LLM Nneɛma a Wɔde Yɛ Adwuma
Nsɛm a wɔka
Mewayz Team
Editorial Team
Adaka Tuntum no Akyi: LLM Architecture Gallery no mu Nsrahwɛ
Kasa Nhwɛsoɔ akɛseɛ (LLMs) afiri nhwehwɛmu dan mu akɔ adwumayɛ ho nhyehyɛeɛ mu, nanso wɔn mu adwumayɛ taa te sɛ ahintasɛm adaka tuntum. Wɔ nnwuma akannifo ne wɔn a wɔyɛ nneɛma a wɔrehwehwɛ sɛ wɔde saa mfiridwuma a ɛsakra yi bedi dwuma fam no, "sɛnea" no ntease ho hia te sɛ "dɛn" no ara pɛ. Ɛyɛ bere a ɛsɛ sɛ yɛtu anammɔn kɔ LLM Architecture Gallery —beae a wɔakora so a yɛhwɛ fapem mfonini ahorow a ɛma nnɛyi AI tumi. Efi autoregressive models a ɛyɛ mmerɛw a ɛyɛ fɛ so kosi agentic nhyehyɛe ahorow ho nsusuwii a ɛyɛ den so no, adansi a wɔpaw biara gyina hɔ ma tumi soronko ne nea wobetumi de adi dwuma. Sɛnea modular adwumayɛ nhyehyɛe te sɛ Mewayz hyehyɛ adwumayɛ nhyehyɛe ma ɛyɛ adwuma yiye no, LLM nhyehyɛe na ɛkyerɛ n’ahoɔden, mmerɛwyɛ, ne nea ɛfata koraa ma w’adwumakuw ahiade.
Adwuma a Ɛyɛ Fɛ: Nsakrae Fapem
Nsrahwɛ biara fi ase wɔ tweatibo afã no so: Transformer architecture. Wɔde saa nhwɛsoɔ yi baeɛ wɔ afe 2017 mu no, gyaee atetesɛm mu dwumadie a ɛtoatoa soɔ no maa "ankasa adwene" afiri. Fa no sɛ nhwehwɛmufo bi a sɛ́ anka ɔbɛkenkan amanneɛbɔ bi asɛmfua biara no, obetumi ahu abusuabɔ a ɛda asɛmfua biara a ɛwɔ kasamu biara mu ntam ntɛm ara na ɔkari bere koro mu. Saa dwumadie a ɛne ne ho di nsɛ yi ma Transformers tumi te nsɛm a ɛfa ho ne nuance ase wɔ kɛseɛ a ebi mmaeɛ da so, na ɛma wɔn ho akokwaw wɔ nteaseɛ ne nsɛm a ɛte sɛ nnipa a wɔyɛ mu. Nnɛyi LLM ahorow nyinaa—efi GPT-4 kosi Claude ne nea ɛboro saa—yɛ saa fapem nhyehyɛe yi asefo. Ne mmɔdenbɔ wɔ ntetee a ɛfa dataset akɛse ho no nti na yɛwɔ nhwɛso ahorow a tumi wom, a wɔde di dwuma wɔ ɔkwan a ɛkɔ akyiri so nnɛ.
Ntaban a Wɔayɛ no Titiriw: Nsonsonoe a Ɛwɔ Nneɛma a Wɔde Yɛ Adwuma mu ma Nnwuma Pɔtee
Sɛ yɛkɔ akyiri sen base Transformer no, gallery no nkorabata yɛ ntaban soronko. Ɛha yi, adansi mu nsakrae yɛ mfonini ahorow a wɔayɛ no yiye ama atirimpɔw ahorow. Wɔayɛ Encoder-Only nhyehyeɛ (te sɛ BERT) no ama nteaseɛ a emu dɔ —ɛyɛ pɛpɛɛpɛ ma nnwuma te sɛ nkateɛ nhwehwɛmu anaa nsɛm a ɛwɔ mu nkyekyɛmu a "akenkan" yɛ ade titire. Decoder-Only nhyehyeɛ (te sɛ GPT series) di mu wɔ awoɔ ntoatoasoɔ mu, ɛhyɛ asɛmfua a ɛdi hɔ ho nkɔm wɔ ntoatoasoɔ mu de kyerɛw email, koodu, anaa adebɔ kɔpi. Awiei koraa no, Encoder-Decoder nhwɛsoɔ (te sɛ T5) ne nsɛm asekyerɛfoɔ ne nsɛm a wɔaboaboa ano a wɔyɛ adwumaden, a wɔyɛ nsɛm a wɔde hyɛ mu ho adwuma de yɛ nsɛm a wɔayɛ no yie. Model a ɛfata a wobɛpaw no te sɛ module a ɛfata a wobɛpaw wɔ Mewayz mu—wode adwinnade pɔtee a wɔayɛ ama adwuma no di dwuma, hwɛ hu sɛ ɛyɛ pɛpɛɛpɛ na ɛyɛ adwuma.
Nkitahodi Ɔyɛkyerɛ: Agent ne Multi-Modal Systems
Yɛn gallery no fã a ɛyɛ nnam sen biara no da nkɔso a aba foforo adi: LLM ahorow nyɛ sɛ mmuae engine ahorow a egyina hɔ ma, na mmom sɛ nsusuwii ho nneɛma a ɛwɔ nhyehyɛe akɛse mu. Agentic Architecture fa LLM core a ɛtumi yɛ nhyehyɛeɛ, yɛ nnwinnadeɛ (te sɛ calculators anaa search APIs), na ɛsan yɛ bio a egyina nea ɛfiri mu ba so. Eyi dan nkɔmmɔbɔ nhwɛso ma ɛbɛyɛ obi a ɔde ne ho di dwuma a otumi wie adwumayɛ nhyehyɛe a ɛyɛ den na ɛwɔ anammɔn pii. Wɔ eyi nkyɛn no, Multi-Modal Architectures bubu akwanside a ɛfa nkyerɛwee nkutoo ho no, de aniwa, ne ɛtɔ mmere bi a aso, dwumadie bom yɛ no nhwɛsoɔ baako. Wei ma wotumi kyerɛkyerɛ mfonini ahorow mu, hwehwɛ nhyehyɛe ahorow mu, anaasɛ wɔyɛ nneɛma a ɛwɔ nsɛm a wɔahyehyɛ no nyinaa mu. Wɔ platform te sɛ Mewayz fam no, saa nhyehyeɛ yi yɛ den titire, ɛfiri sɛ ɛyɛ modular, interconnected, ne workflow-automating nnyinasosɛm a ɛwɔ nnɛyi adwumayɛ OS mu, baabi a AI agent bɛtumi akɔ data nhwehwɛmu, nkitahodiɛ, ne adwuma sohwɛ ntam a ɛnyɛ den.
a wɔde ahyɛ mu "LLM nhyehyɛe nyɛ mfiridwuma mu spec kɛkɛ; ɛyɛ ne nyansa DNA, a ɛkyerɛkyerɛ nea ebetumi ahu, sɛnea ɛsusuw nneɛma ho, ne ɔhaw ahorow a awiei koraa no ebetumi adi ho dwuma ama w'adwuma."na ɛkyerɛ sɛ woayɛ
Wo Stack a Wobɛhwɛ So: Architecture hyia Dwumadie
Saa blueprints yi nteaseɛ ne anammɔn a ɛdi kan. Nea edi hɔ ne nkabom. LLM ahorow a wɔde bedi dwuma yiye no hwehwɛ sɛ wɔfa ɔkwan a wɔfa so yɛ adwuma a wosusuw nneɛma pii ho sen nhwɛsode no ara kwa. Nneɛma atitiriw a ɛsɛ sɛ wosusuw ho ne:
- Latency vs. Accuracy: So wuhia mmuae a wɔde ma bere ankasa anaasɛ nhwehwɛmu a emu dɔ na ɛho hia sen biara?
- Ɛka a wɔbɔ no yie: So nhwɛsoɔ ketewa bi a wɔayɛ no yie bɛtumi ayɛ adwuma asen generalist kɛseɛ ama wo dwumadie pɔtee no?
- Data Security & Privacy: So wode API-based models bedi dwuma anaasɛ wobɛgye host wɔ kokoam?
- Orchestration: Ɔkwan bɛn so na LLM no ne wo databases, APIs, ne user interfaces a ɛwɔ hɔ dada no bedi nkitaho?
Eha na asɛnka agua a wɔaka abom bɛyɛ nea ɛho hia. Modular adwumayɛ OS te sɛ Mewayz ma canvas a eye ma deploying saa architectural choices yi. Ɛma wo kwan ma wofa LLM tumi ahodoɔ sɛ nnwuma a ɛtumi yɛ adwuma bom —a wode nsusuiɛ agent bi hyɛ mu ma adetɔfoɔ nhumu nhwehwɛmu berɛ tiawa bi, ne mmara-awoɔ ntoatoasoɔ nhwɛsoɔ ma developer mmoa bere a ɛdi hɔ no —ne nyinaa wɔ ahobanbɔ, nhyehyɛeɛ, ne auditable tebea a ɛwɔ wo nnwuma titire no mu. Ɛnyɛ botae no ne sɛ wobɛtaa nhwɛsoɔ kɛseɛ no akyi, na mmom wobɛboaboa adwumayɛ nhyehyɛeɛ a nyansa, ɛyɛ adwuma yie, na ɛyɛ adwuma yie a AI ahyɛ mu den ano ama wo nsɛnnennen soronko.
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