Awesome

Large Language Model Codebase

This repository is a framework for beginners to dive into llm training, inference and evaluation. The repository is based on huggingface transformers.

Overview

Training

• Supported task：Classification, Reward Model Training, Supervised Finetuning, Rejection Sampling, Weighted Learning, RRHF, DPO, KTO
• Supported model: bert, llama
• Supported training type: Full parameter training, Parameter efficient training

Inference

• Supported task: Reward Model Inference, LLM Inference.
• Supported model: llama

Evaluation

• Supported task: PPL, GPT4 win rate
• Supported model:
  • PPL: llama

Installation

First, you should use the following command to clone the repository.

git clone https://github.com/underwoodnoble/llm_codebase.git
cd llm_codebase

For most of the tasks, use the following command to install the required packages.

pip install -r requirements/normal.txt

For DPO, use the following command to install the required packages.

pip install -r requirements/dpo.txt

For llama1 reward model, use the following command to install the required packages.

pip install -r requirements/llama_rm.txt

Classification

There are two ways to train a classification model in the repository. The first one is to train a model with a classification head. The second one is to train a model with a classification dict head. The classification dict head is a head that can be used to train a model with multiple classification tasks.

Single objective classification

Supported Models: Bert, Llama

Under the hood, the classification head is a linear layer on top of the model output. This repository uses transformers AutoModelForSequenceClassification to train a classification model. The following code shows how to train a classification model with a classification head.

Specific Arguments

• cls_data_text_name: field name of the text in data.
• cls_data_label_name: field name of the label in data.
• cls_data_label_nums: number of classes.

Data Format

{
    "text": "text",
    "label": "label"
}

• You need to align parameter <font color='orange'><cls_data_text_name></font> with the field name of the text in data and <font color='orange'><cls_data_label_name></font> with the field name of the label in data.

Bert

bash scripts/bert_classification.sh

Llama

You can use accelerate or deepspeed to speed up the training process.

Accelerate

REPO_DIR=repo_dir
DATA_DIR=data_dir

DeepSpeed

Multi-objective classification

Reward Model Training

Supported Models: Bert, Llama Specific Arguments

• preference_data_text_name: field name of the text in data.
• preference_data_score_name: field name of the score in data.
• add_lm_loss: whether to add language model loss to the reward model. (Do not support bert)
• lm_loss_coeff: the coefficient of the language model loss. (Do not support bert)
• pooling_type: the pooling type of the model output. The pooling type can be "last", "max", "eos", or "average". (Do not support bert)
• rm_calibration: whether to calibrate the reward model using ece.
• calibration_bins: ece bins

Data Format

{
    "text": ["text1", "text2"],
    "score": ["score1", "score2"]
}

• You need to align parameter <font color='orange'><preference_data_text_name></font> with the field name of the text in data and <font color='orange'><preference_data_score_name></font> with the field name of the score in data.
• You can specify multiple datasets in <font color='orange'><data_paths></font> and <font color='orange'><eval_data_paths></font>. If different dataset have different number of texts(scores), the data will be padded to the maximum length of the texts(scores) in the dataset(padding text with " " and padding score with -100).

Bert reward model

bash scripts/bert_reward.sh

Llama reward model

For Llama1, we recommend to use the requirements/llama1.txt to install the required packages. Because we find that higher version of transformers may degrade the preformance of Llama1. And do not use bf16 to train llama reward model, this will degrade the performance too.

# Install the required packages
pip install -r requirements/llama1.txt
# Train the reward model
bash scripts/llama1_reward.sh