How to Build Your Own Coding Copilot with AMD Radeon GPU Platform

Generative
AI
is
revolutionizing
software
engineering,
with
new
tools
making
it
easier
to
build
AI-driven
code
assistants.
According
to

AMD
blog,
developers
can
now
create
their
own
coding
Copilot
using
AMD
Radeon^TM
graphics
cards
and
open-source
software.

AMD
Radeon
and
RDNA
Architecture

The
latest
AMD
RDNA^TM
architecture,
which
powers
both
cutting-edge
gaming
and
high-performance
AI
experiences,
provides
robust
large-model
inference
acceleration
capabilities.
Incorporating
this





technology
into
a
local
coding
Copilot
setup
offers
significant
advantages
in
terms
of
speed
and
efficiency
for
developers.

Required
Tools
and
Setup

To
create
a
personal
coding
Copilot,
developers
need
the
following
components:

• Windows
  11
• VSCode
  (Integrated
  Development
  Environment)
• Continue
  extension
  for
  VSCode
• LM
  Studio
  (v0.2.20
  ROCm)
  for
  LLM
  inference
• AMD
  Radeon
  7000
  Series
  GPU

LM
Studio
serves
as
the
inference
server
for
the
Llama3
model,
while
the
Continue
extension
connects
to
this
server,
acting
as
the
Copilot
client
within
VSCode.

Implementation
Steps

Step
1:
Set
up
LM
Studio
with
Llama3.
The
latest
version
of
LM
Studio
ROCm
v0.2.22
supports
AMD
Radeon
7000
Series
Graphics
cards
and
has
added
Llama3
to
its
list
of
supported
models.
It
also
supports
other
state-of-the-art
LLMs
like
Mistral.

LM
Studio
can
act
as
an
inference
server.
Developers
can
launch
an
OpenAI
API
HTTP
inference
service
by
clicking
the
Local
Inference
Server
button
in
the
LM
Studio
interface,
with
the
default
port
set
to

http://localhost:1234.

Step
2:
Set
up
the
Continue
extension
in
VSCode.
Search
and
install
the
Continue
extension.
Modify
the
config.json
file
to
set
LM
Studio
as
the
default
model
provider.
This
allows
developers
to
chat
with
Llama3
through
the
Continue
interface
in
VSCode.

Advantages
and
Applications

Continue
provides
a
seamless
interface
for
developers
to
interact
with
the
Llama3
model,
offering
functionalities
like
code
generation
and
autocompletion.
This
setup
is
particularly
beneficial
for
individual
developers
who
may
not
have
access
to
large-scale
AI
inference
capabilities
in
the
cloud.

The
integration
of
AMD
ROCm
open
ecosystem
with
LM
Studio
and
other
software
applications
highlights
the
rapid
development
of
AI
acceleration
solutions.
Developers
can
leverage
these
tools
to
enhance
their
productivity
and
streamline
their
coding
workflows.

Image
source:
Shutterstock

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