README.md

metadata

datasets:
  - louisbrulenaudet/Romulus-cpt-fr
license: llama3
language:
  - fr
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
  - law
  - droit
  - unsloth
  - trl
  - transformers
  - sft
  - llama

QuantFactory/Romulus-cpt-Llama-3.1-8B-v0.1-GGUF

This is quantized version of louisbrulenaudet/Romulus-cpt-Llama-3.1-8B-v0.1 created using llama.cpp

Original Model Card

Romulus, continually pre-trained models for French law.

Romulus is a series of continually pre-trained models enriched in French law and intended to serve as the basis for a fine-tuning process on labeled data. Please note that these models have not been aligned for the production of usable text as they stand, and will certainly need to be fine-tuned for the desired tasks in order to produce satisfactory results.

The training corpus is made up of around 34,864,949 tokens (calculated with the meta-llama/Meta-Llama-3.1-8B tokenizer).

Hyperparameters

The following table outlines the key hyperparameters used for training Romulus.

Parameter	Description	Value
max_seq_length	Maximum sequence length for the model	4096
load_in_4bit	Whether to load the model in 4-bit precision	False
model_name	Pre-trained model name from Hugging Face	meta-llama/Meta-Llama-3.1-8B
r	Rank of the LoRA adapter	128
lora_alpha	Alpha value for the LoRA module	32
lora_dropout	Dropout rate for LoRA layers	0
bias	Bias type for LoRA adapters	none
use_gradient_checkpointing	Whether to use gradient checkpointing	unsloth
train_batch_size	Per device training batch size	8
gradient_accumulation_steps	Number of gradient accumulation steps	8
warmup_ratio	Warmup steps as a fraction of total steps	0.1
num_train_epochs	Number of training epochs	1
learning_rate	Learning rate for the model	5e-5
embedding_learning_rate	Learning rate for embeddings	1e-5
optim	Optimizer used for training	adamw_8bit
weight_decay	Weight decay to prevent overfitting	0.01
lr_scheduler_type	Type of learning rate scheduler	linear

Training script

Romulus was trained using Unsloth on a Nvidia H100 Azure EST US instance provided by the Microsoft for Startups program from this script:

# -*- coding: utf-8 -*-
import os

from typing import (
    Dict,
)

from datasets import load_dataset
from unsloth import (
    FastLanguageModel,
    is_bfloat16_supported,
    UnslothTrainer,
    UnslothTrainingArguments,
)

max_seq_length = 4096
dtype = None
load_in_4bit = False

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="meta-llama/Meta-Llama-3.1-8B",
    max_seq_length=max_seq_length,
    dtype=dtype,
    load_in_4bit=load_in_4bit,
    token="hf_token",
)

model = FastLanguageModel.get_peft_model(
    model,
    r=128,
    target_modules=[
        "q_proj",
        "k_proj",
        "v_proj",
        "o_proj",
        "gate_proj",
        "up_proj",
        "down_proj",
        "embed_tokens",
        "lm_head",
    ],
    lora_alpha=32,
    lora_dropout=0,
    bias="none",
    use_gradient_checkpointing="unsloth",
    random_state=3407,
    use_rslora=True,
    loftq_config=None,
)

prompt = """### Référence :
{}
### Contenu :
{}"""

EOS_TOKEN = tokenizer.eos_token

def formatting_prompts_func(examples):
    """
    Format input examples into prompts for a language model.

    This function takes a dictionary of examples containing titles and texts,
    combines them into formatted prompts, and appends an end-of-sequence token.

    Parameters
    ----------
    examples : dict
        A dictionary containing two keys:
        - 'title': A list of titles.
        - 'text': A list of corresponding text content.

    Returns
    -------
    dict
        A dictionary with a single key 'text', containing a list of formatted prompts.

    Notes
    -----
    - The function assumes the existence of a global `prompt` variable, which is a
      formatting string used to combine the title and text.
    - The function also assumes the existence of a global `EOS_TOKEN` variable,
      which is appended to the end of each formatted prompt.
    - The input lists 'title' and 'text' are expected to have the same length.

    Examples
    --------
    >>> examples = {
    ...     'title': ['Title 1', 'Title 2'],
    ...     'text': ['Content 1', 'Content 2']
    ... }
    >>> formatting_cpt_prompts_func(examples)
    {'text': ['<formatted_prompt_1><EOS>', '<formatted_prompt_2><EOS>']}
    """
    refs = examples["ref"]
    texts = examples["texte"]
    outputs = []

    for ref, text in zip(refs, texts):
        text = prompt.format(ref, text) + EOS_TOKEN
        outputs.append(text)

    return {
        "text": outputs,
    }


cpt_dataset = load_dataset(
    "louisbrulenaudet/Romulus-cpt-fr",
    split="train",
    token="hf_token",
)

cpt_dataset = cpt_dataset.map(
    formatting_prompts_func,
    batched=True,
)

trainer = UnslothTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=cpt_dataset,
    dataset_text_field="text",
    max_seq_length=max_seq_length,
    dataset_num_proc=2,
    args=UnslothTrainingArguments(
        per_device_train_batch_size=8,
        gradient_accumulation_steps=8,
        warmup_ratio=0.1,
        num_train_epochs=1,
        learning_rate=5e-5,
        embedding_learning_rate=1e-5,
        fp16=not is_bfloat16_supported(),
        bf16=is_bfloat16_supported(),
        logging_steps=1,
        report_to="wandb",
        save_steps=350,
        run_name="romulus-cpt",
        optim="adamw_8bit",
        weight_decay=0.01,
        lr_scheduler_type="linear",
        seed=3407,
        output_dir="outputs",
    ),
)

trainer_stats = trainer.train()

Citing & Authors

If you use this code in your research, please use the following BibTeX entry.

@misc{louisbrulenaudet2024,
  author =       {Louis Brulé Naudet},
  title =        {Romulus, continually pre-trained models for French law},
  year =         {2024}
  howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/Romulus-cpt-fr}},
}

Feedback

If you have any feedback, please reach out at [email protected].