m-a-p/CT-LLM-SFT-DPO

🌐 Homepage | 🤗 MAP-CC | 🤗 CHC-Bench | 🤗 CT-LLM | 📖 arXiv | GitHub

Introduction

CT-LLM-SFT-DPO is an alignment version of CT-LLM. The main features of this model is:

1. Our model, an alignment-enhanced variant of CT-LLM-SFT, is trained using DPO, a direct preference-based learning method.
2. We utilize a combination of publicly available datasets and synthetic data to train our model.
3. Our model outperforms a range of 2B LLMs on the Cvalues benchmark, demonstrating its enhanced harmless nature. The Alignment training also enhance its general performances. Specifically, it shows enhanced results on benchmarks like COPA, CMMLU, Hellaswag, and TriviaQA compared to the CT-LLM-SFT version.

Training Data

Our model incorporates a blend of publicly accessible datasets and synthetic data from the LLM. The open-source Chinese datasets consist of non-harmful and beneficial sections from cvalues_rlhf, comparison_gpt4_data_zh and oaast_rm_zh in Llama-factory, huozi, and zhihu. For English, the dataset includes comparison_gpt4_data_en from Llama-factory and beavertails. To construct a more high-quality preference dataset via a synthetics approach, we adopt alpaca-gpt4 which generates "chosen" responses using GPT-4, and we adopt baichuan-6B serving as a weaker model for generating "reject" responses. The dataset comprises 183k Chinese pairs and 46k English pairs in total.

Training Settings

We leverage the CT-LLM-SFT as a reference model $\pi_{sft}$ to optimize the objective language model $\pi_{\theta}$. $\pi_{\theta}$ is initialized by the model parameters of the $\pi_{sft}$. We set the hyperparameters as follows:

1. The $\pi_{\theta}$ is trained on 8 H800,
2. learning rate $=1e-6$,
3. batch size $=4$,
4. epoch numbers $=2$,
5. weight decay $=0.1$,
6. warmup ratio $=0.03$,
7. $\beta=0.5$ to control the deviation from $\pi_{sft}$.

Results

Performance on CValues

Performance on General Benchmark

Uses

from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = '<your-model-path>'
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype='auto'
).eval()
messages = [
    {"role": "system", "content": "你是一个有用的人工智能助手。"},
    {"role": "user", "content": "你好"},
]
input_ids = tokenizer.apply_chat_template(conversation=messages, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'), max_new_tokens=20)
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
print(response)

Disclaimer

This model, developed for academic purposes, employs rigorously compliance-checked training data to uphold the highest standards of integrity and compliance. Despite our efforts, the inherent complexities of data and the broad spectrum of model applications prevent us from ensuring absolute accuracy or appropriateness of the model outputs in every scenario.

It is essential to highlight that our model and its associated training data are intended solely for scholarly research. We explicitly disclaim any liability for problems that may arise from improper use, interpretation errors, unlawful activities, the dissemination of false information, or any data security issues related to the utilization of our model or its training data.

We strongly encourage users to report any concerns related to data misuse, security breaches, or potential infringement issues directly to us for immediate investigation and resolution.

Contact: {[email protected]; [email protected]}

Our commitment to responsible data sharing and the security of our academic tools is paramount. We thank you for your cooperation in maintaining the ethical use of this technology.