NVIDIA TensorRT-LLM Boosts Hebrew LLM Performance

Developing
a
high-performing
Hebrew
large
language
model
(LLM)
presents
distinct
challenges
due
to
the
complex
nature
of
the
Hebrew
language.
The
intricate
structure
of
Hebrew,
combined
with
the
lack
of
capitalization
and
frequent
absence
of
punctuation,
complicates
sentence
segmentation
and
accurate
text
processing.

Challenges
in
Hebrew
Language
Processing

Hebrew
words
are
formed
through
root
and
pattern
combinations,
leading
to
multiple
meanings
for
a
single
word
based
on
context.
Additionally,
Hebrew
syntax
allows
flexible
word
order,
adding
another
layer
of
complexity.
The
absence
of
diacritical
marks
that
convey
vowel
sounds
further
complicates
text
understanding.

To
address
these
challenges,
the
DictaLM-2.0
suite
of
Hebrew-specific
LLMs
was
trained
on
classical
and
modern
Hebrew
texts.
This
suite
has
led
the
Hugging
Face
Open
Leaderboard
for
Hebrew
LLMs.

Optimization
with
NVIDIA
TensorRT-LLM

NVIDIA’s
TensorRT-LLM
and
Triton
Inference
Server
offer
solutions
to
optimize
and
accelerate
the
deployment
of
Hebrew
LLMs
at
scale.
TensorRT-LLM
is
an
open-source
library
for
compiling
and
optimizing
LLMs
for
NVIDIA
GPUs,
while
Triton
Inference
Server
streamlines
AI
inference
workloads
for
production-ready
deployment.

Low-Resource
Languages

Low-resource
languages,
such
as
Hebrew,
lack
large
amounts
of
training
data.
This
scarcity
of
high-quality
digitized
text
data
makes
it
difficult
for
LLMs
to
capture
the
nuances
and
cultural
contexts
of
non-Western
languages.
As
a
result,
LLMs
trained
primarily
on
English
text
corpora
struggle
with
these
languages.

Contemporary
LLMs
rely
on
statistically-driven
tokenization
methods,
which
are
less
effective
for
low-resource
languages
due
to
limited
token
sets.
This
results
in
poor
compression
efficiency
and
increased
computational
complexity
for
generating
text
in
these
languages.

Optimization
Workflow

The
optimization
process
for
Hebrew
LLMs
involves
several
steps.
Initially,
the
DictaLM
2.0
Instruct
model,
pre-trained
on
Mistral
7B,
is
cloned
and
set
up
with
TensorRT-LLM.
The
Triton
Inference
Server
container
with
TensorRT-LLM
backend
is
then
pulled
and
run
to
optimize
the
model.

Creating
the
FP16
TensorRT-LLM
Engine

The
Hugging
Face
checkpoint
is
converted
to
TensorRT-LLM
format,
followed
by
building
the
optimized
engine.
Post-training
quantization
(PTQ)
to
INT4
is
performed
using
a
representative
dataset,
enhancing
memory
efficiency
while
maintaining
statistical
similarity.

Deploying
with
Triton
Inference
Server

After
building
the
optimized
engine,
the
model
is
deployed
with
Triton
Inference
Server,
which
leverages
the
TensorRT-LLM
C++
runtime
for
rapid
inference
execution.
Customized
tokenizers
are
set
up
to
handle
the
unique
token
mapping
of
low-resource
languages.

Performance
Results

Performance
experiments
conducted
on
a
single
NVIDIA
A100
GPU
showed
significant
improvements
in
latency
with
TensorRT-LLM
compared
to
a
non-accelerated
Python
backend.
The
TensorRT-LLM
provided
effective
scaling
for
multiple
asynchronous
requests,
demonstrating
its
efficiency.

Conclusion

NVIDIA
TensorRT-LLM
and
Triton
Inference
Server
offer
a
robust
toolkit
for
optimizing,
deploying,
and
running
LLMs
efficiently.
For
more
information,
visit
the

NVIDIA
Technical
Blog.

Image
source:
Shutterstock