diff --git a/models/experimental/functional_mobilenetv2/README.md b/models/experimental/functional_mobilenetv2/README.md
new file mode 100644
index 000000000000..99061f50e3fa
--- /dev/null
+++ b/models/experimental/functional_mobilenetv2/README.md
@@ -0,0 +1,26 @@
+# MobilenetV2
+The MobileNetV2 model is a convolutional neural network (CNN) architecture designed for efficient mobile and embedded vision applications. It was introduced in the paper ["MobileNetV2: Inverted Residuals and Linear Bottlenecks"](https://arxiv.org/abs/1801.04381). </br>
+The MobileNetV2 model has been pre-trained on the ImageNet dataset and can be used for various tasks such as image classification, object detection, and semantic segmentation. It has achieved state-of-the-art performance on several benchmarks 1 for mobile and embedded vision applications.
+
+## How to Run
+
+To run the demo, make sure to build the project, activate the environment, and set the appropriate environment variables.
+For more information, refer [installation and build guide](https://docs.tenstorrent.com/tt-metalium/latest/get_started/get_started.html#install-and-build).
+
+To run the functional Mobilenetv2 model on a single-chip:
+```sh
+pytest --disable-warnings models/experimental/functional_mobilenetv2/test/test_ttnn_mobilenetv2.py
+```
+
+To run the functional Mobilenetv2 model on a single-chip:
+```sh
+pytest --disable-warnings models/experimental/functional_mobilenetv2/demo/demo.py
+```
+
+## Supported Hardware
+- N150
+
+## Other Details
+
+- Inputs by default are random data in test_ttnn_mobilenetv2 and images can be fed as input in demo.py.
+- The model weights will be automatically downloaded from Google Drive using wget implemented in weights_download.sh.
diff --git a/models/experimental/functional_mobilenetv2/demo/demo.py b/models/experimental/functional_mobilenetv2/demo/demo.py
new file mode 100644
index 000000000000..ce6b36f962a1
--- /dev/null
+++ b/models/experimental/functional_mobilenetv2/demo/demo.py
@@ -0,0 +1,101 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+
+import pytest
+import ttnn
+import torch
+
+from tests.ttnn.utils_for_testing import assert_with_pcc
+
+from models.experimental.functional_mobilenetv2.reference.mobilenetv2 import Mobilenetv2
+from models.experimental.functional_mobilenetv2.tt.model_preprocessing import (
+    create_mobilenetv2_input_tensors,
+    create_mobilenetv2_model_parameters,
+)
+from models.experimental.functional_mobilenetv2.tt import ttnn_mobilenetv2
+import os
+from models.utility_functions import (
+    skip_for_grayskull,
+)
+from PIL import Image
+from torchvision import transforms
+
+
+@pytest.mark.parametrize("device_params", [{"l1_small_size": 32768}], indirect=True)
+@skip_for_grayskull()
+def test_mobilenetv2_demo(device, reset_seeds):
+    if not os.path.exists("models/experimental/functional_mobilenetv2/mobilenet_v2-b0353104.pth"):
+        os.system(
+            "bash models/experimental/functional_mobilenetv2/weights_download.sh"
+        )  # execute the weights_download.sh file
+
+    state_dict = torch.load("models/experimental/functional_mobilenetv2/mobilenet_v2-b0353104.pth")
+    ds_state_dict = {k: v for k, v in state_dict.items()}
+    torch_model = Mobilenetv2()
+
+    new_state_dict = {}
+
+    for (name1, parameter1), (name2, parameter2) in zip(torch_model.state_dict().items(), ds_state_dict.items()):
+        if isinstance(parameter2, torch.FloatTensor):
+            new_state_dict[name1] = parameter2
+
+    torch_model.load_state_dict(new_state_dict)
+    torch_model.eval()
+
+    transform = transforms.Compose(
+        [
+            transforms.Resize(128),
+            transforms.CenterCrop(128),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        ]
+    )
+
+    img = Image.open("models/experimental/functional_mobilenetv2/demo/images/strawberry.jpg")
+
+    img_t = transform(img)
+    torch_input_tensor = torch.unsqueeze(img_t, 0)
+
+    # torch_input_tensor, ttnn_input_tensor = create_mobilenetv2_input_tensors()
+    torch_output_tensor = torch_model(torch_input_tensor)
+
+    parameters = create_mobilenetv2_model_parameters(torch_model, torch_input_tensor, device=device)
+
+    ttnn_input_tensor = torch.permute(torch_input_tensor, (0, 2, 3, 1))
+    ttnn_input_tensor = ttnn_input_tensor.reshape(
+        1,
+        1,
+        ttnn_input_tensor.shape[0] * ttnn_input_tensor.shape[1] * ttnn_input_tensor.shape[2],
+        ttnn_input_tensor.shape[3],
+    )
+    ttnn_input_tensor = ttnn.from_torch(ttnn_input_tensor, dtype=ttnn.bfloat16)
+
+    ttnn_model = ttnn_mobilenetv2.MobileNetV2(parameters, device, torch_model)
+    output_tensor = ttnn_model(device, ttnn_input_tensor)
+
+    #
+    # Tensor Postprocessing
+    #
+    output_tensor = ttnn.to_torch(output_tensor)
+    output_tensor = output_tensor.reshape(torch_output_tensor.shape)
+    output_tensor = output_tensor.to(torch_input_tensor.dtype)
+
+    with open("models/experimental/functional_mobilenetv2/demo/imagenet_classes.txt") as f:
+        classes = [line.strip() for line in f.readlines()]
+
+    # Get the predicted class index and probability
+    _, index = torch.max(output_tensor, 1)  # Get the index of the highest probability class
+    percentage = torch.nn.functional.softmax(output_tensor, dim=1)[0] * 100  # Calculate the class probabilities
+
+    # Print the predicted class and its probability
+    print("\033[1m" + f"Predicted class: {classes[index[0]]}")
+    print(
+        "\033[1m" + f"Probability: {percentage[index[0]].item():.2f}%"
+    )  # Format the probability with two decimal places
+
+    _, indices = torch.sort(output_tensor, descending=True)
+    [(classes[idx], percentage[idx].item()) for idx in indices[0][:5]]
+
+    assert_with_pcc(torch_output_tensor, output_tensor, pcc=0.95)
diff --git a/models/experimental/functional_mobilenetv2/demo/imagenet_classes.txt b/models/experimental/functional_mobilenetv2/demo/imagenet_classes.txt
new file mode 100644
index 000000000000..34c3324c3a3b
--- /dev/null
+++ b/models/experimental/functional_mobilenetv2/demo/imagenet_classes.txt
@@ -0,0 +1,1000 @@
+0, tench
+1, goldfish
+2, great_white_shark
+3, tiger_shark
+4, hammerhead
+5, electric_ray
+6, stingray
+7, cock
+8, hen
+9, ostrich
+10, brambling
+11, goldfinch
+12, house_finch
+13, junco
+14, indigo_bunting
+15, robin
+16, bulbul
+17, jay
+18, magpie
+19, chickadee
+20, water_ouzel
+21, kite
+22, bald_eagle
+23, vulture
+24, great_grey_owl
+25, European_fire_salamander
+26, common_newt
+27, eft
+28, spotted_salamander
+29, axolotl
+30, bullfrog
+31, tree_frog
+32, tailed_frog
+33, loggerhead
+34, leatherback_turtle
+35, mud_turtle
+36, terrapin
+37, box_turtle
+38, banded_gecko
+39, common_iguana
+40, American_chameleon
+41, whiptail
+42, agama
+43, frilled_lizard
+44, alligator_lizard
+45, Gila_monster
+46, green_lizard
+47, African_chameleon
+48, Komodo_dragon
+49, African_crocodile
+50, American_alligator
+51, triceratops
+52, thunder_snake
+53, ringneck_snake
+54, hognose_snake
+55, green_snake
+56, king_snake
+57, garter_snake
+58, water_snake
+59, vine_snake
+60, night_snake
+61, boa_constrictor
+62, rock_python
+63, Indian_cobra
+64, green_mamba
+65, sea_snake
+66, horned_viper
+67, diamondback
+68, sidewinder
+69, trilobite
+70, harvestman
+71, scorpion
+72, black_and_gold_garden_spider
+73, barn_spider
+74, garden_spider
+75, black_widow
+76, tarantula
+77, wolf_spider
+78, tick
+79, centipede
+80, black_grouse
+81, ptarmigan
+82, ruffed_grouse
+83, prairie_chicken
+84, peacock
+85, quail
+86, partridge
+87, African_grey
+88, macaw
+89, sulphur-crested_cockatoo
+90, lorikeet
+91, coucal
+92, bee_eater
+93, hornbill
+94, hummingbird
+95, jacamar
+96, toucan
+97, drake
+98, red-breasted_merganser
+99, goose
+100, black_swan
+101, tusker
+102, echidna
+103, platypus
+104, wallaby
+105, koala
+106, wombat
+107, jellyfish
+108, sea_anemone
+109, brain_coral
+110, flatworm
+111, nematode
+112, conch
+113, snail
+114, slug
+115, sea_slug
+116, chiton
+117, chambered_nautilus
+118, Dungeness_crab
+119, rock_crab
+120, fiddler_crab
+121, king_crab
+122, American_lobster
+123, spiny_lobster
+124, crayfish
+125, hermit_crab
+126, isopod
+127, white_stork
+128, black_stork
+129, spoonbill
+130, flamingo
+131, little_blue_heron
+132, American_egret
+133, bittern
+134, crane
+135, limpkin
+136, European_gallinule
+137, American_coot
+138, bustard
+139, ruddy_turnstone
+140, red-backed_sandpiper
+141, redshank
+142, dowitcher
+143, oystercatcher
+144, pelican
+145, king_penguin
+146, albatross
+147, grey_whale
+148, killer_whale
+149, dugong
+150, sea_lion
+151, Chihuahua
+152, Japanese_spaniel
+153, Maltese_dog
+154, Pekinese
+155, Shih-Tzu
+156, Blenheim_spaniel
+157, papillon
+158, toy_terrier
+159, Rhodesian_ridgeback
+160, Afghan_hound
+161, basset
+162, beagle
+163, bloodhound
+164, bluetick
+165, black-and-tan_coonhound
+166, Walker_hound
+167, English_foxhound
+168, redbone
+169, borzoi
+170, Irish_wolfhound
+171, Italian_greyhound
+172, whippet
+173, Ibizan_hound
+174, Norwegian_elkhound
+175, otterhound
+176, Saluki
+177, Scottish_deerhound
+178, Weimaraner
+179, Staffordshire_bullterrier
+180, American_Staffordshire_terrier
+181, Bedlington_terrier
+182, Border_terrier
+183, Kerry_blue_terrier
+184, Irish_terrier
+185, Norfolk_terrier
+186, Norwich_terrier
+187, Yorkshire_terrier
+188, wire-haired_fox_terrier
+189, Lakeland_terrier
+190, Sealyham_terrier
+191, Airedale
+192, cairn
+193, Australian_terrier
+194, Dandie_Dinmont
+195, Boston_bull
+196, miniature_schnauzer
+197, giant_schnauzer
+198, standard_schnauzer
+199, Scotch_terrier
+200, Tibetan_terrier
+201, silky_terrier
+202, soft-coated_wheaten_terrier
+203, West_Highland_white_terrier
+204, Lhasa
+205, flat-coated_retriever
+206, curly-coated_retriever
+207, golden_retriever
+208, Labrador_retriever
+209, Chesapeake_Bay_retriever
+210, German_short-haired_pointer
+211, vizsla
+212, English_setter
+213, Irish_setter
+214, Gordon_setter
+215, Brittany_spaniel
+216, clumber
+217, English_springer
+218, Welsh_springer_spaniel
+219, cocker_spaniel
+220, Sussex_spaniel
+221, Irish_water_spaniel
+222, kuvasz
+223, schipperke
+224, groenendael
+225, malinois
+226, briard
+227, kelpie
+228, komondor
+229, Old_English_sheepdog
+230, Shetland_sheepdog
+231, collie
+232, Border_collie
+233, Bouvier_des_Flandres
+234, Rottweiler
+235, German_shepherd
+236, Doberman
+237, miniature_pinscher
+238, Greater_Swiss_Mountain_dog
+239, Bernese_mountain_dog
+240, Appenzeller
+241, EntleBucher
+242, boxer
+243, bull_mastiff
+244, Tibetan_mastiff
+245, French_bulldog
+246, Great_Dane
+247, Saint_Bernard
+248, Eskimo_dog
+249, malamute
+250, Siberian_husky
+251, dalmatian
+252, affenpinscher
+253, basenji
+254, pug
+255, Leonberg
+256, Newfoundland
+257, Great_Pyrenees
+258, Samoyed
+259, Pomeranian
+260, chow
+261, keeshond
+262, Brabancon_griffon
+263, Pembroke
+264, Cardigan
+265, toy_poodle
+266, miniature_poodle
+267, standard_poodle
+268, Mexican_hairless
+269, timber_wolf
+270, white_wolf
+271, red_wolf
+272, coyote
+273, dingo
+274, dhole
+275, African_hunting_dog
+276, hyena
+277, red_fox
+278, kit_fox
+279, Arctic_fox
+280, grey_fox
+281, tabby
+282, tiger_cat
+283, Persian_cat
+284, Siamese_cat
+285, Egyptian_cat
+286, cougar
+287, lynx
+288, leopard
+289, snow_leopard
+290, jaguar
+291, lion
+292, tiger
+293, cheetah
+294, brown_bear
+295, American_black_bear
+296, ice_bear
+297, sloth_bear
+298, mongoose
+299, meerkat
+300, tiger_beetle
+301, ladybug
+302, ground_beetle
+303, long-horned_beetle
+304, leaf_beetle
+305, dung_beetle
+306, rhinoceros_beetle
+307, weevil
+308, fly
+309, bee
+310, ant
+311, grasshopper
+312, cricket
+313, walking_stick
+314, cockroach
+315, mantis
+316, cicada
+317, leafhopper
+318, lacewing
+319, dragonfly
+320, damselfly
+321, admiral
+322, ringlet
+323, monarch
+324, cabbage_butterfly
+325, sulphur_butterfly
+326, lycaenid
+327, starfish
+328, sea_urchin
+329, sea_cucumber
+330, wood_rabbit
+331, hare
+332, Angora
+333, hamster
+334, porcupine
+335, fox_squirrel
+336, marmot
+337, beaver
+338, guinea_pig
+339, sorrel
+340, zebra
+341, hog
+342, wild_boar
+343, warthog
+344, hippopotamus
+345, ox
+346, water_buffalo
+347, bison
+348, ram
+349, bighorn
+350, ibex
+351, hartebeest
+352, impala
+353, gazelle
+354, Arabian_camel
+355, llama
+356, weasel
+357, mink
+358, polecat
+359, black-footed_ferret
+360, otter
+361, skunk
+362, badger
+363, armadillo
+364, three-toed_sloth
+365, orangutan
+366, gorilla
+367, chimpanzee
+368, gibbon
+369, siamang
+370, guenon
+371, patas
+372, baboon
+373, macaque
+374, langur
+375, colobus
+376, proboscis_monkey
+377, marmoset
+378, capuchin
+379, howler_monkey
+380, titi
+381, spider_monkey
+382, squirrel_monkey
+383, Madagascar_cat
+384, indri
+385, Indian_elephant
+386, African_elephant
+387, lesser_panda
+388, giant_panda
+389, barracouta
+390, eel
+391, coho
+392, rock_beauty
+393, anemone_fish
+394, sturgeon
+395, gar
+396, lionfish
+397, puffer
+398, abacus
+399, abaya
+400, academic_gown
+401, accordion
+402, acoustic_guitar
+403, aircraft_carrier
+404, airliner
+405, airship
+406, altar
+407, ambulance
+408, amphibian
+409, analog_clock
+410, apiary
+411, apron
+412, ashcan
+413, assault_rifle
+414, backpack
+415, bakery
+416, balance_beam
+417, balloon
+418, ballpoint
+419, Band_Aid
+420, banjo
+421, bannister
+422, barbell
+423, barber_chair
+424, barbershop
+425, barn
+426, barometer
+427, barrel
+428, barrow
+429, baseball
+430, basketball
+431, bassinet
+432, bassoon
+433, bathing_cap
+434, bath_towel
+435, bathtub
+436, beach_wagon
+437, beacon
+438, beaker
+439, bearskin
+440, beer_bottle
+441, beer_glass
+442, bell_cote
+443, bib
+444, bicycle-built-for-two
+445, bikini
+446, binder
+447, binoculars
+448, birdhouse
+449, boathouse
+450, bobsled
+451, bolo_tie
+452, bonnet
+453, bookcase
+454, bookshop
+455, bottlecap
+456, bow
+457, bow_tie
+458, brass
+459, brassiere
+460, breakwater
+461, breastplate
+462, broom
+463, bucket
+464, buckle
+465, bulletproof_vest
+466, bullet_train
+467, butcher_shop
+468, cab
+469, caldron
+470, candle
+471, cannon
+472, canoe
+473, can_opener
+474, cardigan
+475, car_mirror
+476, carousel
+477, carpenter's_kit
+478, carton
+479, car_wheel
+480, cash_machine
+481, cassette
+482, cassette_player
+483, castle
+484, catamaran
+485, CD_player
+486, cello
+487, cellular_telephone
+488, chain
+489, chainlink_fence
+490, chain_mail
+491, chain_saw
+492, chest
+493, chiffonier
+494, chime
+495, china_cabinet
+496, Christmas_stocking
+497, church
+498, cinema
+499, cleaver
+500, cliff_dwelling
+501, cloak
+502, clog
+503, cocktail_shaker
+504, coffee_mug
+505, coffeepot
+506, coil
+507, combination_lock
+508, computer_keyboard
+509, confectionery
+510, container_ship
+511, convertible
+512, corkscrew
+513, cornet
+514, cowboy_boot
+515, cowboy_hat
+516, cradle
+517, crane
+518, crash_helmet
+519, crate
+520, crib
+521, Crock_Pot
+522, croquet_ball
+523, crutch
+524, cuirass
+525, dam
+526, desk
+527, desktop_computer
+528, dial_telephone
+529, diaper
+530, digital_clock
+531, digital_watch
+532, dining_table
+533, dishrag
+534, dishwasher
+535, disk_brake
+536, dock
+537, dogsled
+538, dome
+539, doormat
+540, drilling_platform
+541, drum
+542, drumstick
+543, dumbbell
+544, Dutch_oven
+545, electric_fan
+546, electric_guitar
+547, electric_locomotive
+548, entertainment_center
+549, envelope
+550, espresso_maker
+551, face_powder
+552, feather_boa
+553, file
+554, fireboat
+555, fire_engine
+556, fire_screen
+557, flagpole
+558, flute
+559, folding_chair
+560, football_helmet
+561, forklift
+562, fountain
+563, fountain_pen
+564, four-poster
+565, freight_car
+566, French_horn
+567, frying_pan
+568, fur_coat
+569, garbage_truck
+570, gasmask
+571, gas_pump
+572, goblet
+573, go-kart
+574, golf_ball
+575, golfcart
+576, gondola
+577, gong
+578, gown
+579, grand_piano
+580, greenhouse
+581, grille
+582, grocery_store
+583, guillotine
+584, hair_slide
+585, hair_spray
+586, half_track
+587, hammer
+588, hamper
+589, hand_blower
+590, hand-held_computer
+591, handkerchief
+592, hard_disc
+593, harmonica
+594, harp
+595, harvester
+596, hatchet
+597, holster
+598, home_theater
+599, honeycomb
+600, hook
+601, hoopskirt
+602, horizontal_bar
+603, horse_cart
+604, hourglass
+605, iPod
+606, iron
+607, jack-o'-lantern
+608, jean
+609, jeep
+610, jersey
+611, jigsaw_puzzle
+612, jinrikisha
+613, joystick
+614, kimono
+615, knee_pad
+616, knot
+617, lab_coat
+618, ladle
+619, lampshade
+620, laptop
+621, lawn_mower
+622, lens_cap
+623, letter_opener
+624, library
+625, lifeboat
+626, lighter
+627, limousine
+628, liner
+629, lipstick
+630, Loafer
+631, lotion
+632, loudspeaker
+633, loupe
+634, lumbermill
+635, magnetic_compass
+636, mailbag
+637, mailbox
+638, maillot
+639, maillot
+640, manhole_cover
+641, maraca
+642, marimba
+643, mask
+644, matchstick
+645, maypole
+646, maze
+647, measuring_cup
+648, medicine_chest
+649, megalith
+650, microphone
+651, microwave
+652, military_uniform
+653, milk_can
+654, minibus
+655, miniskirt
+656, minivan
+657, missile
+658, mitten
+659, mixing_bowl
+660, mobile_home
+661, Model_T
+662, modem
+663, monastery
+664, monitor
+665, moped
+666, mortar
+667, mortarboard
+668, mosque
+669, mosquito_net
+670, motor_scooter
+671, mountain_bike
+672, mountain_tent
+673, mouse
+674, mousetrap
+675, moving_van
+676, muzzle
+677, nail
+678, neck_brace
+679, necklace
+680, nipple
+681, notebook
+682, obelisk
+683, oboe
+684, ocarina
+685, odometer
+686, oil_filter
+687, organ
+688, oscilloscope
+689, overskirt
+690, oxcart
+691, oxygen_mask
+692, packet
+693, paddle
+694, paddlewheel
+695, padlock
+696, paintbrush
+697, pajama
+698, palace
+699, panpipe
+700, paper_towel
+701, parachute
+702, parallel_bars
+703, park_bench
+704, parking_meter
+705, passenger_car
+706, patio
+707, pay-phone
+708, pedestal
+709, pencil_box
+710, pencil_sharpener
+711, perfume
+712, Petri_dish
+713, photocopier
+714, pick
+715, pickelhaube
+716, picket_fence
+717, pickup
+718, pier
+719, piggy_bank
+720, pill_bottle
+721, pillow
+722, ping-pong_ball
+723, pinwheel
+724, pirate
+725, pitcher
+726, plane
+727, planetarium
+728, plastic_bag
+729, plate_rack
+730, plow
+731, plunger
+732, Polaroid_camera
+733, pole
+734, police_van
+735, poncho
+736, pool_table
+737, pop_bottle
+738, pot
+739, potter's_wheel
+740, power_drill
+741, prayer_rug
+742, printer
+743, prison
+744, projectile
+745, projector
+746, puck
+747, punching_bag
+748, purse
+749, quill
+750, quilt
+751, racer
+752, racket
+753, radiator
+754, radio
+755, radio_telescope
+756, rain_barrel
+757, recreational_vehicle
+758, reel
+759, reflex_camera
+760, refrigerator
+761, remote_control
+762, restaurant
+763, revolver
+764, rifle
+765, rocking_chair
+766, rotisserie
+767, rubber_eraser
+768, rugby_ball
+769, rule
+770, running_shoe
+771, safe
+772, safety_pin
+773, saltshaker
+774, sandal
+775, sarong
+776, sax
+777, scabbard
+778, scale
+779, school_bus
+780, schooner
+781, scoreboard
+782, screen
+783, screw
+784, screwdriver
+785, seat_belt
+786, sewing_machine
+787, shield
+788, shoe_shop
+789, shoji
+790, shopping_basket
+791, shopping_cart
+792, shovel
+793, shower_cap
+794, shower_curtain
+795, ski
+796, ski_mask
+797, sleeping_bag
+798, slide_rule
+799, sliding_door
+800, slot
+801, snorkel
+802, snowmobile
+803, snowplow
+804, soap_dispenser
+805, soccer_ball
+806, sock
+807, solar_dish
+808, sombrero
+809, soup_bowl
+810, space_bar
+811, space_heater
+812, space_shuttle
+813, spatula
+814, speedboat
+815, spider_web
+816, spindle
+817, sports_car
+818, spotlight
+819, stage
+820, steam_locomotive
+821, steel_arch_bridge
+822, steel_drum
+823, stethoscope
+824, stole
+825, stone_wall
+826, stopwatch
+827, stove
+828, strainer
+829, streetcar
+830, stretcher
+831, studio_couch
+832, stupa
+833, submarine
+834, suit
+835, sundial
+836, sunglass
+837, sunglasses
+838, sunscreen
+839, suspension_bridge
+840, swab
+841, sweatshirt
+842, swimming_trunks
+843, swing
+844, switch
+845, syringe
+846, table_lamp
+847, tank
+848, tape_player
+849, teapot
+850, teddy
+851, television
+852, tennis_ball
+853, thatch
+854, theater_curtain
+855, thimble
+856, thresher
+857, throne
+858, tile_roof
+859, toaster
+860, tobacco_shop
+861, toilet_seat
+862, torch
+863, totem_pole
+864, tow_truck
+865, toyshop
+866, tractor
+867, trailer_truck
+868, tray
+869, trench_coat
+870, tricycle
+871, trimaran
+872, tripod
+873, triumphal_arch
+874, trolleybus
+875, trombone
+876, tub
+877, turnstile
+878, typewriter_keyboard
+879, umbrella
+880, unicycle
+881, upright
+882, vacuum
+883, vase
+884, vault
+885, velvet
+886, vending_machine
+887, vestment
+888, viaduct
+889, violin
+890, volleyball
+891, waffle_iron
+892, wall_clock
+893, wallet
+894, wardrobe
+895, warplane
+896, washbasin
+897, washer
+898, water_bottle
+899, water_jug
+900, water_tower
+901, whiskey_jug
+902, whistle
+903, wig
+904, window_screen
+905, window_shade
+906, Windsor_tie
+907, wine_bottle
+908, wing
+909, wok
+910, wooden_spoon
+911, wool
+912, worm_fence
+913, wreck
+914, yawl
+915, yurt
+916, web_site
+917, comic_book
+918, crossword_puzzle
+919, street_sign
+920, traffic_light
+921, book_jacket
+922, menu
+923, plate
+924, guacamole
+925, consomme
+926, hot_pot
+927, trifle
+928, ice_cream
+929, ice_lolly
+930, French_loaf
+931, bagel
+932, pretzel
+933, cheeseburger
+934, hotdog
+935, mashed_potato
+936, head_cabbage
+937, broccoli
+938, cauliflower
+939, zucchini
+940, spaghetti_squash
+941, acorn_squash
+942, butternut_squash
+943, cucumber
+944, artichoke
+945, bell_pepper
+946, cardoon
+947, mushroom
+948, Granny_Smith
+949, strawberry
+950, orange
+951, lemon
+952, fig
+953, pineapple
+954, banana
+955, jackfruit
+956, custard_apple
+957, pomegranate
+958, hay
+959, carbonara
+960, chocolate_sauce
+961, dough
+962, meat_loaf
+963, pizza
+964, potpie
+965, burrito
+966, red_wine
+967, espresso
+968, cup
+969, eggnog
+970, alp
+971, bubble
+972, cliff
+973, coral_reef
+974, geyser
+975, lakeside
+976, promontory
+977, sandbar
+978, seashore
+979, valley
+980, volcano
+981, ballplayer
+982, groom
+983, scuba_diver
+984, rapeseed
+985, daisy
+986, yellow_lady's_slipper
+987, corn
+988, acorn
+989, hip
+990, buckeye
+991, coral_fungus
+992, agaric
+993, gyromitra
+994, stinkhorn
+995, earthstar
+996, hen-of-the-woods
+997, bolete
+998, ear
+999, toilet_tissue
diff --git a/models/experimental/functional_mobilenetv2/demo/images/strawberry.jpg b/models/experimental/functional_mobilenetv2/demo/images/strawberry.jpg
new file mode 100644
index 000000000000..2cefb972eb87
Binary files /dev/null and b/models/experimental/functional_mobilenetv2/demo/images/strawberry.jpg differ
diff --git a/models/experimental/functional_mobilenetv2/reference/mobilenetv2.py b/models/experimental/functional_mobilenetv2/reference/mobilenetv2.py
new file mode 100644
index 000000000000..f9bf6130f614
--- /dev/null
+++ b/models/experimental/functional_mobilenetv2/reference/mobilenetv2.py
@@ -0,0 +1,384 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+
+import torch
+import torch.nn as nn
+
+
+class Mobilenetv2(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.c1 = nn.Conv2d(3, 32, 3, 2, 1, bias=False)
+        self.b1 = nn.BatchNorm2d(32)
+        self.relu = nn.ReLU6(inplace=True)
+
+        self.c2 = nn.Conv2d(32, 32, 3, 1, 1, groups=32, bias=False)
+        self.b2 = nn.BatchNorm2d(32)
+
+        self.c3 = nn.Conv2d(32, 16, 1, 1, bias=False)
+        self.b3 = nn.BatchNorm2d(16)
+
+        self.c4 = nn.Conv2d(16, 96, 1, 1, bias=False)
+        self.b4 = nn.BatchNorm2d(96)
+
+        self.c5 = nn.Conv2d(96, 96, 3, 2, 1, groups=96, bias=False)
+        self.b5 = nn.BatchNorm2d(96)
+
+        self.c6 = nn.Conv2d(96, 24, 1, 1, bias=False)
+        self.b6 = nn.BatchNorm2d(24)
+
+        self.c7 = nn.Conv2d(24, 144, 1, 1, bias=False)
+        self.b7 = nn.BatchNorm2d(144)
+
+        self.c8 = nn.Conv2d(144, 144, 3, 1, 1, groups=144, bias=False)
+        self.b8 = nn.BatchNorm2d(144)
+
+        self.c9 = nn.Conv2d(144, 24, 1, 1, bias=False)
+        self.b9 = nn.BatchNorm2d(24)
+
+        self.c10 = nn.Conv2d(24, 144, 1, 1, bias=False)
+        self.b10 = nn.BatchNorm2d(144)
+
+        self.c11 = nn.Conv2d(144, 144, 3, 2, 1, groups=144, bias=False)
+        self.b11 = nn.BatchNorm2d(144)
+
+        self.c12 = nn.Conv2d(144, 32, 1, 1, bias=False)
+        self.b12 = nn.BatchNorm2d(32)
+
+        self.c13 = nn.Conv2d(32, 192, 1, 1, bias=False)
+        self.b13 = nn.BatchNorm2d(192)
+
+        self.c14 = nn.Conv2d(192, 192, 3, 1, 1, groups=192, bias=False)
+        self.b14 = nn.BatchNorm2d(192)
+
+        self.c15 = nn.Conv2d(192, 32, 1, 1, bias=False)
+        self.b15 = nn.BatchNorm2d(32)
+
+        self.c16 = nn.Conv2d(32, 192, 1, 1, bias=False)
+        self.b16 = nn.BatchNorm2d(192)
+
+        self.c17 = nn.Conv2d(192, 192, 3, 1, 1, groups=192, bias=False)
+        self.b17 = nn.BatchNorm2d(192)
+
+        self.c18 = nn.Conv2d(192, 32, 1, 1, bias=False)
+        self.b18 = nn.BatchNorm2d(32)
+
+        self.c19 = nn.Conv2d(32, 192, 1, 1, bias=False)
+        self.b19 = nn.BatchNorm2d(192)
+
+        self.c20 = nn.Conv2d(192, 192, 3, 2, 1, groups=192, bias=False)
+        self.b20 = nn.BatchNorm2d(192)
+
+        self.c21 = nn.Conv2d(192, 64, 1, 1, bias=False)
+        self.b21 = nn.BatchNorm2d(64)
+
+        self.c22 = nn.Conv2d(64, 384, 1, 1, bias=False)
+        self.b22 = nn.BatchNorm2d(384)
+
+        self.c23 = nn.Conv2d(384, 384, 3, 1, 1, groups=384, bias=False)
+        self.b23 = nn.BatchNorm2d(384)
+
+        self.c24 = nn.Conv2d(384, 64, 1, 1, bias=False)
+        self.b24 = nn.BatchNorm2d(64)
+
+        self.c25 = nn.Conv2d(64, 384, 1, 1, bias=False)
+        self.b25 = nn.BatchNorm2d(384)
+
+        self.c26 = nn.Conv2d(384, 384, 3, 1, 1, groups=384, bias=False)
+        self.b26 = nn.BatchNorm2d(384)
+
+        self.c27 = nn.Conv2d(384, 64, 1, 1, bias=False)
+        self.b27 = nn.BatchNorm2d(64)
+
+        self.c28 = nn.Conv2d(64, 384, 1, 1, bias=False)
+        self.b28 = nn.BatchNorm2d(384)
+
+        self.c29 = nn.Conv2d(384, 384, 3, 1, 1, groups=384, bias=False)
+        self.b29 = nn.BatchNorm2d(384)
+
+        self.c30 = nn.Conv2d(384, 64, 1, 1, bias=False)
+        self.b30 = nn.BatchNorm2d(64)
+
+        self.c31 = nn.Conv2d(64, 384, 1, 1, bias=False)
+        self.b31 = nn.BatchNorm2d(384)
+
+        self.c32 = nn.Conv2d(384, 384, 3, 1, 1, groups=384, bias=False)
+        self.b32 = nn.BatchNorm2d(384)
+
+        self.c33 = nn.Conv2d(384, 96, 1, 1, bias=False)
+        self.b33 = nn.BatchNorm2d(96)
+
+        self.c34 = nn.Conv2d(96, 576, 1, 1, bias=False)
+        self.b34 = nn.BatchNorm2d(576)
+
+        self.c35 = nn.Conv2d(576, 576, 3, 1, 1, groups=576, bias=False)
+        self.b35 = nn.BatchNorm2d(576)
+
+        self.c36 = nn.Conv2d(576, 96, 1, 1, bias=False)
+        self.b36 = nn.BatchNorm2d(96)
+
+        self.c37 = nn.Conv2d(96, 576, 1, 1, bias=False)
+        self.b37 = nn.BatchNorm2d(576)
+
+        self.c38 = nn.Conv2d(576, 576, 3, 1, 1, groups=576, bias=False)
+        self.b38 = nn.BatchNorm2d(576)
+
+        self.c39 = nn.Conv2d(576, 96, 1, 1, bias=False)
+        self.b39 = nn.BatchNorm2d(96)
+
+        self.c40 = nn.Conv2d(96, 576, 1, 1, bias=False)
+        self.b40 = nn.BatchNorm2d(576)
+
+        self.c41 = nn.Conv2d(576, 576, 3, 2, 1, groups=576, bias=False)
+        self.b41 = nn.BatchNorm2d(576)
+
+        self.c42 = nn.Conv2d(576, 160, 1, 1, bias=False)
+        self.b42 = nn.BatchNorm2d(160)
+
+        self.c43 = nn.Conv2d(160, 960, 1, 1, bias=False)
+        self.b43 = nn.BatchNorm2d(960)
+
+        self.c44 = nn.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False)
+        self.b44 = nn.BatchNorm2d(960)
+
+        self.c45 = nn.Conv2d(960, 160, 1, 1, bias=False)
+        self.b45 = nn.BatchNorm2d(160)
+
+        self.c46 = nn.Conv2d(160, 960, 1, 1, bias=False)
+        self.b46 = nn.BatchNorm2d(960)
+
+        self.c47 = nn.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False)
+        self.b47 = nn.BatchNorm2d(960)
+
+        self.c48 = nn.Conv2d(960, 160, 1, 1, bias=False)
+        self.b48 = nn.BatchNorm2d(160)
+
+        self.c49 = nn.Conv2d(160, 960, 1, 1, bias=False)
+        self.b49 = nn.BatchNorm2d(960)
+
+        self.c50 = nn.Conv2d(960, 960, 3, 1, 1, groups=960, bias=False)
+        self.b50 = nn.BatchNorm2d(960)
+
+        self.c51 = nn.Conv2d(960, 320, 1, 1, bias=False)
+        self.b51 = nn.BatchNorm2d(320)
+
+        self.c52 = nn.Conv2d(320, 1280, 1, 1, bias=False)
+        self.b52 = nn.BatchNorm2d(1280)
+
+        self.l1 = nn.Linear(in_features=1280, out_features=1000)
+
+    def forward(self, input: torch.Tensor):
+        x1 = self.c1(input)
+        x1_b = self.b1(x1)
+        x1_m = self.relu(x1_b)
+
+        x2 = self.c2(x1_m)
+        x2_b = self.b2(x2)
+        x2_m = self.relu(x2_b)
+
+        x3 = self.c3(x2_m)
+        x3_b = self.b3(x3)
+
+        x4 = self.c4(x3_b)
+        x4_b = self.b4(x4)
+        x4_m = self.relu(x4_b)
+
+        x5 = self.c5(x4_m)
+        x5_b = self.b5(x5)
+        x5_m = self.relu(x5_b)
+
+        x6 = self.c6(x5_m)
+        x6_b = self.b6(x6)
+
+        x7 = self.c7(x6_b)
+        x7_b = self.b7(x7)
+        x7_m = self.relu(x7_b)
+
+        x8 = self.c8(x7_m)
+        x8_b = self.b8(x8)
+        x8_m = self.relu(x8_b)
+
+        x9 = self.c9(x8_m)
+        x9_b = self.b9(x9)
+        a1 = x9_b + x6_b
+        x10 = self.c10(a1)
+        x10_b = self.b10(x10)
+        x10_m = self.relu(x10_b)
+
+        x11 = self.c11(x10_m)
+        x11_b = self.b11(x11)
+        x11_m = self.relu(x11_b)
+
+        x12 = self.c12(x11_m)
+        x12_b = self.b12(x12)
+
+        x13 = self.c13(x12_b)
+        x13_b = self.b13(x13)
+        x13_m = self.relu(x13_b)
+
+        x14 = self.c14(x13_m)
+        x14_b = self.b14(x14)
+        x14_m = self.relu(x14_b)
+
+        x15 = self.c15(x14_m)
+        x15_b = self.b15(x15)
+
+        a2 = x15_b + x12_b
+
+        x16 = self.c16(a2)
+        x16_b = self.b16(x16)
+        x16_m = self.relu(x16_b)
+
+        x17 = self.c17(x16_m)
+        x17_b = self.b17(x17)
+        x17_m = self.relu(x17_b)
+
+        x18 = self.c18(x17_m)
+        x18_b = self.b18(x18)
+
+        a3 = a2 + x18_b
+
+        x19 = self.c19(a3)
+        x19_b = self.b19(x19)
+        x19_m = self.relu(x19_b)
+
+        x20 = self.c20(x19_m)
+        x20_b = self.b20(x20)
+        x20_m = self.relu(x20_b)
+
+        x21 = self.c21(x20_m)
+        x21_b = self.b21(x21)
+
+        x22 = self.c22(x21_b)
+        x22_b = self.b22(x22)
+        x22_m = self.relu(x22_b)
+
+        x23 = self.c23(x22_m)
+        x23_b = self.b23(x23)
+        x23_m = self.relu(x23_b)
+
+        x24 = self.c24(x23_m)
+        x24_b = self.b24(x24)
+
+        a4 = x21_b + x24_b
+
+        x25 = self.c25(a4)
+        x25_b = self.b25(x25)
+        x25_m = self.relu(x25_b)
+
+        x26 = self.c26(x25_m)
+        x26_b = self.b26(x26)
+        x26_m = self.relu(x26_b)
+
+        x27 = self.c27(x26_m)
+        x27_b = self.b27(x27)
+
+        a5 = a4 + x27_b
+
+        x28 = self.c28(a5)
+        x28_b = self.b28(x28)
+        x28_m = self.relu(x28_b)
+
+        x29 = self.c29(x28_m)
+        x29_b = self.b29(x29)
+        x29_m = self.relu(x29_b)
+
+        x30 = self.c30(x29_m)
+        x30_b = self.b30(x30)
+
+        a6 = a5 + x30_b
+
+        x31 = self.c31(a6)
+        x31_b = self.b31(x31)
+        x31_m = self.relu(x31_b)
+
+        x32 = self.c32(x31_m)
+        x32_b = self.b32(x32)
+        x32_m = self.relu(x32_b)
+
+        x33 = self.c33(x32_m)
+        x33_b = self.b33(x33)
+
+        x34 = self.c34(x33_b)
+        x34_b = self.b34(x34)
+        x34_m = self.relu(x34_b)
+
+        x35 = self.c35(x34_m)
+        x35_b = self.b35(x35)
+        x35_m = self.relu(x35_b)
+
+        x36 = self.c36(x35_m)
+        x36_b = self.b36(x36)
+
+        a7 = x33_b + x36_b
+
+        x37 = self.c37(a7)
+        x37_b = self.b37(x37)
+        x37_m = self.relu(x37_b)
+
+        x38 = self.c38(x37_m)
+        x38_b = self.b38(x38)
+        x38_m = self.relu(x38_b)
+
+        x39 = self.c39(x38_m)
+        x39_b = self.b39(x39)
+
+        a8 = a7 + x39_b
+
+        x40 = self.c40(a8)
+        x40_b = self.b40(x40)
+        x40_m = self.relu(x40_b)
+
+        x41 = self.c41(x40_m)
+        x41_b = self.b41(x41)
+        x41_m = self.relu(x41_b)
+
+        x42 = self.c42(x41_m)
+        x42_b = self.b42(x42)
+
+        x43 = self.c43(x42_b)
+        x43_b = self.b43(x43)
+        x43_m = self.relu(x43_b)
+
+        x44 = self.c44(x43_m)
+        x44_b = self.b44(x44)
+        x44_m = self.relu(x44_b)
+
+        x45 = self.c45(x44_m)
+        x45_b = self.b45(x45)
+
+        a9 = x45_b + x42_b
+
+        x46 = self.c46(a9)
+        x46_b = self.b46(x46)
+        x46_m = self.relu(x46_b)
+
+        x47 = self.c47(x46_m)
+        x47_b = self.b47(x47)
+        x47_m = self.relu(x47_b)
+
+        x48 = self.c48(x47_m)
+        x48_b = self.b48(x48)
+
+        a10 = a9 + x48_b
+
+        x49 = self.c49(a10)
+        x49_b = self.b49(x49)
+        x49_m = self.relu(x49_b)
+
+        x50 = self.c50(x49_m)
+        x50_b = self.b50(x50)
+        x50_m = self.relu(x50_b)
+
+        x51 = self.c51(x50_m)
+        x51_b = self.b51(x51)
+
+        x52 = self.c52(x51_b)
+        x52_b = self.b52(x52)
+        x52_m = self.relu(x52_b)
+        x = nn.functional.adaptive_avg_pool2d(x52_m, (1, 1))
+        x = torch.flatten(x, 1)
+        x53 = self.l1(x)
+        return x53
diff --git a/models/experimental/functional_mobilenetv2/test/test_ttnn_mobilenetv2.py b/models/experimental/functional_mobilenetv2/test/test_ttnn_mobilenetv2.py
new file mode 100644
index 000000000000..3d271b6f979c
--- /dev/null
+++ b/models/experimental/functional_mobilenetv2/test/test_ttnn_mobilenetv2.py
@@ -0,0 +1,58 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+
+import pytest
+import ttnn
+import torch
+
+from tests.ttnn.utils_for_testing import assert_with_pcc
+
+from models.experimental.functional_mobilenetv2.reference.mobilenetv2 import Mobilenetv2
+from models.experimental.functional_mobilenetv2.tt.model_preprocessing import (
+    create_mobilenetv2_input_tensors,
+    create_mobilenetv2_model_parameters,
+)
+from models.experimental.functional_mobilenetv2.tt import ttnn_mobilenetv2
+import os
+from models.utility_functions import (
+    skip_for_grayskull,
+)
+
+
+@pytest.mark.parametrize("device_params", [{"l1_small_size": 32768}], indirect=True)
+@skip_for_grayskull()
+def test_mobilenetv2(device, reset_seeds):
+    if not os.path.exists("models/experimental/functional_mobilenetv2/mobilenet_v2-b0353104.pth"):
+        os.system(
+            "bash models/experimental/functional_mobilenetv2/weights_download.sh"
+        )  # execute the weights_download.sh file
+
+    state_dict = torch.load("models/experimental/functional_mobilenetv2/mobilenet_v2-b0353104.pth")
+    ds_state_dict = {k: v for k, v in state_dict.items()}
+    torch_model = Mobilenetv2()
+
+    new_state_dict = {}
+
+    for (name1, parameter1), (name2, parameter2) in zip(torch_model.state_dict().items(), ds_state_dict.items()):
+        if isinstance(parameter2, torch.FloatTensor):
+            new_state_dict[name1] = parameter2
+
+    torch_model.load_state_dict(new_state_dict)
+    torch_model.eval()
+    torch_input_tensor, ttnn_input_tensor = create_mobilenetv2_input_tensors()
+    torch_output_tensor = torch_model(torch_input_tensor)
+
+    parameters = create_mobilenetv2_model_parameters(torch_model, torch_input_tensor, device=device)
+
+    ttnn_model = ttnn_mobilenetv2.MobileNetV2(parameters, device, torch_model)
+    output_tensor = ttnn_model(device, ttnn_input_tensor)
+
+    #
+    # Tensor Postprocessing
+    #
+    output_tensor = ttnn.to_torch(output_tensor)
+    output_tensor = output_tensor.reshape(torch_output_tensor.shape)
+    output_tensor = output_tensor.to(torch_input_tensor.dtype)
+    assert_with_pcc(torch_output_tensor, output_tensor, pcc=0.95)
diff --git a/models/experimental/functional_mobilenetv2/tt/model_preprocessing.py b/models/experimental/functional_mobilenetv2/tt/model_preprocessing.py
new file mode 100644
index 000000000000..a21a3d2f45f2
--- /dev/null
+++ b/models/experimental/functional_mobilenetv2/tt/model_preprocessing.py
@@ -0,0 +1,36 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+import torch
+import ttnn
+
+from models.experimental.functional_mobilenetv2.reference.mobilenetv2 import Mobilenetv2
+from ttnn.model_preprocessing import infer_ttnn_module_args
+
+
+def create_mobilenetv2_input_tensors(batch=1, input_channels=3, input_height=128, input_width=128):
+    torch_input_tensor = torch.randn(batch, input_channels, input_height, input_width)
+    ttnn_input_tensor = torch.permute(torch_input_tensor, (0, 2, 3, 1))
+    ttnn_input_tensor = ttnn_input_tensor.reshape(
+        1,
+        1,
+        ttnn_input_tensor.shape[0] * ttnn_input_tensor.shape[1] * ttnn_input_tensor.shape[2],
+        ttnn_input_tensor.shape[3],
+    )
+    ttnn_input_tensor = ttnn.from_torch(ttnn_input_tensor, dtype=ttnn.bfloat16)
+
+    return torch_input_tensor, ttnn_input_tensor
+
+
+def create_mobilenetv2_model_parameters(model: Mobilenetv2, input_tensor, device):
+    parameters = infer_ttnn_module_args(model=model, run_model=lambda model: model(input_tensor), device=None)
+    assert parameters is not None
+    for key in parameters.keys():
+        parameters[key].module = getattr(model, key)
+
+    parameters["l1"] = {}
+    parameters["l1"]["weight"] = model.l1.weight
+    parameters["l1"]["bias"] = model.l1.bias
+
+    return parameters
diff --git a/models/experimental/functional_mobilenetv2/tt/ttnn_mobilenetv2.py b/models/experimental/functional_mobilenetv2/tt/ttnn_mobilenetv2.py
new file mode 100644
index 000000000000..7f18ddac0fa0
--- /dev/null
+++ b/models/experimental/functional_mobilenetv2/tt/ttnn_mobilenetv2.py
@@ -0,0 +1,464 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+import ttnn
+import torch
+
+from ttnn.model_preprocessing import ParameterDict
+
+from torch import nn
+from ttnn.model_preprocessing import preprocess_linear_weight, preprocess_linear_bias
+
+
+class MobileNetV2Conv2D:
+    def fold_batch_norm2d_into_conv2d(self, conv, bn):
+        if not bn.track_running_stats:
+            raise RuntimeError("BatchNorm2d must have track_running_stats=True to be folded into Conv2d")
+        weight = conv.weight
+        running_mean = bn.running_mean
+        running_var = bn.running_var
+        eps = bn.eps
+        scale = bn.weight
+        shift = bn.bias
+        weight = weight * (scale / torch.sqrt(running_var + eps))[:, None, None, None]
+        bias = shift - running_mean * (scale / torch.sqrt(running_var + eps))
+        return weight, bias
+
+    def __init__(
+        self,
+        conv,
+        bn=None,
+        device=None,
+        cache={},
+        activation="",
+        activation_dtype=ttnn.bfloat8_b,
+        weights_dtype=ttnn.bfloat8_b,
+        use_1d_systolic_array=True,
+        shard_layout=ttnn.TensorMemoryLayout.HEIGHT_SHARDED,
+    ):
+        self.device = device
+        self.batch_size = conv.batch_size
+        self.input_height = conv.input_height
+        self.input_width = conv.input_width
+        self.in_channels = conv.in_channels
+        self.out_channels = conv.out_channels
+        self.kernel_size = conv.kernel_size
+        self.padding = conv.padding
+        self.stride = conv.stride
+        self.groups = conv.groups
+        self.use_1d_systolic_array = use_1d_systolic_array
+        self.deallocate_activation = True
+        self.cache = cache
+
+        self.conv_config = ttnn.Conv2dConfig(
+            dtype=activation_dtype,
+            weights_dtype=weights_dtype,
+            math_fidelity=ttnn.MathFidelity.LoFi,
+            shard_layout=shard_layout,
+            deallocate_activation=self.deallocate_activation,
+            fp32_dest_acc_enabled=True,
+            packer_l1_accum_enabled=False,
+            enable_act_double_buffer=False,
+            enable_split_reader=False,
+            enable_subblock_padding=False,
+            reshard_if_not_optimal=True if self.use_1d_systolic_array else False,
+            activation=activation,
+        )
+        config_override = conv.conv_blocking_and_parallelization_config_override
+        if config_override and "act_block_h" in config_override:
+            self.conv_config.act_block_h_override = config_override["act_block_h"]
+
+        if bn is not None:
+            weight, bias = self.fold_batch_norm2d_into_conv2d(conv.module, bn.module)
+        else:
+            weight, bias = conv.module.weight, conv.module.bias
+
+        weight = weight
+        bias = torch.reshape(bias, (1, 1, 1, -1))
+        self.weight = ttnn.from_torch(weight, dtype=ttnn.float32)
+        self.bias = ttnn.from_torch(bias, dtype=ttnn.float32)
+
+    def __call__(self, x):
+        x, output_height, output_width, self.weight, self.bias = ttnn.conv2d(
+            input_tensor=x,
+            weight_tensor=self.weight,
+            bias_tensor=self.bias,
+            device=self.device,
+            in_channels=self.in_channels,
+            out_channels=self.out_channels,
+            input_height=self.input_height,
+            input_width=self.input_width,
+            batch_size=self.batch_size,
+            kernel_size=self.kernel_size,
+            stride=self.stride,
+            padding=self.padding,
+            conv_config=self.conv_config,
+            conv_op_cache=self.cache,
+            groups=self.groups,
+        )
+        return x
+
+
+class MobileNetV2:
+    def input_preprocessor(self, tensor, n, c, h, w):
+        tensor = ttnn.to_torch(tensor).to(torch.float32)
+        tensor = torch.reshape(tensor, (n, h, w, c))
+        tensor = torch.permute(tensor, (0, 3, 1, 2))
+        return tensor
+
+    def __init__(self, parameters: ParameterDict, device, model) -> None:
+        self.device = device
+        self.model = model
+        self.parameters = parameters
+
+        self.c1 = MobileNetV2Conv2D(parameters.c1, parameters.b1, device)
+        self.c2 = MobileNetV2Conv2D(parameters.c2, parameters.b2, device)
+
+        self.c3 = MobileNetV2Conv2D(parameters.c3, parameters.b3, device)
+
+        self.c4 = MobileNetV2Conv2D(parameters.c4, parameters.b4, device)
+
+        self.c5 = MobileNetV2Conv2D(parameters.c5, parameters.b5, device)
+
+        self.c6 = MobileNetV2Conv2D(parameters.c6, parameters.b6, device)
+
+        self.c7 = MobileNetV2Conv2D(parameters.c7, parameters.b7, device)
+
+        self.c8 = MobileNetV2Conv2D(parameters.c8, parameters.b8, device)
+
+        self.c9 = MobileNetV2Conv2D(parameters.c9, parameters.b9, device)
+
+        self.c10 = MobileNetV2Conv2D(parameters.c10, parameters.b10, device)
+
+        self.c11 = MobileNetV2Conv2D(parameters.c11, parameters.b11, device)
+
+        self.c12 = MobileNetV2Conv2D(parameters.c12, parameters.b12, device)
+
+        self.c13 = MobileNetV2Conv2D(parameters.c13, parameters.b13, device)
+        self.c14 = MobileNetV2Conv2D(parameters.c14, parameters.b14, device)
+        self.c15 = MobileNetV2Conv2D(parameters.c15, parameters.b15, device)
+        self.c16 = MobileNetV2Conv2D(parameters.c16, parameters.b16, device)
+        self.c17 = MobileNetV2Conv2D(parameters.c17, parameters.b17, device)
+        self.c18 = MobileNetV2Conv2D(parameters.c18, parameters.b18, device)
+        self.c19 = MobileNetV2Conv2D(parameters.c19, parameters.b19, device)
+        self.c20 = MobileNetV2Conv2D(parameters.c20, parameters.b20, device)
+        self.c21 = MobileNetV2Conv2D(parameters.c21, parameters.b21, device)
+        self.c22 = MobileNetV2Conv2D(parameters.c22, parameters.b22, device)
+        self.c23 = MobileNetV2Conv2D(parameters.c23, parameters.b23, device)
+        self.c24 = MobileNetV2Conv2D(parameters.c24, parameters.b24, device)
+        self.c25 = MobileNetV2Conv2D(parameters.c25, parameters.b25, device)
+        self.c26 = MobileNetV2Conv2D(parameters.c26, parameters.b26, device)
+        self.c27 = MobileNetV2Conv2D(parameters.c27, parameters.b27, device)
+        self.c28 = MobileNetV2Conv2D(parameters.c28, parameters.b28, device)
+        self.c29 = MobileNetV2Conv2D(parameters.c29, parameters.b29, device)
+        self.c30 = MobileNetV2Conv2D(parameters.c30, parameters.b30, device)
+        self.c31 = MobileNetV2Conv2D(parameters.c31, parameters.b31, device)
+        self.c32 = MobileNetV2Conv2D(parameters.c32, parameters.b32, device)
+        self.c33 = MobileNetV2Conv2D(parameters.c33, parameters.b33, device)
+        self.c34 = MobileNetV2Conv2D(parameters.c34, parameters.b34, device)
+
+        self.c35 = MobileNetV2Conv2D(
+            parameters.c35, parameters.b35, device, shard_layout=ttnn.TensorMemoryLayout.WIDTH_SHARDED
+        )
+
+        self.c36 = MobileNetV2Conv2D(parameters.c36, parameters.b36, device)
+        self.c37 = MobileNetV2Conv2D(parameters.c37, parameters.b37, device)
+
+        self.c38 = MobileNetV2Conv2D(
+            parameters.c38, parameters.b38, device, shard_layout=ttnn.TensorMemoryLayout.WIDTH_SHARDED
+        )
+
+        self.c39 = MobileNetV2Conv2D(parameters.c39, parameters.b39, device)
+        self.c40 = MobileNetV2Conv2D(parameters.c40, parameters.b40, device)
+
+        self.c41 = MobileNetV2Conv2D(
+            parameters.c41, parameters.b41, device, shard_layout=ttnn.TensorMemoryLayout.WIDTH_SHARDED
+        )
+
+        self.c42 = MobileNetV2Conv2D(parameters.c42, parameters.b42, device)
+        self.c43 = MobileNetV2Conv2D(parameters.c43, parameters.b43, device)
+
+        self.c44 = MobileNetV2Conv2D(
+            parameters.c44, parameters.b44, device, shard_layout=ttnn.TensorMemoryLayout.WIDTH_SHARDED
+        )
+
+        self.c45 = MobileNetV2Conv2D(parameters.c45, parameters.b45, device)
+        self.c46 = MobileNetV2Conv2D(parameters.c46, parameters.b46, device)
+
+        self.c47 = MobileNetV2Conv2D(
+            parameters.c47, parameters.b47, device, shard_layout=ttnn.TensorMemoryLayout.WIDTH_SHARDED
+        )
+
+        self.c48 = MobileNetV2Conv2D(parameters.c48, parameters.b48, device)
+        self.c49 = MobileNetV2Conv2D(parameters.c49, parameters.b49, device)
+
+        self.c50 = MobileNetV2Conv2D(
+            parameters.c50, parameters.b50, device, shard_layout=ttnn.TensorMemoryLayout.WIDTH_SHARDED
+        )
+
+        self.c51 = MobileNetV2Conv2D(parameters.c51, parameters.b51, device)
+        self.c52 = MobileNetV2Conv2D(parameters.c52, parameters.b52, device)
+
+        self.l1_weight = parameters.l1["weight"]
+        self.l1_bias = parameters.l1["bias"]
+
+    def __call__(
+        self,
+        device,
+        x,
+    ):
+        output_tensor = self.c1(x)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c2(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c3(output_tensor)
+
+        output_tensor = self.c4(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c5(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c6(output_tensor)
+        output_tensor_c6 = output_tensor
+
+        if output_tensor_c6.is_sharded():
+            output_tensor_c6 = ttnn.sharded_to_interleaved(output_tensor_c6, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = self.c7(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c8(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c9(output_tensor)
+
+        if output_tensor.is_sharded():
+            output_tensor = ttnn.sharded_to_interleaved(output_tensor, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = ttnn.add(output_tensor_c6, output_tensor)
+
+        output_tensor = self.c10(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c11(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c12(output_tensor)
+        output_tensor_c12 = output_tensor
+
+        if output_tensor_c12.is_sharded():
+            output_tensor_c12 = ttnn.sharded_to_interleaved(output_tensor_c12, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = self.c13(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c14(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c15(output_tensor)
+        output_tensor_c15 = output_tensor
+
+        if output_tensor_c15.is_sharded():
+            output_tensor_c15 = ttnn.sharded_to_interleaved(output_tensor_c15, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = output_tensor_c15 + output_tensor_c12
+        output_tensor_a2 = output_tensor
+
+        if output_tensor_a2.is_sharded():
+            output_tensor_a2 = ttnn.sharded_to_interleaved(output_tensor_a2, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = self.c16(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c17(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c18(output_tensor)
+
+        if output_tensor.is_sharded():
+            output_tensor = ttnn.sharded_to_interleaved(output_tensor, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = output_tensor_a2 + output_tensor
+
+        output_tensor = self.c19(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c20(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c21(output_tensor)
+
+        output_tensor_c21 = output_tensor
+        if output_tensor_c21.is_sharded():
+            output_tensor_c21 = ttnn.sharded_to_interleaved(output_tensor_c21, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = self.c22(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c23(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c24(output_tensor)
+
+        if output_tensor.is_sharded():
+            output_tensor = ttnn.sharded_to_interleaved(output_tensor, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = output_tensor_c21 + output_tensor
+
+        output_tensor_a4 = output_tensor
+
+        if output_tensor_a4.is_sharded():
+            output_tensor_a4 = ttnn.sharded_to_interleaved(output_tensor_a4, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = self.c25(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c26(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c27(output_tensor)
+
+        if output_tensor.is_sharded():
+            output_tensor = ttnn.sharded_to_interleaved(output_tensor, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = output_tensor_a4 + output_tensor
+        output_tensor_a5 = output_tensor
+        if output_tensor_a5.is_sharded():
+            output_tensor_a5 = ttnn.sharded_to_interleaved(output_tensor_a5, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = self.c28(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c29(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c30(output_tensor)
+
+        if output_tensor.is_sharded():
+            output_tensor = ttnn.sharded_to_interleaved(output_tensor, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = ttnn.add(output_tensor_a5, output_tensor)
+
+        output_tensor = self.c31(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c32(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c33(output_tensor)
+
+        output_tensor_c33 = output_tensor
+        if output_tensor_c33.is_sharded():
+            output_tensor_c33 = ttnn.sharded_to_interleaved(output_tensor_c33, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = self.c34(output_tensor_c33)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c35(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c36(output_tensor)
+
+        if output_tensor.is_sharded():
+            output_tensor = ttnn.sharded_to_interleaved(output_tensor, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = output_tensor_c33 + output_tensor
+
+        output_tensor_a7 = output_tensor
+
+        if output_tensor_a7.is_sharded():
+            output_tensor_a7 = ttnn.sharded_to_interleaved(output_tensor_a7, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = self.c37(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c38(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c39(output_tensor)
+
+        if output_tensor.is_sharded():
+            output_tensor = ttnn.sharded_to_interleaved(output_tensor, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = output_tensor_a7 + output_tensor
+
+        output_tensor = self.c40(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c41(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c42(output_tensor)
+
+        output_tensor_c42 = output_tensor
+        if output_tensor_c42.is_sharded():
+            output_tensor_c42 = ttnn.sharded_to_interleaved(output_tensor_c42, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = self.c43(output_tensor_c42)
+        output_tensor = ttnn.relu6(output_tensor)
+        output_tensor = ttnn.to_layout(output_tensor, ttnn.ROW_MAJOR_LAYOUT)
+
+        output_tensor = self.c44(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c45(output_tensor)
+
+        if output_tensor.is_sharded():
+            output_tensor = ttnn.sharded_to_interleaved(output_tensor, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = output_tensor_c42 + output_tensor
+        output_tensor_a9 = output_tensor
+
+        if output_tensor_a9.is_sharded():
+            output_tensor_a9 = ttnn.sharded_to_interleaved(output_tensor_a9, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = self.c46(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = ttnn.to_layout(output_tensor, ttnn.ROW_MAJOR_LAYOUT)
+        output_tensor = self.c47(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c48(output_tensor)
+
+        if output_tensor.is_sharded():
+            output_tensor = ttnn.sharded_to_interleaved(output_tensor, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = output_tensor + output_tensor_a9
+
+        output_tensor = self.c49(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = ttnn.to_layout(output_tensor, ttnn.ROW_MAJOR_LAYOUT)
+        output_tensor = self.c50(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        output_tensor = self.c51(output_tensor)
+
+        output_tensor = self.c52(output_tensor)
+        output_tensor = ttnn.relu6(output_tensor)
+
+        if output_tensor.is_sharded():
+            output_tensor = ttnn.sharded_to_interleaved(output_tensor, ttnn.L1_MEMORY_CONFIG)
+
+        output_tensor = ttnn.global_avg_pool2d(output_tensor)
+
+        output_tensor = self.input_preprocessor(output_tensor, 1, 1280, 1, 1)
+
+        output_tensor = torch.flatten(output_tensor, 1)
+
+        output_tensor = ttnn.from_torch(output_tensor, dtype=ttnn.bfloat16, layout=ttnn.TILE_LAYOUT, device=device)
+        output_tensor = ttnn.to_memory_config(output_tensor, ttnn.L1_MEMORY_CONFIG)
+
+        self.l1_weight = preprocess_linear_weight(self.l1_weight, dtype=ttnn.bfloat16)
+        self.l1_bias = preprocess_linear_bias(self.l1_bias, dtype=ttnn.bfloat16)
+        self.l1_weight = ttnn.to_device(self.l1_weight, device)
+        self.l1_bias = ttnn.to_device(self.l1_bias, device)
+
+        output_tensor = ttnn.linear(output_tensor, self.l1_weight, bias=self.l1_bias)
+
+        return ttnn.from_device(output_tensor)
diff --git a/models/experimental/functional_mobilenetv2/weights_download.sh b/models/experimental/functional_mobilenetv2/weights_download.sh
new file mode 100644
index 000000000000..89dbc939007e
--- /dev/null
+++ b/models/experimental/functional_mobilenetv2/weights_download.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+
+# Output filename
+OUTPUT="models/experimental/functional_mobilenetv2/mobilenet_v2-b0353104.pth"
+
+# Create output directory if it doesn't exist
+mkdir -p "$(dirname "$OUTPUT")"
+
+# Download the file using wget
+if wget "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" -O "${OUTPUT}"; then
+    echo "File downloaded successfully: ${OUTPUT}"
+else
+    echo "Error downloading the file."
+    exit 1
+fi
diff --git a/tests/nightly/single_card/functional_mobilenetv2/experimental/functional_mobilenetv2/test/test_ttnn_mobilenetv2.py b/tests/nightly/single_card/functional_mobilenetv2/experimental/functional_mobilenetv2/test/test_ttnn_mobilenetv2.py
new file mode 120000
index 000000000000..c1a01b75951e
--- /dev/null
+++ b/tests/nightly/single_card/functional_mobilenetv2/experimental/functional_mobilenetv2/test/test_ttnn_mobilenetv2.py
@@ -0,0 +1 @@
+../../../../../../../models/experimental/functional_mobilenetv2/test/test_ttnn_mobilenetv2.py
\ No newline at end of file
diff --git a/tests/scripts/single_card/nightly/run_common_models.sh b/tests/scripts/single_card/nightly/run_common_models.sh
index 9c057b224827..1b2afd0f8bc1 100755
--- a/tests/scripts/single_card/nightly/run_common_models.sh
+++ b/tests/scripts/single_card/nightly/run_common_models.sh
@@ -15,3 +15,4 @@ env pytest -n auto tests/nightly/single_card/common_models/ ; fail+=$?
 if [[ $fail -ne 0 ]]; then
   exit 1
 fi
+env pytest models/experimental/functional_mobilenetv2/test/test_ttnn_mobilenetv2.py