metadata

license: apache-2.0
language:
  - en

RedPajama-Base-INCITE-2.8B

RedPajama-Base-INCITE-2.8B-v1, is a large transformer-based language model developed by Together Computer and trained on the RedPajama-Data-1T dataset.

Model Details

Developed by: Together Computer.
Model type: Language Model
Language(s): English
License: Apache 2.0
Model Description: A 2.8B parameter pretrained language model.

Quick Start

GPU Inference

This requires a GPU with 8GB memory.

import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM

MIN_TRANSFORMERS_VERSION = '4.25.1'

# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'

# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-Base-INCITE-2.8B-v1")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-Base-INCITE-2.8B-v1", torch_dtype=torch.float16)
model = model.to('cuda:0')

# infer
prompt = "Alan Turing is"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
    **inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True,
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
a name that has been synonymous with the computer age since the 1950s. The British mathematician, logician, and cryptanalyst is widely regarded as the father of modern computing. His contributions to the development of the modern computer and the theory of computation have had a profound impact on the world we live in today.
Turing’s contributions to the development of the modern computer were made in the 1940s and 1950s. He is most famous for his work on the Turing machine, a theoretical model of a computing machine that was able to perform all the mathematical operations of a computer. Turing’s work on the...
"""

GPU Inference in Int8

To run inference with int8, please ensure you have installed accelerate and bitandbytes. You can install them with the following command:

pip install accelerate
pip install bitsandbytes

Then you can run inference with int8 as follows:

import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM

MIN_TRANSFORMERS_VERSION = '4.25.1'

# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'

# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-Base-INCITE-2.8B-v1")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-Base-INCITE-2.8B-v1", device_map='auto', torch_dtype=torch.float16, load_in_8bit=True)

# infer
prompt = "Alan Turing is"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
    **inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
the man who cracked the Enigma code during World War II, and who was later convicted of homosexual acts. He was a brilliant mathematician, and a visionary who foresaw the computer age....
"""

CPU Inference

You can run inference on CPU as follows:

import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM

MIN_TRANSFORMERS_VERSION = '4.25.1'

# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'

# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-Base-INCITE-2.8B-v1")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-Base-INCITE-2.8B-v1", torch_dtype=torch.float32)
# infer
prompt = "Alan Turing is"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
    **inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
one of the most famous people to have come out of Cambridge. He is also one of the most famous people to have been arrested for homosexuality.
"""

Please note that since LayerNormKernelImpl is not implemented in fp16 for CPU, we use fp32 for CPU inference.

Uses

Direct Use

The model is intended for research purposes. Possible research areas and tasks include

Safe deployment of models which have the potential to generate harmful content.
Probing and understanding the limitations and biases of dialogue models or language models.
Generation of artworks and use in design and other artistic processes.
Applications in educational or creative tools.
Research on dialogue models or language models.

Excluded uses are described below.

Misuse, Malicious Use, and Out-of-Scope Use

It is the responsibility of the end user to ensure that the model is used in a responsible and ethical manner.

Out-of-Scope Use

RedPajama-Base-INCITE-2.8B is a language model and may not perform well for other use cases outside of its intended scope. For example, it may not be suitable for use in safety-critical applications or for making decisions that have a significant impact on individuals or society. It is important to consider the limitations of the model and to only use it for its intended purpose.

Misuse and Malicious Use

RedPajama-Base-INCITE-2.8B is designed for language modeling. Misuse of the model, such as using it to engage in illegal or unethical activities, is strictly prohibited and goes against the principles of the OpenChatKit community project.

Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:

Generating fake news, misinformation, or propaganda
Promoting hate speech, discrimination, or violence against individuals or groups
Impersonating individuals or organizations without their consent
Engaging in cyberbullying or harassment
Defamatory content
Spamming or scamming
Sharing confidential or sensitive information without proper authorization
Violating the terms of use of the model or the data used to train it
Creating automated bots for malicious purposes such as spreading malware, phishing scams, or spamming

Limitations

RedPajama-Base-INCITE-2.8B, like other language models, has limitations that should be taken into consideration. For example, the model may not always provide accurate or relevant answers, particularly for questions that are complex, ambiguous, or outside of its training data. We therefore welcome contributions from individuals and organizations, and encourage collaboration towards creating a more robust and inclusive chatbot.

Training

Training Data

Please refer to togethercomputer/RedPajama-Data-1T

Training Procedure

Hardware: 256 nodes of 6xV100 (IBM Power9), on the OLCF Summit cluster
Optimizer: Apex FusedAdam
Parallelism: Pipeline parallel 6, tensor parallel 2
Gradient Accumulations: 8 (global batch size 4M tokens)
Num of Tokens: 800B Tokens
Learning rate: 0.00016

Community

Join us on Together Discord