ipt-350m

ipt-350m

ipt-350m is a decoder-style transformer pretrained from scratch on ~13B tokens of Italian text (wip: trained on unfiltered oscar).

It uses a modified transformer architecture optimized for efficient training and inference. Positional embeddings are replaced with Attention with Linear Biases (ALiBi).

ipt-350m is:

• Licensed for the possibility of commercial use
• Prepared to handle extremely long inputs thanks to ALiBi.
• Capable of fast training and inference (via FlashAttention and FasterTransformer)
• Equipped with highly efficient open-source training code via the llm-foundry repository

If you find this project useful, consider supporting its development:

How to Use

import transformers
model = transformers.AutoModelForCausalLM.from_pretrained(
  'efederici/ipt-350m',
  trust_remote_code=True
)

Note: This model requires that trust_remote_code=True be passed to the from_pretrained method.

To use the optimized triton implementation of FlashAttention, you can load the model on GPU (cuda:0) with attn_impl='triton' and with bfloat16 precision:

import torch
import transformers

name = 'efederici/ipt-350m'

config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.attn_config['attn_impl'] = 'triton'
config.init_device = 'cuda:0'

model = transformers.AutoModelForCausalLM.from_pretrained(
  name,
  config=config,
  torch_dtype=torch.bfloat16,
  trust_remote_code=True
)

Although the model was trained with a sequence length of 2048, ALiBi enables to increase the maximum sequence length during finetuning and/or inference.

import transformers

name = 'efederici/ipt-350m'

config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.max_seq_len = 4096 # (input + output) tokens can now be up to 4096

model = transformers.AutoModelForCausalLM.from_pretrained(
  name,
  config=config,
  trust_remote_code=True
)

Model Description

The architecture is a modification of a standard decoder-only transformer.

The model has been modified from a standard transformer in the following ways:

• It uses FlashAttention
• It uses ALiBi (Attention with Linear Biases) and does not use positional embeddings
• It does not use biases

Dataset

The model was trained for ~13B tokens (with batch size 64 and sequence length 2048) on OSCAR-2301. Each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length.

Vocabulary size is 50432, a multiple of 128 as suggested in MEGATRON-LM, model flop utilization (MFU) increased by up to four percentage points.