OpenAssistant/oasst1
  --base_dataset_text_field BASE_DATASET_TEXT_FIELD
                        The base dataset's column name containing the actual text to translate. Defaults to text
  --base_dataset_lang_field BASE_DATASET_LANG_FIELD
                        The base dataset's column name containing the language the source text was written in. Defaults to lang
  --checkpoint_n CHECKPOINT_N
                        An integer representing how often a checkpoint file will be written out. To start off, 400 is a reasonable number.
  --batch_size BATCH_SIZE
                        The batch size for a single translation model. Adjust based on your GPU capacity. Default is 10.
  --max_length MAX_LENGTH
                        How much tokens to generate at most. More tokens might be more accurate for lengthy input but creates a risk of running out of memory. Default is unlimited.
  --cpu                 Forces usage of CPU. By default GPU is taken if available.
  --source_lang SOURCE_LANG
                        Source language to select from OASST based on lang property of dataset

python translate.py [MODEL] -h

# Using M2M with 4bit quantization and differen batch sizes to translate Dutch
python translate.py m2m nl ./output_nl --quant4 --batch_size 20

# Using madlad 7B with 8bit quantization for German with different max_length
python translate.py madlad --model_size 7b de ./output_de --quant8 --batch_size 5 --max_length 512

# Be sure to use target language codes that the model you use understands
python translate.py mbart xh_ZA ./output_xhosa
python translate.py nllb nld_Latn ./output_nl

usage: combine_checkpoints.py [-h] input_folder output_location

Combine checkpoint files from translation.

positional arguments:
  input_folder     The checkpoint folder used in translation, with the target language appended.
                   Example: "./output_nl".
  output_location  Where to write the Huggingface Dataset. Can be a disk location or a Huggingface
                   Dataset repository.

options:
  -h, --help       show this help message and exit

usage: finetune.py [-h] [--base_model BASE_MODEL] [--base_dataset_text_field BASE_DATASET_TEXT_FIELD] [--base_dataset_rank_field BASE_DATASET_RANK_FIELD]
                   [--base_dataset_id_field BASE_DATASET_ID_FIELD] [--base_dataset_parent_field BASE_DATASET_PARENT_FIELD] [--base_dataset_role_field BASE_DATASET_ROLE_FIELD]
                   [--quant8] [--noquant] [--max_seq_length MAX_SEQ_LENGTH] [--num_train_epochs NUM_TRAIN_EPOCHS] [--batch_size BATCH_SIZE]
                   [--threads_output_name THREADS_OUTPUT_NAME] [--thread_template THREAD_TEMPLATE]
                   tuned_model dataset_name instruction_prompt

Finetune a base instruct/chat model using (Q)LoRA and PEFT

positional arguments:
  tuned_model           The name of the resulting tuned model.
  dataset_name          The name of the dataset to use for fine-tuning. This should be the output of the combine_checkpoints script.
  instruction_prompt    An instruction message added to every prompt given to the chatbot to force it to answer in the target language. Example: "You are a generic chatbot that
                        always answers in English."

options:
  -h, --help            show this help message and exit
  --base_model BASE_MODEL
                        The base foundation model. Default is "NousResearch/Llama-2-7b-chat-hf".
  --base_dataset_text_field BASE_DATASET_TEXT_FIELD
                        The dataset's column name containing the actual text to translate. Defaults to text
  --base_dataset_rank_field BASE_DATASET_RANK_FIELD
                        The dataset's column name containing the rank of an answer given to a prompt. Defaults to rank
  --base_dataset_id_field BASE_DATASET_ID_FIELD
                        The dataset's column name containing the id of a text. Defaults to message_id
  --base_dataset_parent_field BASE_DATASET_PARENT_FIELD
                        The dataset's column name containing the parent id of a text. Defaults to parent_id
  --base_dataset_role_field BASE_DATASET_ROLE_FIELD
                        The dataset's column name containing the role of the author of the text (eg. prompter, assistant). Defaults to role
  --quant8              Finetunes the model in 8 bits. Requires more memory than the default 4 bit.
  --noquant             Do not quantize the finetuning. Requires more memory than the default 4 bit and optional 8 bit.
  --max_seq_length MAX_SEQ_LENGTH
                        The maximum sequence length to use in finetuning. Should most likely line up with your base model's default max_seq_length. Default is 512.
  --num_train_epochs NUM_TRAIN_EPOCHS
                        Number of epochs to use. 2 is default and has been shown to work well.
  --batch_size BATCH_SIZE
                        The batch size to use in finetuning. Adjust to fit in your GPU vRAM. Default is 4
  --threads_output_name THREADS_OUTPUT_NAME
                        If specified, the threads created in this script for finetuning will also be saved to disk or HuggingFace Hub.
  --thread_template THREAD_TEMPLATE
                        A file containing the thread template to use. Default is threads/template_fefault.txt

usage: run_inference.py [-h] model_name instruction_prompt input

Script to run inference on a tuned model.

positional arguments:
  model_name          The name of the tuned model that you pushed to Huggingface in the previous
                      step.
  instruction_prompt  An instruction message added to every prompt given to the chatbot to force
                      it to answer in the target language.
  input               The actual chat input prompt. The script is only meant for testing purposes
                      and exits after answering.

options:
  -h, --help          show this help message and exit

usage: benchmark.py [-h] [--cpu] [--start START] [--n N] [--max_length MAX_LENGTH] source_language target_language included_models

Benchmark all the different translation models for a specific source and target language to find out which performs best. This uses 4bit quantization to limit GPU usage. Note:
the outcomes are indicative - you cannot assume corretness of the BLEU and CHRF scores but you can compare models against each other relatively.

positional arguments:
  source_language       The source language you want to test for. Check your dataset to see which occur most prevalent or use English as a good start.
  target_language       The source language you want to test for. This should be the language you want to apply the translate script on. Note: in benchmark, we use 2-character
                        language codes, in constrast to translate.py where you need to specify whatever your model expects.
  included_models       Comma-separated list of models to include. Allowed values are: opus, m2m_418m, m2m_1.2b, madlad_3b, madlad_7b, madlad_10b, madlad_7bbt, mbart,
                        nllb_distilled600m, nllb_1.3b, nllb_distilled1.3b, nllb_3.3b, seamless

options:
  -h, --help            show this help message and exit
  --cpu                 Forces usage of CPU. By default GPU is taken if available.
  --start START         The starting offset to include sentences from the OPUS books dataset from. Defaults to 0.
  --n N                 The number of sentences to benchmark on. Defaults to 100.
  --max_length MAX_LENGTH
                        How much tokens to generate at most. More tokens might be more accurate for lengthy input but creates a risk of running out of memory. Default is 512.