# Analyze the fine tuning result **URL:** \ **Category:** Models\ **Created:** [February 18, 2025, 4:27am UTC](https://discuss.huggingface.co/t/analyze-the-fine-tuning-result/141508 "2025-02-18T04:27:21Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![taylorzhang55](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/taylorzhang55/32/41396_2.png) [@taylorzhang55](https://discuss.huggingface.co/u/taylorzhang55)\ **Post date:** [February 18, 2025, 4:27am UTC](https://discuss.huggingface.co/t/analyze-the-fine-tuning-result/141508/1 "2025-02-18T04:27:21Z")
Hi, I’m new to the fine tuning task. I’ve prepared over 500 examples for my training, and run two fine tune jobs for gpt-4omini. Can someone help me analyze the following results and suggest a better fine tune hyperparameter? —Version 1 Result— Hyperparamter used: Batch size = 32, Learning rate multiplier = 2, epochs = 5 step train\_loss train\_mean\_token\_accuracy valid\_loss valid\_mean\_token\_accuracy 1 1.223983883857727 0.7312172651290894 0.9252833432474432 0.799030122167304 2 1.1674647331237793 0.7475640177726746 0.5941609295564302 0.8627935723114957 3 0.9744173288345337 0.7645379304885864 0.5770454281352505 0.8494939884093071 4 1.015865445137024 0.741278350353241 0.43793509023616617 0.8864472071441079 5 0.7584841251373291 0.8055519461631775 0.561777539547724 0.8502039654681719 6 0.8092712163925171 0.7855374813079834 0.49266161459475993 0.8685709278892491 7 0.7327184081077576 0.8027288317680359 0.4328274742345327 0.8802526537415563 8 0.6495775580406189 0.829208254814148 0.5033842056043153 0.8619557872381504 9 0.854096531867981 0.7737263441085815 0.3729245558956796 0.8969718064740689 10 0.7585716843605042 0.80228191614151 0.4963070153092978 0.8583655438983987 11 0.9064979553222656 0.7615699172019958 0.35174561399713683 0.900052603892688 12 0.6902016401290894 0.8095133900642395 0.4106475178746978 0.8794130724766563 13 0.7084276676177979 0.8097351789474487 0.3385785975757844 0.902247778358599 14 0.8393263220787048 0.7761092185974121 0.37088152250284073 0.89281210592686 15 0.8501049280166626 0.7712512016296387 0.38096020136417175 0.8892114628207529 16 0.6419965028762817 0.8254801630973816 0.30945074553155355 0.9082270353565619 17 0.7826900482177734 0.7881622314453125 0.36574560079795254 0.8944336028250163 18 0.6780310273170471 0.8088566064834595 0.2534700838535518 0.9256697601120645 19 0.6242154836654663 0.824070394039154 0.3383867441213744 0.9008636200071969 20 0.4584072530269623 0.8679026365280151 0.26177146068130264 0.9228015020522226 21 0.5827918648719788 0.8392739295959473 0.31360479052779744 0.9101391271202592 22 0.6062675714492798 0.8269094228744507 0.2622073710659325 0.9258523953388001 23 0.5932649970054626 0.8292341828346252 0.25196206358057505 0.9268186564566655 24 0.47557249665260315 0.8599757552146912 0.30946511839485596 0.9148605306638157 25 0.4841950535774231 0.8645213842391968 0.22860966517647766 0.9357374807725175 26 0.7061476707458496 0.7996266484260559 0.2925021669254576 0.9165945997886038 27 0.6015442609786987 0.8315213322639465 0.20215039940516044 0.9420202714310256 28 0.43521547317504883 0.8741108775138855 0.24719053309984135 0.9313070976809558 29 0.4239364266395569 0.8793813586235046 0.2097700149081843 0.9422942206654992 30 0.5048272609710693 0.8593810796737671 0.2401556129483657 0.9309646539027983 31 0.489469975233078 0.8627908229827881 0.22536287135240027 0.9415836101882613 32 0.4647248387336731 0.8671607971191406 0.20903944469786978 0.940456081081081 33 0.5304650664329529 0.8471976518630981 0.24628775630858765 0.9372413129271344 34 0.34490254521369934 0.8998329043388367 0.1833824870476928 0.9495656798439993 35 0.5354068875312805 0.8458157181739807 0.23160343482017096 0.940521431727795 36 0.29858243465423584 0.9098154306411743 0.17850942183282695 0.9527180562838664 37 0.29642680287361145 0.9162688255310059 0.22617750950240242 0.9432742054693274 38 0.4861167371273041 0.8573485016822815 0.18226540506301675 0.9541189635878454 39 0.3010188639163971 0.9082650542259216 0.1837235573703967 0.9513513513513514 40 0.41039326786994934 0.8822667002677917 0.2379425308819243 0.9425186055620838 41 0.3613796830177307 0.8922756314277649 0.17909636414762375 0.954633939906898 42 0.3663441836833954 0.89372718334198 0.22591740866913815 0.9456902261330306 43 0.30851638317108154 0.9080442786216736 0.1494070034792702 0.9612565445026178 44 0.394888699054718 0.8823418617248535 0.19560620598128078 0.9520476931052358 45 0.36645153164863586 0.8998531699180603 0.16021833797492605 0.9587164073550212 46 0.3333863914012909 0.9018248915672302 0.1999048011272296 0.9505310229178312 47 0.2874438464641571 0.9160529971122742 0.16666816257604808 0.960165081643639 48 0.3492628335952759 0.8969290256500244 0.18108391457485634 0.9548982407726803 49 0.26795411109924316 0.9243243336677551 0.21701353653393765 0.9508528476438277 50 0.248086079955101 0.9305405616760254 0.16292359000351517 0.960112705820199 51 0.3259166181087494 0.9070965051651001 0.19723174867869758 0.9538953540019544 52 0.3302572965621948 0.9024418592453003 0.1508477964913319 0.961190855927698 53 0.27958548069000244 0.9207113981246948 0.1904797747824282 0.9543767538698289 54 0.16433565318584442 0.948958694934845 0.17251680215833257 0.9598856350719113 55 0.27173298597335815 0.92266845703125 0.16782141611709758 0.9599893541518808 56 0.2755664587020874 0.9186662435531616 0.22669832923696626 0.9516470814871894 57 0.189011812210083 0.9415751099586487 0.1675886069683395 0.9613572101790764 58 0.2607693374156952 0.9214391112327576 0.2151498398784825 0.9551431502378066 59 0.2746150493621826 0.918997585773468 0.1380430418927615 0.9676849726293484 60 0.21890798211097717 0.9355718493461609 0.19920565840509377 0.9581351094196003 61 0.15521526336669922 0.9544575214385986 0.14964216850493173 0.9644545613727894 62 0.25583744049072266 0.9254676103591919 0.20288241586991884 0.9565506118964046 63 0.17124205827713013 0.9474444389343262 0.16137715266712455 0.9648646280557259 64 0.21466396749019623 0.9381143450737 0.1749702367942389 0.9606983068690236 65 0.27771100401878357 0.9192952513694763 0.22375213640722855 0.9553045660778068 66 0.26340624690055847 0.9214857220649719 0.1539960362294639 0.9651931778628611 67 0.14390389621257782 0.9563758373260498 0.20468247180351035 0.9574820541137493 68 0.2060651183128357 0.9404266476631165 0.14046846865019044 0.968788357005085 69 0.22334806621074677 0.9356790781021118 0.1867908813625826 0.9606936416184971 70 0.263373464345932 0.9197981953620911 0.1636001367896333 0.9648893535459139 71 0.14088337123394012 0.9582729339599609 0.172205272998236 0.9629796433075122 72 0.16707344353199005 0.9497310519218445 0.22274104484705293 0.9572017231691328 73 0.18338549137115479 0.9464616775512695 0.16009202504360087 0.9630168649944354 74 0.1776522696018219 0.9469922184944153 0.2380899750669033 0.9558591208995446 75 0.15418972074985504 0.9549180269241333 0.13821197904527677 0.969094729469445 76 0.24340903759002686 0.9277198314666748 0.2047599586234242 0.9594278517452322 77 0.14811696112155914 0.9572144150733948 0.15122933005070688 0.9663784822286263 78 0.2442699372768402 0.9245946407318115 0.2007233374500947 0.9605488850771869 79 0.2504882216453552 0.9278743267059326 0.16786470853618676 0.9646957272334916 80 0.09753310680389404 0.9727811217308044 0.16190347678147662 0.9654542290288897 81 0.12955155968666077 0.9611342549324036 0.2350659158628621 0.9557780153562057 82 0.1640556901693344 0.9537267684936523 0.1616236491603457 0.9647923431891984 83 0.12819471955299377 0.9622443914413452 0.21609570307221665 0.9590660132603056 84 0.15176016092300415 0.9576533436775208 0.14456109323978342 0.9674454561071981 85 0.15537168085575104 0.9550252556800842 0.1806748856647033 0.9646872803935348 86 0.16997376084327698 0.9518248438835144 0.17191327273741286 0.9636602451838879 full\_valid\_loss and full\_valid\_mean\_token\_accuracy for 5 epochs: epochs, full\_valid\_loss, full\_valid\_mean\_token\_accuracy epoch 1:0.31483180259075205, 0.9072434607645875 epoch 2: 0.21055486082311128, 0.9437122736418511 epoch 3: 0.17589645213044625, 0.9572434607645876 epoch 4: 0.17671230333431864, 0.9617706237424547 epoch 5: 0.1795135809861918, 0.9639336016096579 —Version 2 Result— Hyperparamter used: Batch size = 16, Learning rate multiplier = 1.2, epochs = 5 step train\_loss train\_mean\_token\_accuracy valid\_loss valid\_mean\_token\_accuracy 1 0.43189537525177 0.921484649181366 0.21273402596543523 0.9600138480180024 2 0.42393168807029724 0.9153726696968079 0.26159323018618164 0.9522846744844319 3 0.38738900423049927 0.9318317770957947 0.10961287551008395 0.973808719161879 4 0.2736471891403198 0.9544827342033386 0.28052981435899904 0.941198224852071 5 0.17514564096927643 0.9653102159500122 0.19717209323329266 0.9585482682387619 6 0.2140514850616455 0.9495588541030884 0.17734154048604325 0.9608674384477417 7 0.2762037515640259 0.940207839012146 0.24560403230626007 0.9407744874715261 8 0.2963683307170868 0.9292734861373901 0.13536482950312365 0.9636045494313211 9 0.324065625667572 0.926846981048584 0.21911179024692679 0.9520753102267865 10 0.3919684886932373 0.9076589941978455 0.11047193710251725 0.9710243736151355 11 0.2450418919324875 0.9404680132865906 0.2104776581458658 0.939612188365651 12 0.44408413767814636 0.8888119459152222 0.15874627543592817 0.9614871623874625 13 0.14514826238155365 0.9633381962776184 0.19065130261386587 0.9482791047583223 14 0.16256770491600037 0.9608996510505676 0.1439259528172953 0.9593883590924038 15 0.1808147430419922 0.9534288644790649 0.15932348770881766 0.9576492537313432 16 0.16920186579227448 0.9613741636276245 0.21277083900361615 0.9505901180236047 17 0.24061264097690582 0.9365837574005127 0.10922363706924373 0.9709871244635193 18 0.20352333784103394 0.9468533992767334 0.1833776504626997 0.9530615846126699 19 0.20636878907680511 0.9448661804199219 0.16456625673951222 0.9657142857142857 20 0.15028052031993866 0.9614084362983704 0.2033144525631935 0.950366151342555 21 0.2507861852645874 0.926957368850708 0.08347860418060059 0.9767624430836866 22 0.3309449553489685 0.9150654673576355 0.1891160676799787 0.95473496128648 23 0.2610990107059479 0.9295814037322998 0.20097168595318632 0.9559420289855073 24 0.2596650719642639 0.9278410077095032 0.11205066322691162 0.971499176276771 25 0.2815389633178711 0.925652265548706 0.17269353410075083 0.9543951261966928 26 0.18293045461177826 0.9501661062240601 0.1548588538323733 0.9644165358451072 27 0.15631188452243805 0.9607421159744263 0.22782525708598475 0.9487179487179487 28 0.1804698407649994 0.9483796954154968 0.0858754798165914 0.9774976061283115 29 0.15336890518665314 0.9549483060836792 0.17814949029768987 0.9543704274162496 30 0.23244576156139374 0.9358959794044495 0.20778908545217925 0.9542304886943836 31 0.27950039505958557 0.9250527620315552 0.11598562857591851 0.9724137931034482 32 0.17105869948863983 0.9572479128837585 0.16068660890964379 0.9561795743158648 33 0.2705594003200531 0.9306616187095642 0.15069855995224898 0.9638175144205559 34 0.14705871045589447 0.9563832879066467 0.23752587333558098 0.9474206349206349 35 0.12761597335338593 0.9674128890037537 0.08262021540758022 0.9785739374780471 36 0.11598173528909683 0.9694864153862 0.14186885170430444 0.9605446927374302 37 0.10020824521780014 0.9722169637680054 0.15782693691823965 0.9682539682539683 38 0.164589062333107 0.9512903094291687 0.17549285406646248 0.957002457002457 39 0.11086989939212799 0.9675757884979248 0.1191982862382632 0.9686334350627331 40 0.1451531946659088 0.9598774313926697 0.14608805179166595 0.9672249234647938 41 0.1073744148015976 0.9690449833869934 0.23428076060850228 0.9546019900497512 42 0.1486506313085556 0.9600721597671509 0.11234176238798381 0.9742504409171076 43 0.11642973870038986 0.9676239490509033 0.15819018105536578 0.9597327237559345 44 0.0905413031578064 0.9731410145759583 0.15203743556589544 0.9718548660562902 45 0.16237851977348328 0.9499695897102356 0.20854299338501456 0.9522298936368726 46 0.13676676154136658 0.9620065689086914 0.08107621569083795 0.9776618294472526 47 0.12156200408935547 0.9660566449165344 0.17481176353023742 0.9586648554240248 48 0.09535674750804901 0.9744645953178406 0.21507026436172913 0.9544392523364486 49 0.1322920024394989 0.963008463382721 0.11673265817584452 0.9729318220267298 50 0.10834038257598877 0.9700822830200195 0.1531171270076361 0.959419789328268 51 0.10997047275304794 0.9658938050270081 0.14998494743409518 0.9704399234915667 52 0.10207048058509827 0.9709548354148865 0.22470843095550014 0.9495274914089347 53 0.09919016063213348 0.9705097675323486 0.06274121156482627 0.9829201934703748 54 0.10392924398183823 0.9724887609481812 0.17875565583137606 0.9568245125348189 55 0.12309497594833374 0.9678566455841064 0.20332985016906147 0.9606049290515309 56 0.06669408082962036 0.9812448620796204 0.11175869313311561 0.9737889847378899 57 0.20481833815574646 0.9497835636138916 0.14889756411752358 0.9595959595959596 58 0.0723666101694107 0.977207362651825 0.14741052796276502 0.9703998615198199 59 0.1123598963022232 0.9685141444206238 0.2120405425439226 0.957541447634452 60 0.12750758230686188 0.9620202779769897 0.08094070166419913 0.9792159513349105 61 0.14131535589694977 0.9590163826942444 0.1457113344993817 0.9648668639053254 62 0.05263190716505051 0.9843288064002991 0.1654847123294344 0.9696020633750921 63 0.12790805101394653 0.9641807675361633 0.16833545417978463 0.9633132235447579 64 0.23157501220703125 0.9402003288269043 0.12245071040825771 0.9670579989486595 65 0.18075081706047058 0.9499608874320984 0.12963226899074445 0.9702537182852143 66 0.12532301247119904 0.9656912088394165 0.24372883724462388 0.9512195121951219 67 0.07458651810884476 0.9807178974151611 0.11254987949119712 0.9752854951423214 68 0.12526200711727142 0.968244731426239 0.15376408014271067 0.961034164358264 69 0.0428009107708931 0.9902346134185791 0.13807195407146214 0.9751583861287095 70 0.08501363545656204 0.9803309440612793 0.20190374049024742 0.9563663720142938 71 0.08386851847171783 0.9786530137062073 0.09830754181931811 0.9740217033870437 72 0.055533938109874725 0.986828088760376 0.15781847363087667 0.9639925373134328 73 0.095095694065094 0.9746028780937195 0.24272610984294027 0.9547909581916383 74 0.07834992557764053 0.979353666305542 0.10963620951247317 0.9769957081545064 75 0.055497217923402786 0.9858512282371521 0.16190037374797692 0.9604729133580377 76 0.05339839681982994 0.9848642349243164 0.1550326143793699 0.9719327731092438 77 0.08000563830137253 0.9774056673049927 0.23093709875840884 0.9530105777054516 78 0.053885579109191895 0.9862081408500671 0.06579741167861189 0.9836709059506987 79 0.053419698029756546 0.9838683009147644 0.1805136254412659 0.9602938256898947 80 0.10355999320745468 0.9720398783683777 0.22218169797445841 0.9592270531400966 81 0.07630997896194458 0.9818206429481506 0.11705187934234468 0.9736408566721582 82 0.05590583011507988 0.9854439496994019 0.14766124994263014 0.9641427328111402 83 0.05970679223537445 0.9821974635124207 0.13502232558062288 0.9754055468341183 84 0.04537366330623627 0.9877315759658813 0.24992251534221388 0.9520264681555004 85 0.08679049462080002 0.9771019220352173 0.06235492979038115 0.9851579955314396 86 0.058907411992549896 0.9838352799415588 0.16483020929359316 0.9634193299961494 87 0.09172443300485611 0.9750885367393494 0.21412671107953696 0.962253829321663 88 0.0542391799390316 0.9857635498046875 0.11546697976749715 0.9752052545155994 89 0.07171512395143509 0.9797830581665039 0.13252184272046977 0.967626542657843 90 0.0971238985657692 0.9729651808738708 0.13481341873593022 0.9734312183184758 91 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0.022655064240098 0.9947890639305115 0.16661001655685329 0.9656363322396823 165 0.02598702348768711 0.9945166110992432 0.1533924607562306 0.9763519387932533 166 0.030285196378827095 0.9904102087020874 0.24841255092948572 0.9553264604810997 167 0.03933889418840408 0.9910299777984619 0.06209488445498779 0.9862454655380894 168 0.06741807609796524 0.9826927185058594 0.19482203778450202 0.9623955431754875 169 0.031035343185067177 0.9929735660552979 0.2109367357785351 0.9669529499626587 170 0.014346349984407425 0.9971481561660767 0.12165767179867568 0.9761114797611148 171 0.03277391195297241 0.9909849166870117 0.15658672460528653 0.9654439128123339 full\_valid\_loss and full\_valid\_mean\_token\_accuracy for 5 epochs: epochs, full\_valid\_loss, full\_valid\_mean\_token\_accuracy epoch 1:0.15247034518051916, 0.9098591549295775 epoch 2: 0.15076934432599626, 0.9138832997987928 epoch 3: 0.14382053937470649, 0.9175050301810865 epoch 4: 0.15336846777610855, 0.9181086519114688 epoch 5: 0.16031172299528987, 0.9179577464788733 Some background information: I’m fine tuning for answering exam questions in the domain of accounting and finance, there are both multiple choice questions and comprehensive questions. MCQ is easier and comprehensive is very long and difficult. I have both in my training data, but only MCQ is validation data (because it’s hard to make new comprehensive questions in the validation) Need some advise on how to adjust the hyperparameter. Additonally, I’m aslo wondering whether not having comprehensive questions in the validation data will be a big issue? Thanks! ---
**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\ **Post date:** [February 18, 2025, 5:41am UTC](https://discuss.huggingface.co/t/analyze-the-fine-tuning-result/141508/2 "2025-02-18T05:41:54Z")
There seems to be a way to search for parameters, but I have no idea! > **[Hyperparameter Search with Transformers and Ray Tune](https://huggingface.co/blog/ray-tune)** > > We’re on a journey to advance and democratize artificial intelligence through open source and open science. > **[Hyperparameter Search using Trainer API](https://huggingface.co/docs/transformers/hpo_train)** > > We’re on a journey to advance and democratize artificial intelligence through open source and open science. > **[Hyperparameter Optimization for 🤗Transformers: A guide](https://medium.com/distributed-computing-with-ray/hyperparameter-optimization-for-transformers-a-guide-c4e32c6c989b)** > > By Amog Kamsetty, Kai Fricke, Richard Liaw ---
**Author:** ![taylorzhang55](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/taylorzhang55/32/41396_2.png) [@taylorzhang55](https://discuss.huggingface.co/u/taylorzhang55)\ **Post date:** [February 18, 2025, 6:17am UTC](https://discuss.huggingface.co/t/analyze-the-fine-tuning-result/141508/3 "2025-02-18T06:17:15Z")
Thanks anyway!