NVIDIA Launches Nemotron-4 340B for Synthetic Data Generation in AI Training

NVIDIA
has
introduced
Nemotron-4
340B,
a
new
family
of
models
designed
to
generate
synthetic
data
for
training
large
language
models
(LLMs)
across
various
industries,
including
healthcare,
finance,
manufacturing,
and
retail,
according
to
the

NVIDIA
Blog.

Navigating
Nemotron
to
Generate
Synthetic
Data

High-quality
training
data
is
crucial
for
the
performance
and
accuracy
of
custom
LLMs.
However,
obtaining
robust
datasets
can
be
costly
and
challenging.
Nemotron-4
340B
aims
to
address
this
by
providing
developers
with
a
free
and
scalable
way
to
generate
synthetic
data
through
a
permissive
open
model
license.

The
Nemotron-4
340B
family
includes
base,
instruct,
and
reward
models
optimized
to
work
with
NVIDIA
NeMo
and
NVIDIA
TensorRT-LLM.
These
models
form
a
pipeline
to
generate
synthetic
data
used
for
training
and
refining
LLMs.
Developers
can
download
Nemotron-4
340B
from

Hugging
Face
and
will
soon
be
able
to
access
the
models
at
ai.nvidia.com.

Fine-Tuning
With
NeMo,
Optimizing
for
Inference
With
TensorRT-LLM

Utilizing
open-source
frameworks
such
as
NVIDIA
NeMo
and
NVIDIA
TensorRT-LLM,
developers
can
optimize
the
efficiency
of
their
instruct
and
reward
models
to
generate
synthetic
data
and
score
responses.
All
Nemotron-4
340B
models
are
optimized
with
TensorRT-LLM
to
leverage
tensor
parallelism,
enabling
efficient
inference
at
scale.

Nemotron-4
340B
Base,
trained
on
9
trillion
tokens,
can
be
customized
using
the
NeMo
framework
to
fit
specific
use
cases
or
domains.
This
fine-tuning
process
benefits
from
extensive
pretraining
data,
yielding
more
accurate
outputs
for
specific
downstream
tasks.

Customization
methods
available
through
the
NeMo
framework
include
supervised
fine-tuning
and
parameter-efficient
fine-tuning
methods
such
as
low-rank
adaptation
(LoRA).
Developers
can
also
align
their
models
with
NeMo
Aligner
and
datasets
annotated
by
Nemotron-4
340B
Reward
to
ensure
accurate
and
contextually
appropriate
outputs.

Evaluating
Model
Security
and
Getting
Started

The
Nemotron-4
340B
Instruct
model
has
undergone
extensive
safety
evaluation,
including
adversarial
tests,
and
performed
well
across
various
risk
indicators.
However,
users
should
still
carefully
evaluate
the
model’s
outputs
to
ensure
the
synthetically
generated
data
is
suitable,
safe,
and
accurate
for
their
specific
use
case.

For
more
detailed
information
on
model
security
and
safety
evaluation,
users
can
refer
to
the
model
card.
Nemotron-4
340B
models
can
be
downloaded
via
Hugging
Face.
Researchers
and
developers
interested
in
the
underlying
technology
can
also
review
the
research
papers
on
the
model
and
dataset.

Image
source:
Shutterstock