Powerlm 3b

🚀 ibm/PowerLM-3b

PowerLM-3B is a 3B state-of-the-art small language model trained with the Power learning rate scheduler, showing promising results in various benchmarks.

🚀 Quick Start

PowerLM-3B is a 3B state-of-the-art small language model trained with the Power learning rate scheduler. It is trained on a mix of open - source and proprietary datasets. PowerLM-3B has shown promising results compared to other models in the size categories across various benchmarks, including natural language multi - choices, code generation, and math reasoning. Paper: https://arxiv.org/abs/2408.13359

📦 Installation

⚠️ Important Note

Requires installing HF transformers from source.

💻 Usage Examples

Basic Usage

This is a simple example of how to use PowerLM-3b model.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # or "cpu"
model_path = "ibm/PowerLM-3b"
tokenizer = AutoTokenizer.from_pretrained(model_path)
# drop device_map if running on CPU
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
model.eval()
# change input text as desired
prompt = "Write a code to find the maximum value in a list of numbers."
# tokenize the text
input_tokens = tokenizer(prompt, return_tensors="pt")
# transfer tokenized inputs to the device
for i in input_tokens:
    input_tokens[i] = input_tokens[i].to(device)
# generate output tokens
output = model.generate(**input_tokens, max_new_tokens=100)
# decode output tokens into text
output = tokenizer.batch_decode(output)
# loop over the batch to print, in this example the batch size is 1
for i in output:
    print(i)

📄 License

This model is licensed under the apache-2.0 license.

📚 Documentation

The following table shows the performance of the ibm/PowerLM-3b model on various benchmarks: