model-index:
- name: zephyr-math
results: []
license: apache-2.0
datasets:
- rishiraj/guanaco-style-metamath
language:
- en
tags:
- autotrain
- text-generation
widget:
- text: 'I love AutoTrain because '
Zephyr Math 7B Trained Using AutoTrain
Model Details
rishiraj/zephyr-math is the LLM (released under Apache License 2.0) fully fine-tuned on the MetaMathQA dataset and based on the powerful HuggingFaceH4/zephyr-7b-alpha model.
We try achieving State-Of-The-Art result in pass@1 on the GSM8k Benchmarks. The A100 GPU used for this fine-tuning process is generously provided by Weights & Biases. I am thankful to Soumik Rakshit from team W&B for constant support in this integration. The experiment can be tracked using Weights & Biases here.
Preparing the dataset
AutoTrain Advanced expects your CSV custom dataset in a certain format to work properly. Your training file must contain a "text" column on which the training will be done. For best results, the "text" column should have data in the ### Human: Question?### Assistant: Answer. format. A great example for the kind of dataset AutoTrain Advanced expects would be timdettmers/openassistant-guanaco. However, if you observe the MetaMathQA dataset, there are 3 columns - "query", "response" and "type". We will preprocess this dataset by removing the "type" column and combining the content of the "query" and "response" columns under one "text" column with the ### Human: Query?### Assistant: Response. format. The resulting dataset is rishiraj/guanaco-style-metamath and it will be used for training.
Adjusting hyperparameters
AutoTrain Advanced comes with a host hyperparameters we can tune to get the best model. While the default hyperparameters are a great start for everyone, I made a few changes there that are suitable for our use case. Here are the hyperparameters I used:
learning_rate = 2e-5
num_epochs = 3
batch_size = 4
block_size = 1024
trainer = "sft"
warmup_ratio = 0.03
weight_decay = 0.
gradient_accumulation = 4
use_fp16 = True
use_peft = True
use_int4 = True
merge_adapter = True
lora_r = 16
lora_alpha = 32
lora_dropout = 0.05
logging_steps = 10
log = "wandb"
Results
Check out the W&B Report for a detailed overview of the finetuned model including its Benchmark scores on a variety of tests like the ARC, HellaSwag, MMLU, TruthfulQA. I also included a comparison with other open-source LLMs on GSM8k Pass@1 and MATH Pass@1.
Model Usage
Here's how you can run the model using the pipeline() function from 🤗 Transformers:
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="rishiraj/zephyr-math", torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate",
},
{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
Experiments
| Model | GSM8k Pass@1 | MATH Pass@1 |
|---|---|---|
| MPT-7B | 6.8 | 3.0 |
| Falcon-7B | 6.8 | 2.3 |
| LLaMA-1-7B | 11.0 | 2.9 |
| LLaMA-2-7B | 14.6 | 2.5 |
| MPT-30B | 15.2 | 3.1 |
| LLaMA-1-13B | 17.8 | 3.9 |
| GPT-Neo-2.7B | 19.5 | -- |
| Falcon-40B | 19.6 | 2.5 |
| Baichuan-chat-13B | 23.9 | -- |
| Vicuna-v1.3-13B | 27.6 | -- |
| LLaMA-2-13B | 28.7 | 3.9 |
| InternLM-7B | 31.2 | -- |
| ChatGLM-2-6B | 32.4 | -- |
| GPT-J-6B | 34.9 | -- |
| LLaMA-1-33B | 35.6 | 3.9 |
| LLaMA-2-34B | 42.2 | 6.24 |
| RFT-7B | 50.3 | -- |
| LLaMA-1-65B | 50.9 | 10.6 |
| Qwen-7B | 51.6 | -- |
| WizardMath-7B | 54.9 | 10.7 |
| LLaMA-2-70B | 56.8 | 13.5 |
| WizardMath-13B | 63.9 | 14.0 |
| MAmmoTH-7B (COT) | 50.5 | 10.4 |
| MAmmoTH-7B (POT+COT) | 53.6 | 31.5 |
| Arithmo-Mistral-7B | 74.7 | 25.3 |
| MetaMath-7B | 66.5 | 19.8 |
| MetaMath-13B | 72.3 | 22.4 |
| 🔥 Zephyr-Math-7B | ?? | ?? |
Citation
@software{acharya2023zephyrmath
title = {Zephyr Math: Zephyr 7B Alpha Model Fine-tuned on MetaMathQA Dataset},
author = {Rishiraj Acharya and Soumik Rakshit},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/rishiraj/zephyr-math}},
}