{
  "aboutHtml": "<p>Liquid AI LFM2.5-2.6B je naknadno učeni besedilni model iz kompaktne družine LFM2.5 podjetja Liquid, zasnovan za agente na napravi in praktične poteke. Njegova konkretna kartica modela navaja 2,69 milijarde parametrov in 30 plasti: 22 kratkih konvolucijskih blokov z dvojnimi vrati in osem blokov pozornosti z združenimi poizvedbami. Hibridna zasnova je usmerjena v učinkovito obdelavo besedila. Model sprejme besedilo in vrne besedilo, vključno s premišljenim odgovorom ali zahtevo za uporabo določene funkcije.</p><p>Proizvajalec ga priporoča za agentske obremenitve, uporabo orodij, izluščanje podatkov, generiranje s pridobivanjem virov in poteke z dolgim kontekstom. Učni cevovod izdaje združuje nadzorovano dodatno učenje, specializirane učiteljske modele, večdomensko destilacijo na podlagi lastne politike in spodbujevalno učenje znotraj agentskih okolij. To pomaga pojasniti osredotočenost na sledenje navodilom naloge, uporabo opažanj orodij in napredovanje skozi več korakov. Podajte mu dejansko gradivo in natančen cilj, namesto da se pri zapolnjevanju vrzeli zanašate na široko naučeno znanje.</p><p>Pri izluščanju pred predložitvijo gradiva določite polja in pravila za vire. Praktična zahteva je: »Preberi te posodobitve storitve, izlušči identifikator vsakega zapisa, navedeno stanje in naslednje potrebno dejanje ter ohrani poved, ki podpira vsako polje. Kadar polje manjka, vrni null.« Določite, ali naj se vrednost natančno prekopira, normalizira ali sklepa. Tako je izhod preverljiv in prepreči, da bi verjetna interpretacija tiho nadomestila podatke, dejansko prisotne v viru.</p><p>Za RAG predložite ustrezne odlomke s stalnimi oznakami in zahtevajte odgovor, utemeljen v teh odlomkih. Primer: »Samo na podlagi teh navodil za namestitev razloži, kateri koraki veljajo za navedeno okolje, in naštej predpogoje, ki ostajajo neznani.« Različice dokumentov in pogoje hranite ob besedilu. Od modela zahtevajte prepoznavanje manjkajočega ali protislovnega dejstva, namesto da izmisli celoten postopek. To bolje ustreza proizvajalčevemu priporočilu za naloge, utemeljene z viri, kot zahteva, naj kompaktni model deluje kot izčrpna shramba strokovnega znanja.</p><p>LFM2.5-2.6B je model, namenjen izključno sklepanju: njegova pogovorna predloga začne odgovor pomočnika z oznako razmišljanja in pred končnim odgovorom sklepa. Proizvajalčeva kartica modela za generiranje priporoča temperature 0.1, top-k 50 in kazen za ponavljanje 1.1. To so nastavitve postopka za poimenovani model. Končni izdelek ločite od besedila sklepanja in v delovni zahtevi pustite prostor za oba. Za ta model ne predpostavljajte nastavitev ali načinov neposrednega odgovarjanja, ki pripadajo drugim izdajam LFM za sledenje navodilom.</p><p>Model uporablja pogovorni format, podoben ChatML, z vlogami system, user, assistant in tool. Njegov dokumentirani funkcijski potek ima štiri faze: opredelite orodja, pustite modelu zahtevati funkcijo, izvedite funkcijo v okoliški aplikaciji in vrnite dejanski rezultat, da model lahko odgovori. Nativni klici so seznami v slogu Python, obdani z oznakami klica orodja; proizvajalec opisuje tudi zahtevo za format klica JSON prek sistemskega navodila. Uporabite ustrezen razčlenjevalnik, ime funkcije in argumente preverite glede na predložene definicije ter rezultat ohranite s klicem, ki ga je ustvaril.</p><p>Pri kompaktnem agentskem poteku razkrijte le funkcije, pomembne za trenutno nalogo, z jedrnatimi opisi in izrecnimi tipi argumentov. Tudi definicije orodij zasedajo kontekst. Uporabno zaporedje lahko poišče stanje predloženega zapisa, primerja vrnjeno stanje s pravilom in razloži, ali je potrebno nadaljnje ukrepanje. Identifikator zapisa predložite kot dokaz in zahtevajte končni odgovor, povezan z vrnjenim opažanjem. Model operacijo predlaga in razlaga; aplikacija zagotovi njeno dejansko izvedbo in rezultat.</p><p>Trenutna proizvajalčeva kartica modela določa kontekst modela 131.072 žetonov, objava pa opisuje namensko fazo razširitve konteksta. Delajte znotraj okna uporabljene konfiguracije ter ohranite meje virov, pomembno zgodovino in prostor za nadaljevanje. Pri več dokumentih zahtevajte določeno primerjavo ali sledenje odvisnosti. Nativna kontrolna točka ter izdelki GGUF, ONNX in MLX služijo različnim potrebam namestitve, osnutkovni model za špekulativno dekodiranje pa je ločen spremljevalni izdelek. Podatki o virih ali hitrosti ene konfiguracije ne opisujejo samodejno vsakega formata modela in delovnega okna.</p><p>Liquid izrecno ne priporoča LFM2.5-2.6B za agentsko programiranje ali naloge z velikimi zahtevami po znanju. Osredotočite ga na konkretne poteke s predloženimi dokazi, pravili izluščanja in opažanji orodij. Tudi predhodno učena kontrolna točka Base je ločen izdelek, namenjen prilagajanju, ta naknadno učena izdaja pa je agentski model. Pri končnem rezultatu preverite izluščene vrednosti glede na njihov vir, preverite, ali navedeni odlomek podpira odgovor, in ločite zahtevani klic orodja od operacije z zabeleženim izidom. Nerazrešene podatke ohranite tam, kjer vplivajo na sklep, namesto da jih tekoče besedilo zakrije.</p>",
  "aboutText": "Liquid AI LFM2.5-2.6B je naknadno učeni besedilni model iz kompaktne družine LFM2.5 podjetja Liquid, zasnovan za agente na napravi in praktične poteke. Njegova konkretna kartica modela navaja 2,69 milijarde parametrov in 30 plasti: 22 kratkih konvolucijskih blokov z dvojnimi vrati in osem blokov pozornosti z združenimi poizvedbami. Hibridna zasnova je usmerjena v učinkovito obdelavo besedila. Model sprejme besedilo in vrne besedilo, vključno s premišljenim odgovorom ali zahtevo za uporabo določene funkcije.\n\nProizvajalec ga priporoča za agentske obremenitve, uporabo orodij, izluščanje podatkov, generiranje s pridobivanjem virov in poteke z dolgim kontekstom. Učni cevovod izdaje združuje nadzorovano dodatno učenje, specializirane učiteljske modele, večdomensko destilacijo na podlagi lastne politike in spodbujevalno učenje znotraj agentskih okolij. To pomaga pojasniti osredotočenost na sledenje navodilom naloge, uporabo opažanj orodij in napredovanje skozi več korakov. Podajte mu dejansko gradivo in natančen cilj, namesto da se pri zapolnjevanju vrzeli zanašate na široko naučeno znanje.\n\nPri izluščanju pred predložitvijo gradiva določite polja in pravila za vire. Praktična zahteva je: »Preberi te posodobitve storitve, izlušči identifikator vsakega zapisa, navedeno stanje in naslednje potrebno dejanje ter ohrani poved, ki podpira vsako polje. Kadar polje manjka, vrni null.« Določite, ali naj se vrednost natančno prekopira, normalizira ali sklepa. Tako je izhod preverljiv in prepreči, da bi verjetna interpretacija tiho nadomestila podatke, dejansko prisotne v viru.\n\nZa RAG predložite ustrezne odlomke s stalnimi oznakami in zahtevajte odgovor, utemeljen v teh odlomkih. Primer: »Samo na podlagi teh navodil za namestitev razloži, kateri koraki veljajo za navedeno okolje, in naštej predpogoje, ki ostajajo neznani.« Različice dokumentov in pogoje hranite ob besedilu. Od modela zahtevajte prepoznavanje manjkajočega ali protislovnega dejstva, namesto da izmisli celoten postopek. To bolje ustreza proizvajalčevemu priporočilu za naloge, utemeljene z viri, kot zahteva, naj kompaktni model deluje kot izčrpna shramba strokovnega znanja.\n\nLFM2.5-2.6B je model, namenjen izključno sklepanju: njegova pogovorna predloga začne odgovor pomočnika z oznako razmišljanja in pred končnim odgovorom sklepa. Proizvajalčeva kartica modela za generiranje priporoča temperature 0.1, top-k 50 in kazen za ponavljanje 1.1. To so nastavitve postopka za poimenovani model. Končni izdelek ločite od besedila sklepanja in v delovni zahtevi pustite prostor za oba. Za ta model ne predpostavljajte nastavitev ali načinov neposrednega odgovarjanja, ki pripadajo drugim izdajam LFM za sledenje navodilom.\n\nModel uporablja pogovorni format, podoben ChatML, z vlogami system, user, assistant in tool. Njegov dokumentirani funkcijski potek ima štiri faze: opredelite orodja, pustite modelu zahtevati funkcijo, izvedite funkcijo v okoliški aplikaciji in vrnite dejanski rezultat, da model lahko odgovori. Nativni klici so seznami v slogu Python, obdani z oznakami klica orodja; proizvajalec opisuje tudi zahtevo za format klica JSON prek sistemskega navodila. Uporabite ustrezen razčlenjevalnik, ime funkcije in argumente preverite glede na predložene definicije ter rezultat ohranite s klicem, ki ga je ustvaril.\n\nPri kompaktnem agentskem poteku razkrijte le funkcije, pomembne za trenutno nalogo, z jedrnatimi opisi in izrecnimi tipi argumentov. Tudi definicije orodij zasedajo kontekst. Uporabno zaporedje lahko poišče stanje predloženega zapisa, primerja vrnjeno stanje s pravilom in razloži, ali je potrebno nadaljnje ukrepanje. Identifikator zapisa predložite kot dokaz in zahtevajte končni odgovor, povezan z vrnjenim opažanjem. Model operacijo predlaga in razlaga; aplikacija zagotovi njeno dejansko izvedbo in rezultat.\n\nTrenutna proizvajalčeva kartica modela določa kontekst modela 131.072 žetonov, objava pa opisuje namensko fazo razširitve konteksta. Delajte znotraj okna uporabljene konfiguracije ter ohranite meje virov, pomembno zgodovino in prostor za nadaljevanje. Pri več dokumentih zahtevajte določeno primerjavo ali sledenje odvisnosti. Nativna kontrolna točka ter izdelki GGUF, ONNX in MLX služijo različnim potrebam namestitve, osnutkovni model za špekulativno dekodiranje pa je ločen spremljevalni izdelek. Podatki o virih ali hitrosti ene konfiguracije ne opisujejo samodejno vsakega formata modela in delovnega okna.\n\nLiquid izrecno ne priporoča LFM2.5-2.6B za agentsko programiranje ali naloge z velikimi zahtevami po znanju. Osredotočite ga na konkretne poteke s predloženimi dokazi, pravili izluščanja in opažanji orodij. Tudi predhodno učena kontrolna točka Base je ločen izdelek, namenjen prilagajanju, ta naknadno učena izdaja pa je agentski model. Pri končnem rezultatu preverite izluščene vrednosti glede na njihov vir, preverite, ali navedeni odlomek podpira odgovor, in ločite zahtevani klic orodja od operacije z zabeleženim izidom. Nerazrešene podatke ohranite tam, kjer vplivajo na sklep, namesto da jih tekoče besedilo zakrije.",
  "editorialOverlay": {
    "copy_fields": null,
    "copy_sha256": null,
    "copy_source": null,
    "html_sha256": "3a04ef24fab04817b58e637e8e320d58a4455b3b37e9c86bf23f40a4fd61b5ff",
    "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06",
    "raw_model_unchanged": true,
    "schema": "ailoot-native-model-editorial-v1"
  },
  "locale": "sl",
  "methods": [
    {
      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">•</span><h2 id=\"sheet-title\">LiquidAI: LFM2.5-2.6B (free)</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Zapri\">×</button></div><div class=\"idline\"><span>LiquidAI /</span><code>liquid/lfm-2.5-2.6b:free</code><button type=\"button\" id=\"copy-id\">Kopiraj ID</button></div><nav class=\"card-jump-nav\" aria-label=\"Razdelki kartice modela\"><button type=\"button\" data-card-jump=\"overview\">Začetek</button><button type=\"button\" data-card-jump=\"methods\">Vhodi in metode</button><button type=\"button\" data-card-jump=\"pricing\">Cenik</button><button type=\"button\" data-card-jump=\"examples\">Primeri</button><button type=\"button\" data-card-jump=\"results\">Rezultati</button><button type=\"button\" data-card-jump=\"aliases\">Druga imena</button></nav><div class=\"card-method-tabs\" role=\"tablist\" aria-label=\"Metode API\"><button type=\"button\" role=\"tab\" tabindex=\"0\" aria-controls=\"card-method-content\" aria-selected=\"true\" id=\"card-method-tab-0\" data-card-method=\"Ultra|chat|/v1/chat/completions|liquid%2Flfm-2.5-2.6b%3Afree\">Chat</button><button type=\"button\" role=\"tab\" tabindex=\"-1\" aria-controls=\"card-method-content\" aria-selected=\"false\" id=\"card-method-tab-1\" data-card-method=\"Ultra|messages|/v1/messages|liquid%2Flfm-2.5-2.6b%3Afree\">Messages</button></div><div id=\"card-method-content\" role=\"tabpanel\" aria-labelledby=\"card-method-tab-0\"><section class=\"card-section\" data-card-section=\"overview\"><h3 tabindex=\"-1\">Začetek</h3><p class=\"mdesc-full\">Liquid AI LFM2.5-2.6B je kompakten besedilni model za sklepanje, namenjen agentom, uporabi orodij, izluščanju podatkov in RAG. Njegova hibridna zasnova ima 2,69 milijarde parametrov. Pred odgovorom sklepa in najbolje deluje s konkretnim izvornim gradivom ter jasno določenimi orodji; proizvajalec odsvetuje agentsko programiranje in naloge z velikimi zahtevami po znanju.</p><dl class=\"card-io\" data-card-io><div><dt>Dovoljeni vhodi</dt><dd>Besedilo</dd></div><div><dt>Rezultati</dt><dd>Besedilo</dd></div></dl><details class=\"card-about\"><summary>O modelu</summary><p class=\"note-s\" data-model-method-boundary>Ta opis zajema zmožnosti modela. Vhodi, izhodi in možnosti, ki so na voljo v Ailoot, so spodaj navedeni za izbrano metodo.</p><div class=\"mabout\" id=\"m-about\"><p>Liquid AI LFM2.5-2.6B je naknadno učeni besedilni model iz kompaktne družine LFM2.5 podjetja Liquid, zasnovan za agente na napravi in praktične poteke. Njegova konkretna kartica modela navaja 2,69 milijarde parametrov in 30 plasti: 22 kratkih konvolucijskih blokov z dvojnimi vrati in osem blokov pozornosti z združenimi poizvedbami. Hibridna zasnova je usmerjena v učinkovito obdelavo besedila. Model sprejme besedilo in vrne besedilo, vključno s premišljenim odgovorom ali zahtevo za uporabo določene funkcije.</p><p>Proizvajalec ga priporoča za agentske obremenitve, uporabo orodij, izluščanje podatkov, generiranje s pridobivanjem virov in poteke z dolgim kontekstom. Učni cevovod izdaje združuje nadzorovano dodatno učenje, specializirane učiteljske modele, večdomensko destilacijo na podlagi lastne politike in spodbujevalno učenje znotraj agentskih okolij. To pomaga pojasniti osredotočenost na sledenje navodilom naloge, uporabo opažanj orodij in napredovanje skozi več korakov. Podajte mu dejansko gradivo in natančen cilj, namesto da se pri zapolnjevanju vrzeli zanašate na široko naučeno znanje.</p><p>Pri izluščanju pred predložitvijo gradiva določite polja in pravila za vire. Praktična zahteva je: »Preberi te posodobitve storitve, izlušči identifikator vsakega zapisa, navedeno stanje in naslednje potrebno dejanje ter ohrani poved, ki podpira vsako polje. Kadar polje manjka, vrni null.« Določite, ali naj se vrednost natančno prekopira, normalizira ali sklepa. Tako je izhod preverljiv in prepreči, da bi verjetna interpretacija tiho nadomestila podatke, dejansko prisotne v viru.</p><p>Za RAG predložite ustrezne odlomke s stalnimi oznakami in zahtevajte odgovor, utemeljen v teh odlomkih. Primer: »Samo na podlagi teh navodil za namestitev razloži, kateri koraki veljajo za navedeno okolje, in naštej predpogoje, ki ostajajo neznani.« Različice dokumentov in pogoje hranite ob besedilu. Od modela zahtevajte prepoznavanje manjkajočega ali protislovnega dejstva, namesto da izmisli celoten postopek. To bolje ustreza proizvajalčevemu priporočilu za naloge, utemeljene z viri, kot zahteva, naj kompaktni model deluje kot izčrpna shramba strokovnega znanja.</p><p>LFM2.5-2.6B je model, namenjen izključno sklepanju: njegova pogovorna predloga začne odgovor pomočnika z oznako razmišljanja in pred končnim odgovorom sklepa. Proizvajalčeva kartica modela za generiranje priporoča temperature 0.1, top-k 50 in kazen za ponavljanje 1.1. To so nastavitve postopka za poimenovani model. Končni izdelek ločite od besedila sklepanja in v delovni zahtevi pustite prostor za oba. Za ta model ne predpostavljajte nastavitev ali načinov neposrednega odgovarjanja, ki pripadajo drugim izdajam LFM za sledenje navodilom.</p><p>Model uporablja pogovorni format, podoben ChatML, z vlogami system, user, assistant in tool. Njegov dokumentirani funkcijski potek ima štiri faze: opredelite orodja, pustite modelu zahtevati funkcijo, izvedite funkcijo v okoliški aplikaciji in vrnite dejanski rezultat, da model lahko odgovori. Nativni klici so seznami v slogu Python, obdani z oznakami klica orodja; proizvajalec opisuje tudi zahtevo za format klica JSON prek sistemskega navodila. Uporabite ustrezen razčlenjevalnik, ime funkcije in argumente preverite glede na predložene definicije ter rezultat ohranite s klicem, ki ga je ustvaril.</p><p>Pri kompaktnem agentskem poteku razkrijte le funkcije, pomembne za trenutno nalogo, z jedrnatimi opisi in izrecnimi tipi argumentov. Tudi definicije orodij zasedajo kontekst. Uporabno zaporedje lahko poišče stanje predloženega zapisa, primerja vrnjeno stanje s pravilom in razloži, ali je potrebno nadaljnje ukrepanje. Identifikator zapisa predložite kot dokaz in zahtevajte končni odgovor, povezan z vrnjenim opažanjem. Model operacijo predlaga in razlaga; aplikacija zagotovi njeno dejansko izvedbo in rezultat.</p><p>Trenutna proizvajalčeva kartica modela določa kontekst modela 131.072 žetonov, objava pa opisuje namensko fazo razširitve konteksta. Delajte znotraj okna uporabljene konfiguracije ter ohranite meje virov, pomembno zgodovino in prostor za nadaljevanje. Pri več dokumentih zahtevajte določeno primerjavo ali sledenje odvisnosti. Nativna kontrolna točka ter izdelki GGUF, ONNX in MLX služijo različnim potrebam namestitve, osnutkovni model za špekulativno dekodiranje pa je ločen spremljevalni izdelek. Podatki o virih ali hitrosti ene konfiguracije ne opisujejo samodejno vsakega formata modela in delovnega okna.</p><p>Liquid izrecno ne priporoča LFM2.5-2.6B za agentsko programiranje ali naloge z velikimi zahtevami po znanju. Osredotočite ga na konkretne poteke s predloženimi dokazi, pravili izluščanja in opažanji orodij. Tudi predhodno učena kontrolna točka Base je ločen izdelek, namenjen prilagajanju, ta naknadno učena izdaja pa je agentski model. Pri končnem rezultatu preverite izluščene vrednosti glede na njihov vir, preverite, ali navedeni odlomek podpira odgovor, in ločite zahtevani klic orodja od operacije z zabeleženim izidom. Nerazrešene podatke ohranite tam, kjer vplivajo na sklep, namesto da jih tekoče besedilo zakrije.</p></div></details></section><section class=\"card-section\" data-card-section=\"methods\"><h3 tabindex=\"-1\">Na voljo v Ailoot · vhodi in metode</h3><details class=\"card-method\" open><summary><b>Ailoot Ultra · OpenAI Chat</b> <code>POST /v1/chat/completions</code></summary><dl class=\"method-capabilities\"><div><dt>Vhod</dt><dd><code>text</code></dd></div><div><dt>Izhod</dt><dd><code>text</code></dd></div><div><dt>Kontekst</dt><dd>65.536 tokens</dd></div><div><dt>Največji izhod</dt><dd>8192 tokens</dd></div><div><dt>Izhodni format</dt><dd>JSON</dd></div></dl><div class=\"tw\" data-card-parameters data-typed-parameters><table><thead><tr><th>Polje</th><th>Vrsta</th><th>Zahteva</th><th>Vrednosti in omejitve</th><th>Privzeto</th></tr></thead><tbody><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>string</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Obvezno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span><div>liquid/lfm-2.5-2.6b:free</div></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">messages</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>array</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Obvezno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">stream</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>boolean</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span><div>false</div></td><td data-cell-label=\"Privzeto\" data-default=\"known\"><span class=\"card-cell-label\">Privzeto</span>false</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">max_tokens</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>integer</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span><div>Najmanj: 1</div><div>Največ: 8192</div></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">max_completion_tokens</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>integer</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span><div>Najmanj: 1</div><div>Največ: 8192</div></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">n</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>integer</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span><div>Najmanj: 1</div><div>Največ: 128</div></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">temperature</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>number</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">top_p</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>number</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">seed</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>integer</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">stop</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code></code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">frequency_penalty</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>number</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">presence_penalty</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>number</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">user</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>string</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr></tbody></table></div><details class=\"note-s\" data-method-contract><summary>Parametri in življenjski cikel metode</summary><div class=\"tw\" data-card-table=\"contract\"><table><thead><tr><th>Parameter</th><th>Specifikacija</th></tr></thead><tbody><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>content_type</code></th><td data-cell-label=\"Specifikacija\"><span class=\"card-cell-label\">Specifikacija</span>application/json</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>required</code></th><td data-cell-label=\"Specifikacija\"><span class=\"card-cell-label\">Specifikacija</span>model, messages</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>optional</code></th><td data-cell-label=\"Specifikacija\"><span class=\"card-cell-label\">Specifikacija</span>stream, max_tokens, max_completion_tokens, n, temperature, top_p, seed, stop, frequency_penalty, presence_penalty, user</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>material_types</code></th><td data-cell-label=\"Specifikacija\"><span class=\"card-cell-label\">Specifikacija</span>text</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>conditions</code></th><td data-cell-label=\"Specifikacija\"><span class=\"card-cell-label\">Specifikacija</span>Use literal text messages only. Cache controls, tools, image/audio/video content and streamed responses are unavailable for this method., The complete encoded JSON request must not exceed 65536 bytes.</td></tr></tbody></table></div><p>Življenjski cikel: <code>synchronous_json</code></p></details></details></section><section class=\"card-section\" data-card-section=\"pricing\"><h3 tabindex=\"-1\">Cenik</h3><div data-native-semantic-method=\"v1\" data-tariff-reference-only><p class=\"note-s\" data-rate-support-note>Tarifni podatki ne dodajajo možnosti zahteve. Podprti parametri so navedeni pri izbrani metodi.</p><dl data-price-conditions><dt>Hitri način</dt><dd>Standardna tarifa <span class=\"note-s\">Ko je izbran hitri način</span></dd><dt>Način flex</dt><dd>Standardna tarifa <span class=\"note-s\">Ko je izbran način flex</span></dd><dt>Najvišja raven razmišljanja</dt><dd>Standardna tarifa <span class=\"note-s\">Ko najvišja raven razmišljanja uporablja privzeto prilagoditev (default)</span></dd><dt>Cene izrecnega predpomnjenja</dt><dd>Ni omogočeno <span class=\"note-s\">Ko je zahtevano eksplicitno predpomnjenje</span></dd><dt>Ničelne tarife samo za besedilo</dt><dd>Text input and output only. Cache, tools, media and streaming are unavailable. <span class=\"note-s\">Za izključno besedilni vhod in izhod</span></dd></dl></div><div data-declared-tariff-pricing><h4>Cene Ailoot</h4><h4>Cene po nastavitvah</h4><div class=\"tw\" data-pricing-profiles><table><thead><tr><th>Nastavitev</th><th>Cenovna lestvica</th><th>Strošek</th></tr></thead><tbody><tr><th data-cell-label=\"Nastavitev\" scope=\"row\"><span class=\"card-cell-label\">Nastavitev</span>Osnovne cene žetonov</th><td data-cell-label=\"Cenovna lestvica\"><span class=\"card-cell-label\">Cenovna lestvica</span>Osnovna cena</td><td data-cell-label=\"Strošek\"><span class=\"card-cell-label\">Strošek</span><div><span>Vhod · Vhodni žetoni</span> <b>$0</b> <span class=\"price-unit\">/ 1K žetonov</span></div><div><span>Izhod · Izhodni žetoni</span> <b>$0</b> <span class=\"price-unit\">/ 1K žetonov</span></div><div><span>Vhod · Branje predpomnilnika</span> <b>—</b> <span class=\"price-unit\">/ 1K žetonov</span></div><div><span>Vhod · Zapis v predpomnilnik</span> <b>—</b> <span class=\"price-unit\">/ 1K žetonov</span></div><div><span>Vhod · Zapis v predpomnilnik, 1 ura</span> <b>—</b> <span class=\"price-unit\">/ 1K žetonov</span></div></td></tr></tbody></table></div></div><p class=\"note-s\">Ailoot pred zahtevo rezervira ocenjeni znesek. Po rezultatu obračuna končni strošek na podlagi izmerjene porabe. Obračun pretakanja uporablja končni zapis porabe. <a href=\"/docs/sl/billing/reserve/\">Kako delujeta rezervacija in obračun →</a></p></section><section class=\"card-section\" data-card-section=\"examples\"><h3 tabindex=\"-1\">Primeri zahtev</h3><h4>Ailoot Ultra · OpenAI Chat</h4><div class=\"code-group\"><div class=\"cg-bar\"><div class=\"cg-tabs\" role=\"tablist\" aria-label=\"Primeri kode\"><button class=\"cg-tab\" id=\"model-code-48-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-48-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl · ustvarite enkrat in ohranite celoten rezultat</button></div><div class=\"cg-tools\"><button class=\"cg-btn cg-copy\" type=\"button\" aria-label=\"Kopiraj kodo\"><svg class=\"cp\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><rect x=\"5.25\" y=\"5.25\" width=\"8.5\" height=\"8.5\" rx=\"1.75\"/><path d=\"M10.75 5.25V3.5a1.25 1.25 0 0 0-1.25-1.25H3.5A1.25 1.25 0 0 0 2.25 3.5v6a1.25 1.25 0 0 0 1.25 1.25h1.75\"/></svg><svg class=\"ok\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><path d=\"m3 8.5 3.25 3.25L13 5\"/></svg></button></div></div><div class=\"cg-body\"><div class=\"cg-pane\" id=\"model-code-48-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-48-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${AILOOT_API_KEY:?Set your Ailoot API key}&quot;\nexport AILOOT_API_KEY\npython3 - &lt;&lt;&#39;PY_REQUEST&#39;\nimport json,os\nfrom pathlib import Path\np=json.loads(&#39;{&quot;model&quot;: &quot;liquid/lfm-2.5-2.6b:free&quot;, &quot;messages&quot;: [{&quot;role&quot;: &quot;user&quot;, &quot;content&quot;: &quot;Summarize the supplied text in three concise points.&quot;}], &quot;stream&quot;: false}&#39;)\n\nPath(&quot;request.json&quot;).write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n  -H &quot;Authorization: Bearer $AILOOT_API_KEY&quot; -H &#39;Content-Type: application/json&#39; \\\n  --data-binary @request.json -D response-headers.txt -o result.json\n# Submit once. Retain the complete result, original identifiers and every response header.\n</code></pre></div></div></div></section><section class=\"card-section\" data-card-section=\"results\"><h3 tabindex=\"-1\">Prejmi in shrani rezultate</h3><h4>Ailoot Ultra · OpenAI Chat</h4><p class=\"note-s\">JSON</p><div class=\"card-result-process\" data-all-results><h5>Ohrani vse dokončane rezultate</h5><p class=\"note-s\">Shranite vsak v celoti vrnjen odgovor, datoteko in del vsebine.</p><p class=\"note-s\">Retain the complete response, all actual results, original identifiers, usage and response headers.</p></div><div class=\"card-result-process\" data-unknown-recovery><h5>Če je treba preveriti izvirni odgovor</h5><p class=\"note-s\">Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.</p></div></section><section class=\"card-section\" data-card-section=\"aliases\"><h3 tabindex=\"-1\">Druga imena</h3><h4>Druga imena modelov</h4><p class=\"note-s\"><span data-model-alias-route=\"Ultra\"><b>Ailoot Ultra</b> <a href=\"#m=liquid%2Flfm-2.5-2.6b%3Afree&amp;route=Ultra&amp;caller=liquid%2Flfm-2.5-2.6b%3Afree&amp;fmt=chat\"><code>liquid/lfm-2.5-2.6b:free</code></a></span></p><h4>Druga imena poti metod</h4><p class=\"note-s\">Uporabite zgoraj navedene poti metod.</p><h4>Povezani ID-ji za klic</h4><p class=\"note-s\"><a href=\"#m=liquid%2Flfm-2.5-2.6b%3Afree&amp;route=Ultra&amp;caller=liquid%2Flfm-2.5-2.6b%3Afree&amp;fmt=chat\"><code>liquid/lfm-2.5-2.6b:free</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "liquid/lfm-2.5-2.6b:free",
        "fmt": "chat",
        "http": "POST",
        "path": "/v1/chat/completions",
        "request_model_id": "liquid/lfm-2.5-2.6b:free",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|chat|/v1/chat/completions|liquid%2Flfm-2.5-2.6b%3Afree",
      "ordinal": 0,
      "rawMethod": {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.",
            "authentication": {
              "api_key_source": "AILOOT_API_KEY",
              "header": "Authorization: Bearer"
            },
            "example_body": {
              "messages": [
                {
                  "content": "Summarize the supplied text in three concise points.",
                  "role": "user"
                }
              ],
              "model": "liquid/lfm-2.5-2.6b:free",
              "stream": false
            },
            "lifecycle": {
              "cancel_endpoint": null,
              "create": {
                "http": "POST",
                "path": "/v1/chat/completions"
              },
              "mode": "synchronous_json",
              "status": [],
              "unknown": "Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.",
              "unknown_no_replay": true
            },
            "outputs": {
              "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.",
              "family": "text",
              "format": "JSON"
            },
            "parameters": [
              {
                "default_known": false,
                "enum": [
                  "liquid/lfm-2.5-2.6b:free"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "default_known": false,
                "enum_known": false,
                "items": {
                  "additionalProperties": false,
                  "properties": {
                    "content": {
                      "type": "string"
                    },
                    "role": {
                      "enum": [
                        "user",
                        "assistant",
                        "system",
                        "developer"
                      ],
                      "type": "string"
                    }
                  },
                  "required": [
                    "role",
                    "content"
                  ],
                  "type": "object"
                },
                "minItems": 1,
                "name": "messages",
                "required": true,
                "type": "array"
              },
              {
                "default": false,
                "default_known": true,
                "enum": [
                  false
                ],
                "enum_known": true,
                "name": "stream",
                "required": false,
                "type": "boolean"
              },
              {
                "default_known": false,
                "enum_known": false,
                "maximum": 8192,
                "minimum": 1,
                "name": "max_tokens",
                "required": false,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "maximum": 8192,
                "minimum": 1,
                "name": "max_completion_tokens",
                "required": false,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "maximum": 128,
                "minimum": 1,
                "name": "n",
                "required": false,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "temperature",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "top_p",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "seed",
                "required": false,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "stop",
                "oneOf": [
                  {
                    "type": "string"
                  },
                  {
                    "items": {
                      "type": "string"
                    },
                    "type": "array"
                  }
                ],
                "required": false
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "frequency_penalty",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "presence_penalty",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "string"
              }
            ],
            "request_schema": {
              "additionalProperties": false,
              "properties": {
                "frequency_penalty": {
                  "type": "number"
                },
                "max_completion_tokens": {
                  "maximum": 8192,
                  "minimum": 1,
                  "type": "integer"
                },
                "max_tokens": {
                  "maximum": 8192,
                  "minimum": 1,
                  "type": "integer"
                },
                "messages": {
                  "items": {
                    "additionalProperties": false,
                    "properties": {
                      "content": {
                        "type": "string"
                      },
                      "role": {
                        "enum": [
                          "user",
                          "assistant",
                          "system",
                          "developer"
                        ],
                        "type": "string"
                      }
                    },
                    "required": [
                      "role",
                      "content"
                    ],
                    "type": "object"
                  },
                  "minItems": 1,
                  "type": "array"
                },
                "model": {
                  "enum": [
                    "liquid/lfm-2.5-2.6b:free"
                  ],
                  "type": "string"
                },
                "n": {
                  "maximum": 128,
                  "minimum": 1,
                  "type": "integer"
                },
                "presence_penalty": {
                  "type": "number"
                },
                "seed": {
                  "type": "integer"
                },
                "stop": {
                  "oneOf": [
                    {
                      "type": "string"
                    },
                    {
                      "items": {
                        "type": "string"
                      },
                      "type": "array"
                    }
                  ]
                },
                "stream": {
                  "default": false,
                  "enum": [
                    false
                  ],
                  "type": "boolean"
                },
                "temperature": {
                  "type": "number"
                },
                "top_p": {
                  "type": "number"
                },
                "user": {
                  "type": "string"
                }
              },
              "required": [
                "model",
                "messages"
              ],
              "type": "object"
            },
            "schema_template": {
              "body": {
                "messages": [
                  {
                    "content": "Summarize the supplied text in three concise points.",
                    "role": "user"
                  }
                ],
                "model": "liquid/lfm-2.5-2.6b:free",
                "stream": false
              },
              "content_type": "application/json"
            }
          },
          "input": {
            "conditions": [
              "Use literal text messages only. Cache controls, tools, image/audio/video content and streamed responses are unavailable for this method.",
              "The complete encoded JSON request must not exceed 65536 bytes."
            ],
            "content_type": "application/json",
            "material_types": [
              "text"
            ],
            "optional": [
              "stream",
              "max_tokens",
              "max_completion_tokens",
              "n",
              "temperature",
              "top_p",
              "seed",
              "stop",
              "frequency_penalty",
              "presence_penalty",
              "user"
            ],
            "required": [
              "model",
              "messages"
            ]
          },
          "lifecycle": {
            "cancel_endpoint": null,
            "create": {
              "http": "POST",
              "path": "/v1/chat/completions"
            },
            "mode": "synchronous_json",
            "status": [],
            "unknown": "Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.",
            "unknown_no_replay": true
          },
          "method": {
            "http": "POST",
            "path": "/v1/chat/completions"
          },
          "method_aliases": [],
          "output": {
            "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.",
            "family": "text",
            "format": "JSON"
          }
        },
        "ctx": 65536,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"liquid/lfm-2.5-2.6b:free\", \"messages\": [{\"role\": \"user\", \"content\": \"Summarize the supplied text in three concise points.\"}], \"stream\": false}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\n# Submit once. Retain the complete result, original identifiers and every response header.\n",
            "label": "curl · create once and retain the complete result",
            "language": "bash"
          }
        ],
        "family": "openai",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "chat",
        "hints": [
          "Send exact model ID with messages.",
          "Context length is not an output cap.",
          "A text file may supply literal text; image/audio/file-object inputs need exact binding."
        ],
        "http": "POST",
        "img_in": null,
        "img_out": null,
        "in": 0,
        "input": [
          "text"
        ],
        "label": "OpenAI Chat",
        "max_out": 8192,
        "max_reasoning_effort_multiplier": 1,
        "method_aliases": [],
        "out": 0,
        "output": [
          "text"
        ],
        "path": "/v1/chat/completions",
        "price_conditions": [
          {
            "label": "Fast mode",
            "value": "Standard rate",
            "when": "When fast mode is selected"
          },
          {
            "label": "Flex mode",
            "value": "Standard rate",
            "when": "When flex mode is selected"
          },
          {
            "label": "Maximum reasoning effort",
            "value": "Standard rate",
            "when": "When maximum reasoning effort uses the default adjustment"
          },
          {
            "label": "Explicit cache pricing",
            "value": "Not enabled",
            "when": "When explicit cache is requested"
          },
          {
            "label": "Text-only zero rates",
            "value": "Text input and output only. Cache, tools, media and streaming are unavailable.",
            "when": "For text-only input and output"
          }
        ],
        "pricing": {
          "allow_explicit_cache": false,
          "billing_mode": "token",
          "cache_read_price": null,
          "cache_write_1h_price": null,
          "cache_write_price": null,
          "fast_multiplier": 1,
          "flex_multiplier": 1,
          "image_input_price": null,
          "image_output_price": null,
          "input_price": 0,
          "intervals": [],
          "max_reasoning_effort_multiplier": 1,
          "output_price": 0,
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "request_model_id": "liquid/lfm-2.5-2.6b:free",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "unit": "token",
        "units": [
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_read_price",
            "label": "Cache read",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "cached_input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "cached_input_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_price",
            "label": "Cache write",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "generic_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "generic_cache_creation_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_1h_price",
            "label": "Cache write, 1 hour",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "one_hour_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "one_hour_cache_creation_token"
          }
        ]
      }
    },
    {
      "html": "<div class=\"sheet-head\"><span class=\"mav\" aria-hidden=\"true\">•</span><h2 id=\"sheet-title\">LiquidAI: LFM2.5-2.6B (free)</h2><button class=\"icon-btn\" type=\"button\" data-close-sheet aria-label=\"Zapri\">×</button></div><div class=\"idline\"><span>LiquidAI /</span><code>liquid/lfm-2.5-2.6b:free</code><button type=\"button\" id=\"copy-id\">Kopiraj ID</button></div><nav class=\"card-jump-nav\" aria-label=\"Razdelki kartice modela\"><button type=\"button\" data-card-jump=\"overview\">Začetek</button><button type=\"button\" data-card-jump=\"methods\">Vhodi in metode</button><button type=\"button\" data-card-jump=\"pricing\">Cenik</button><button type=\"button\" data-card-jump=\"examples\">Primeri</button><button type=\"button\" data-card-jump=\"results\">Rezultati</button><button type=\"button\" data-card-jump=\"aliases\">Druga imena</button></nav><div class=\"card-method-tabs\" role=\"tablist\" aria-label=\"Metode API\"><button type=\"button\" role=\"tab\" tabindex=\"-1\" aria-controls=\"card-method-content\" aria-selected=\"false\" id=\"card-method-tab-0\" data-card-method=\"Ultra|chat|/v1/chat/completions|liquid%2Flfm-2.5-2.6b%3Afree\">Chat</button><button type=\"button\" role=\"tab\" tabindex=\"0\" aria-controls=\"card-method-content\" aria-selected=\"true\" id=\"card-method-tab-1\" data-card-method=\"Ultra|messages|/v1/messages|liquid%2Flfm-2.5-2.6b%3Afree\">Messages</button></div><div id=\"card-method-content\" role=\"tabpanel\" aria-labelledby=\"card-method-tab-1\"><section class=\"card-section\" data-card-section=\"overview\"><h3 tabindex=\"-1\">Začetek</h3><p class=\"mdesc-full\">Liquid AI LFM2.5-2.6B je kompakten besedilni model za sklepanje, namenjen agentom, uporabi orodij, izluščanju podatkov in RAG. Njegova hibridna zasnova ima 2,69 milijarde parametrov. Pred odgovorom sklepa in najbolje deluje s konkretnim izvornim gradivom ter jasno določenimi orodji; proizvajalec odsvetuje agentsko programiranje in naloge z velikimi zahtevami po znanju.</p><dl class=\"card-io\" data-card-io><div><dt>Dovoljeni vhodi</dt><dd>Besedilo</dd></div><div><dt>Rezultati</dt><dd>Besedilo</dd></div></dl><details class=\"card-about\"><summary>O modelu</summary><p class=\"note-s\" data-model-method-boundary>Ta opis zajema zmožnosti modela. Vhodi, izhodi in možnosti, ki so na voljo v Ailoot, so spodaj navedeni za izbrano metodo.</p><div class=\"mabout\" id=\"m-about\"><p>Liquid AI LFM2.5-2.6B je naknadno učeni besedilni model iz kompaktne družine LFM2.5 podjetja Liquid, zasnovan za agente na napravi in praktične poteke. Njegova konkretna kartica modela navaja 2,69 milijarde parametrov in 30 plasti: 22 kratkih konvolucijskih blokov z dvojnimi vrati in osem blokov pozornosti z združenimi poizvedbami. Hibridna zasnova je usmerjena v učinkovito obdelavo besedila. Model sprejme besedilo in vrne besedilo, vključno s premišljenim odgovorom ali zahtevo za uporabo določene funkcije.</p><p>Proizvajalec ga priporoča za agentske obremenitve, uporabo orodij, izluščanje podatkov, generiranje s pridobivanjem virov in poteke z dolgim kontekstom. Učni cevovod izdaje združuje nadzorovano dodatno učenje, specializirane učiteljske modele, večdomensko destilacijo na podlagi lastne politike in spodbujevalno učenje znotraj agentskih okolij. To pomaga pojasniti osredotočenost na sledenje navodilom naloge, uporabo opažanj orodij in napredovanje skozi več korakov. Podajte mu dejansko gradivo in natančen cilj, namesto da se pri zapolnjevanju vrzeli zanašate na široko naučeno znanje.</p><p>Pri izluščanju pred predložitvijo gradiva določite polja in pravila za vire. Praktična zahteva je: »Preberi te posodobitve storitve, izlušči identifikator vsakega zapisa, navedeno stanje in naslednje potrebno dejanje ter ohrani poved, ki podpira vsako polje. Kadar polje manjka, vrni null.« Določite, ali naj se vrednost natančno prekopira, normalizira ali sklepa. Tako je izhod preverljiv in prepreči, da bi verjetna interpretacija tiho nadomestila podatke, dejansko prisotne v viru.</p><p>Za RAG predložite ustrezne odlomke s stalnimi oznakami in zahtevajte odgovor, utemeljen v teh odlomkih. Primer: »Samo na podlagi teh navodil za namestitev razloži, kateri koraki veljajo za navedeno okolje, in naštej predpogoje, ki ostajajo neznani.« Različice dokumentov in pogoje hranite ob besedilu. Od modela zahtevajte prepoznavanje manjkajočega ali protislovnega dejstva, namesto da izmisli celoten postopek. To bolje ustreza proizvajalčevemu priporočilu za naloge, utemeljene z viri, kot zahteva, naj kompaktni model deluje kot izčrpna shramba strokovnega znanja.</p><p>LFM2.5-2.6B je model, namenjen izključno sklepanju: njegova pogovorna predloga začne odgovor pomočnika z oznako razmišljanja in pred končnim odgovorom sklepa. Proizvajalčeva kartica modela za generiranje priporoča temperature 0.1, top-k 50 in kazen za ponavljanje 1.1. To so nastavitve postopka za poimenovani model. Končni izdelek ločite od besedila sklepanja in v delovni zahtevi pustite prostor za oba. Za ta model ne predpostavljajte nastavitev ali načinov neposrednega odgovarjanja, ki pripadajo drugim izdajam LFM za sledenje navodilom.</p><p>Model uporablja pogovorni format, podoben ChatML, z vlogami system, user, assistant in tool. Njegov dokumentirani funkcijski potek ima štiri faze: opredelite orodja, pustite modelu zahtevati funkcijo, izvedite funkcijo v okoliški aplikaciji in vrnite dejanski rezultat, da model lahko odgovori. Nativni klici so seznami v slogu Python, obdani z oznakami klica orodja; proizvajalec opisuje tudi zahtevo za format klica JSON prek sistemskega navodila. Uporabite ustrezen razčlenjevalnik, ime funkcije in argumente preverite glede na predložene definicije ter rezultat ohranite s klicem, ki ga je ustvaril.</p><p>Pri kompaktnem agentskem poteku razkrijte le funkcije, pomembne za trenutno nalogo, z jedrnatimi opisi in izrecnimi tipi argumentov. Tudi definicije orodij zasedajo kontekst. Uporabno zaporedje lahko poišče stanje predloženega zapisa, primerja vrnjeno stanje s pravilom in razloži, ali je potrebno nadaljnje ukrepanje. Identifikator zapisa predložite kot dokaz in zahtevajte končni odgovor, povezan z vrnjenim opažanjem. Model operacijo predlaga in razlaga; aplikacija zagotovi njeno dejansko izvedbo in rezultat.</p><p>Trenutna proizvajalčeva kartica modela določa kontekst modela 131.072 žetonov, objava pa opisuje namensko fazo razširitve konteksta. Delajte znotraj okna uporabljene konfiguracije ter ohranite meje virov, pomembno zgodovino in prostor za nadaljevanje. Pri več dokumentih zahtevajte določeno primerjavo ali sledenje odvisnosti. Nativna kontrolna točka ter izdelki GGUF, ONNX in MLX služijo različnim potrebam namestitve, osnutkovni model za špekulativno dekodiranje pa je ločen spremljevalni izdelek. Podatki o virih ali hitrosti ene konfiguracije ne opisujejo samodejno vsakega formata modela in delovnega okna.</p><p>Liquid izrecno ne priporoča LFM2.5-2.6B za agentsko programiranje ali naloge z velikimi zahtevami po znanju. Osredotočite ga na konkretne poteke s predloženimi dokazi, pravili izluščanja in opažanji orodij. Tudi predhodno učena kontrolna točka Base je ločen izdelek, namenjen prilagajanju, ta naknadno učena izdaja pa je agentski model. Pri končnem rezultatu preverite izluščene vrednosti glede na njihov vir, preverite, ali navedeni odlomek podpira odgovor, in ločite zahtevani klic orodja od operacije z zabeleženim izidom. Nerazrešene podatke ohranite tam, kjer vplivajo na sklep, namesto da jih tekoče besedilo zakrije.</p></div></details></section><section class=\"card-section\" data-card-section=\"methods\"><h3 tabindex=\"-1\">Na voljo v Ailoot · vhodi in metode</h3><details class=\"card-method\" open><summary><b>Ailoot Ultra · Anthropic Messages</b> <code>POST /v1/messages</code></summary><dl class=\"method-capabilities\"><div><dt>Vhod</dt><dd><code>text</code></dd></div><div><dt>Izhod</dt><dd><code>text</code></dd></div><div><dt>Kontekst</dt><dd>65.536 tokens</dd></div><div><dt>Največji izhod</dt><dd>8192 tokens</dd></div><div><dt>Izhodni format</dt><dd>JSON</dd></div></dl><div class=\"tw\" data-card-parameters data-typed-parameters><table><thead><tr><th>Polje</th><th>Vrsta</th><th>Zahteva</th><th>Vrednosti in omejitve</th><th>Privzeto</th></tr></thead><tbody><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">model</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>string</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Obvezno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span><div>liquid/lfm-2.5-2.6b:free</div></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">messages</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>array</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Obvezno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">stream</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>boolean</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span><div>false</div></td><td data-cell-label=\"Privzeto\" data-default=\"known\"><span class=\"card-cell-label\">Privzeto</span>false</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">system</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>string</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">max_tokens</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>integer</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Obvezno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span><div>Najmanj: 1</div><div>Največ: 8192</div></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">temperature</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>number</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">top_p</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>number</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">top_k</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>integer</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">stop_sequences</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>array</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr><tr><th data-cell-label=\"Polje\" scope=\"row\"><span class=\"card-cell-label\">Polje</span><code class=\"card-field-name\">metadata</code></th><td data-cell-label=\"Vrsta\" data-field-type><span class=\"card-cell-label\">Vrsta</span><code>object</code></td><td data-cell-label=\"Zahteva\"><span class=\"card-cell-label\">Zahteva</span>Izbirno</td><td data-cell-label=\"Vrednosti in omejitve\"><span class=\"card-cell-label\">Vrednosti in omejitve</span></td><td data-cell-label=\"Privzeto\" data-default=\"unspecified\"><span class=\"card-cell-label\">Privzeto</span>—</td></tr></tbody></table></div><details class=\"note-s\" data-method-contract><summary>Parametri in življenjski cikel metode</summary><div class=\"tw\" data-card-table=\"contract\"><table><thead><tr><th>Parameter</th><th>Specifikacija</th></tr></thead><tbody><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>content_type</code></th><td data-cell-label=\"Specifikacija\"><span class=\"card-cell-label\">Specifikacija</span>application/json</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>required</code></th><td data-cell-label=\"Specifikacija\"><span class=\"card-cell-label\">Specifikacija</span>model, messages, max_tokens</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>optional</code></th><td data-cell-label=\"Specifikacija\"><span class=\"card-cell-label\">Specifikacija</span>stream, system, temperature, top_p, top_k, stop_sequences, metadata</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>material_types</code></th><td data-cell-label=\"Specifikacija\"><span class=\"card-cell-label\">Specifikacija</span>text</td></tr><tr><th data-cell-label=\"Parameter\" scope=\"row\"><span class=\"card-cell-label\">Parameter</span><code>conditions</code></th><td data-cell-label=\"Specifikacija\"><span class=\"card-cell-label\">Specifikacija</span>Use literal text messages only. Cache controls, tools, image/audio/video content and streamed responses are unavailable for this method., The complete encoded JSON request must not exceed 65536 bytes.</td></tr></tbody></table></div><p>Življenjski cikel: <code>synchronous_json</code></p></details></details></section><section class=\"card-section\" data-card-section=\"pricing\"><h3 tabindex=\"-1\">Cenik</h3><div data-native-semantic-method=\"v1\" data-tariff-reference-only><p class=\"note-s\" data-rate-support-note>Tarifni podatki ne dodajajo možnosti zahteve. Podprti parametri so navedeni pri izbrani metodi.</p><dl data-price-conditions><dt>Hitri način</dt><dd>Standardna tarifa <span class=\"note-s\">Ko je izbran hitri način</span></dd><dt>Način flex</dt><dd>Standardna tarifa <span class=\"note-s\">Ko je izbran način flex</span></dd><dt>Najvišja raven razmišljanja</dt><dd>Standardna tarifa <span class=\"note-s\">Ko najvišja raven razmišljanja uporablja privzeto prilagoditev (default)</span></dd><dt>Ničelne tarife samo za besedilo</dt><dd>Text input and output only. Cache, tools, media and streaming are unavailable. <span class=\"note-s\">Za izključno besedilni vhod in izhod</span></dd></dl></div><div data-declared-tariff-pricing><h4>Cene Ailoot</h4><h4>Cene po nastavitvah</h4><div class=\"tw\" data-pricing-profiles><table><thead><tr><th>Nastavitev</th><th>Cenovna lestvica</th><th>Strošek</th></tr></thead><tbody><tr><th data-cell-label=\"Nastavitev\" scope=\"row\"><span class=\"card-cell-label\">Nastavitev</span>Osnovne cene žetonov</th><td data-cell-label=\"Cenovna lestvica\"><span class=\"card-cell-label\">Cenovna lestvica</span>Osnovna cena</td><td data-cell-label=\"Strošek\"><span class=\"card-cell-label\">Strošek</span><div><span>Vhod · Vhodni žetoni</span> <b>$0</b> <span class=\"price-unit\">/ 1K žetonov</span></div><div><span>Izhod · Izhodni žetoni</span> <b>$0</b> <span class=\"price-unit\">/ 1K žetonov</span></div><div><span>Vhod · Branje predpomnilnika</span> <b>—</b> <span class=\"price-unit\">/ 1K žetonov</span></div><div><span>Vhod · Zapis v predpomnilnik</span> <b>—</b> <span class=\"price-unit\">/ 1K žetonov</span></div><div><span>Vhod · Zapis v predpomnilnik, 1 ura</span> <b>—</b> <span class=\"price-unit\">/ 1K žetonov</span></div></td></tr></tbody></table></div></div><p class=\"note-s\">Ailoot pred zahtevo rezervira ocenjeni znesek. Po rezultatu obračuna končni strošek na podlagi izmerjene porabe. Obračun pretakanja uporablja končni zapis porabe. <a href=\"/docs/sl/billing/reserve/\">Kako delujeta rezervacija in obračun →</a></p></section><section class=\"card-section\" data-card-section=\"examples\"><h3 tabindex=\"-1\">Primeri zahtev</h3><h4>Ailoot Ultra · Anthropic Messages</h4><div class=\"code-group\"><div class=\"cg-bar\"><div class=\"cg-tabs\" role=\"tablist\" aria-label=\"Primeri kode\"><button class=\"cg-tab\" id=\"model-code-49-tab-0\" role=\"tab\" type=\"button\" aria-controls=\"model-code-49-panel-0\" aria-selected=\"true\" tabindex=\"0\">curl · ustvarite enkrat in ohranite celoten rezultat</button></div><div class=\"cg-tools\"><button class=\"cg-btn cg-copy\" type=\"button\" aria-label=\"Kopiraj kodo\"><svg class=\"cp\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><rect x=\"5.25\" y=\"5.25\" width=\"8.5\" height=\"8.5\" rx=\"1.75\"/><path d=\"M10.75 5.25V3.5a1.25 1.25 0 0 0-1.25-1.25H3.5A1.25 1.25 0 0 0 2.25 3.5v6a1.25 1.25 0 0 0 1.25 1.25h1.75\"/></svg><svg class=\"ok\" viewBox=\"0 0 16 16\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.4\"><path d=\"m3 8.5 3.25 3.25L13 5\"/></svg></button></div></div><div class=\"cg-body\"><div class=\"cg-pane\" id=\"model-code-49-panel-0\" role=\"tabpanel\" aria-labelledby=\"model-code-49-tab-0\"><pre data-lang=\"bash\"><code>set -eu\n: &quot;${AILOOT_API_KEY:?Set your Ailoot API key}&quot;\nexport AILOOT_API_KEY\npython3 - &lt;&lt;&#39;PY_REQUEST&#39;\nimport json,os\nfrom pathlib import Path\np=json.loads(&#39;{&quot;model&quot;: &quot;liquid/lfm-2.5-2.6b:free&quot;, &quot;messages&quot;: [{&quot;role&quot;: &quot;user&quot;, &quot;content&quot;: &quot;Summarize the supplied text in three concise points.&quot;}], &quot;stream&quot;: false, &quot;max_tokens&quot;: 256}&#39;)\n\nPath(&quot;request.json&quot;).write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n  -H &quot;Authorization: Bearer $AILOOT_API_KEY&quot; -H &#39;Content-Type: application/json&#39; \\\n  --data-binary @request.json -D response-headers.txt -o result.json\n# Submit once. Retain the complete result, original identifiers and every response header.\n</code></pre></div></div></div></section><section class=\"card-section\" data-card-section=\"results\"><h3 tabindex=\"-1\">Prejmi in shrani rezultate</h3><h4>Ailoot Ultra · Anthropic Messages</h4><p class=\"note-s\">JSON</p><div class=\"card-result-process\" data-all-results><h5>Ohrani vse dokončane rezultate</h5><p class=\"note-s\">Shranite vsak v celoti vrnjen odgovor, datoteko in del vsebine.</p><p class=\"note-s\">Retain the complete response, all actual results, original identifiers, usage and response headers.</p></div><div class=\"card-result-process\" data-unknown-recovery><h5>Če je treba preveriti izvirni odgovor</h5><p class=\"note-s\">Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.</p></div></section><section class=\"card-section\" data-card-section=\"aliases\"><h3 tabindex=\"-1\">Druga imena</h3><h4>Druga imena modelov</h4><p class=\"note-s\"><span data-model-alias-route=\"Ultra\"><b>Ailoot Ultra</b> <a href=\"#m=liquid%2Flfm-2.5-2.6b%3Afree&amp;route=Ultra&amp;caller=liquid%2Flfm-2.5-2.6b%3Afree&amp;fmt=messages\"><code>liquid/lfm-2.5-2.6b:free</code></a></span></p><h4>Druga imena poti metod</h4><p class=\"note-s\">Uporabite zgoraj navedene poti metod.</p><h4>Povezani ID-ji za klic</h4><p class=\"note-s\"><a href=\"#m=liquid%2Flfm-2.5-2.6b%3Afree&amp;route=Ultra&amp;caller=liquid%2Flfm-2.5-2.6b%3Afree&amp;fmt=messages\"><code>liquid/lfm-2.5-2.6b:free</code></a></p></section></div>",
      "method_identity": {
        "effective_request_model_id": "liquid/lfm-2.5-2.6b:free",
        "fmt": "messages",
        "http": "POST",
        "path": "/v1/messages",
        "request_model_id": "liquid/lfm-2.5-2.6b:free",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|messages|/v1/messages|liquid%2Flfm-2.5-2.6b%3Afree",
      "ordinal": 1,
      "rawMethod": {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.",
            "authentication": {
              "api_key_source": "AILOOT_API_KEY",
              "header": "Authorization: Bearer"
            },
            "example_body": {
              "max_tokens": 256,
              "messages": [
                {
                  "content": "Summarize the supplied text in three concise points.",
                  "role": "user"
                }
              ],
              "model": "liquid/lfm-2.5-2.6b:free",
              "stream": false
            },
            "lifecycle": {
              "cancel_endpoint": null,
              "create": {
                "http": "POST",
                "path": "/v1/messages"
              },
              "mode": "synchronous_json",
              "status": [],
              "unknown": "Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.",
              "unknown_no_replay": true
            },
            "outputs": {
              "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.",
              "family": "text",
              "format": "JSON"
            },
            "parameters": [
              {
                "default_known": false,
                "enum": [
                  "liquid/lfm-2.5-2.6b:free"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "default_known": false,
                "enum_known": false,
                "items": {
                  "additionalProperties": false,
                  "properties": {
                    "content": {
                      "type": "string"
                    },
                    "role": {
                      "enum": [
                        "user",
                        "assistant"
                      ],
                      "type": "string"
                    }
                  },
                  "required": [
                    "role",
                    "content"
                  ],
                  "type": "object"
                },
                "minItems": 1,
                "name": "messages",
                "required": true,
                "type": "array"
              },
              {
                "default": false,
                "default_known": true,
                "enum": [
                  false
                ],
                "enum_known": true,
                "name": "stream",
                "required": false,
                "type": "boolean"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "system",
                "required": false,
                "type": "string"
              },
              {
                "default_known": false,
                "enum_known": false,
                "maximum": 8192,
                "minimum": 1,
                "name": "max_tokens",
                "required": true,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "temperature",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "top_p",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "top_k",
                "required": false,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "items": {
                  "type": "string"
                },
                "name": "stop_sequences",
                "required": false,
                "type": "array"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "metadata",
                "required": false,
                "type": "object"
              }
            ],
            "request_schema": {
              "additionalProperties": false,
              "properties": {
                "max_tokens": {
                  "maximum": 8192,
                  "minimum": 1,
                  "type": "integer"
                },
                "messages": {
                  "items": {
                    "additionalProperties": false,
                    "properties": {
                      "content": {
                        "type": "string"
                      },
                      "role": {
                        "enum": [
                          "user",
                          "assistant"
                        ],
                        "type": "string"
                      }
                    },
                    "required": [
                      "role",
                      "content"
                    ],
                    "type": "object"
                  },
                  "minItems": 1,
                  "type": "array"
                },
                "metadata": {
                  "type": "object"
                },
                "model": {
                  "enum": [
                    "liquid/lfm-2.5-2.6b:free"
                  ],
                  "type": "string"
                },
                "stop_sequences": {
                  "items": {
                    "type": "string"
                  },
                  "type": "array"
                },
                "stream": {
                  "default": false,
                  "enum": [
                    false
                  ],
                  "type": "boolean"
                },
                "system": {
                  "type": "string"
                },
                "temperature": {
                  "type": "number"
                },
                "top_k": {
                  "type": "integer"
                },
                "top_p": {
                  "type": "number"
                }
              },
              "required": [
                "model",
                "messages",
                "max_tokens"
              ],
              "type": "object"
            },
            "schema_template": {
              "body": {
                "max_tokens": 256,
                "messages": [
                  {
                    "content": "Summarize the supplied text in three concise points.",
                    "role": "user"
                  }
                ],
                "model": "liquid/lfm-2.5-2.6b:free",
                "stream": false
              },
              "content_type": "application/json"
            }
          },
          "input": {
            "conditions": [
              "Use literal text messages only. Cache controls, tools, image/audio/video content and streamed responses are unavailable for this method.",
              "The complete encoded JSON request must not exceed 65536 bytes."
            ],
            "content_type": "application/json",
            "material_types": [
              "text"
            ],
            "optional": [
              "stream",
              "system",
              "temperature",
              "top_p",
              "top_k",
              "stop_sequences",
              "metadata"
            ],
            "required": [
              "model",
              "messages",
              "max_tokens"
            ]
          },
          "lifecycle": {
            "cancel_endpoint": null,
            "create": {
              "http": "POST",
              "path": "/v1/messages"
            },
            "mode": "synchronous_json",
            "status": [],
            "unknown": "Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.",
            "unknown_no_replay": true
          },
          "method": {
            "http": "POST",
            "path": "/v1/messages"
          },
          "method_aliases": [],
          "output": {
            "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.",
            "family": "text",
            "format": "JSON"
          }
        },
        "ctx": 65536,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"liquid/lfm-2.5-2.6b:free\", \"messages\": [{\"role\": \"user\", \"content\": \"Summarize the supplied text in three concise points.\"}], \"stream\": false, \"max_tokens\": 256}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\n# Submit once. Retain the complete result, original identifiers and every response header.\n",
            "label": "curl · create once and retain the complete result",
            "language": "bash"
          }
        ],
        "family": "anthropic",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "messages",
        "http": "POST",
        "img_in": null,
        "img_out": null,
        "in": 0,
        "input": [
          "text"
        ],
        "label": "Anthropic Messages",
        "max_out": 8192,
        "max_reasoning_effort_multiplier": 1,
        "method_aliases": [],
        "out": 0,
        "output": [
          "text"
        ],
        "path": "/v1/messages",
        "price_conditions": [
          {
            "label": "Fast mode",
            "value": "Standard rate",
            "when": "When fast mode is selected"
          },
          {
            "label": "Flex mode",
            "value": "Standard rate",
            "when": "When flex mode is selected"
          },
          {
            "label": "Maximum reasoning effort",
            "value": "Standard rate",
            "when": "When maximum reasoning effort uses the default adjustment"
          },
          {
            "label": "Text-only zero rates",
            "value": "Text input and output only. Cache, tools, media and streaming are unavailable.",
            "when": "For text-only input and output"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "cache_read_price": null,
          "cache_write_1h_price": null,
          "cache_write_price": null,
          "fast_multiplier": 1,
          "flex_multiplier": 1,
          "image_input_price": null,
          "image_output_price": null,
          "input_price": 0,
          "intervals": [],
          "max_reasoning_effort_multiplier": 1,
          "output_price": 0,
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "request_model_id": "liquid/lfm-2.5-2.6b:free",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "unit": "token",
        "units": [
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_read_price",
            "label": "Cache read",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "cached_input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "cached_input_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_price",
            "label": "Cache write",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "generic_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "generic_cache_creation_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_1h_price",
            "label": "Cache write, 1 hour",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "one_hour_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "one_hour_cache_creation_token"
          }
        ]
      }
    }
  ],
  "model_id": "liquid/lfm-2.5-2.6b:free",
  "nativeProvenance": {
    "input_facts_canonical_sha256": "40caded550354d75d773e23db62ead2877b10a8a09fba4fb1b6c57b9b9f8d397",
    "input_html_sha256": "8cf6034289017548928e4bfd18efe1d29cd28942fc07b986cd622abfdd914553",
    "private_audit_sha256": "bfb89ffeca2afb91f4eafa83f47f83ea94f917bc0635cccc64d3cf10dea5499a",
    "role": "historical-normalized-input-audit-hashes",
    "schema": "ailoot-native-model-provenance-v2"
  },
  "page": {
    "canonical": "https://ailoot.ai/models/sl/liquid-lfm-2-5-2-6b-u003a-free/",
    "description": "Liquid AI LFM2.5-2.6B je kompakten besedilni model za sklepanje, namenjen agentom, uporabi orodij, izluščanju podatkov in RAG. Njegova hibridna zasnova ima 2,69 milijarde parametrov. Pred odgovorom sklepa in najbolje deluje s konkretnim izvornim gradivom ter jasno določenimi orodji; proizvajalec odsvetuje agentsko programiranje in naloge z velikimi zahtevami po znanju.",
    "locale": "sl",
    "md_url": "https://ailoot.ai/models/sl/liquid-lfm-2-5-2-6b-u003a-free/index.md",
    "title": "LiquidAI: LFM2.5-2.6B (free) — Ailoot",
    "url": "https://ailoot.ai/models/sl/liquid-lfm-2-5-2-6b-u003a-free/"
  },
  "rawModel": {
    "about": "Liquid AI LFM2.5-2.6B je naknadno učeni besedilni model iz kompaktne družine LFM2.5 podjetja Liquid, zasnovan za agente na napravi in praktične poteke. Njegova konkretna kartica modela navaja 2,69 milijarde parametrov in 30 plasti: 22 kratkih konvolucijskih blokov z dvojnimi vrati in osem blokov pozornosti z združenimi poizvedbami. Hibridna zasnova je usmerjena v učinkovito obdelavo besedila. Model sprejme besedilo in vrne besedilo, vključno s premišljenim odgovorom ali zahtevo za uporabo določene funkcije.\n\nProizvajalec ga priporoča za agentske obremenitve, uporabo orodij, izluščanje podatkov, generiranje s pridobivanjem virov in poteke z dolgim kontekstom. Učni cevovod izdaje združuje nadzorovano dodatno učenje, specializirane učiteljske modele, večdomensko destilacijo na podlagi lastne politike in spodbujevalno učenje znotraj agentskih okolij. To pomaga pojasniti osredotočenost na sledenje navodilom naloge, uporabo opažanj orodij in napredovanje skozi več korakov. Podajte mu dejansko gradivo in natančen cilj, namesto da se pri zapolnjevanju vrzeli zanašate na široko naučeno znanje.\n\nPri izluščanju pred predložitvijo gradiva določite polja in pravila za vire. Praktična zahteva je: »Preberi te posodobitve storitve, izlušči identifikator vsakega zapisa, navedeno stanje in naslednje potrebno dejanje ter ohrani poved, ki podpira vsako polje. Kadar polje manjka, vrni null.« Določite, ali naj se vrednost natančno prekopira, normalizira ali sklepa. Tako je izhod preverljiv in prepreči, da bi verjetna interpretacija tiho nadomestila podatke, dejansko prisotne v viru.\n\nZa RAG predložite ustrezne odlomke s stalnimi oznakami in zahtevajte odgovor, utemeljen v teh odlomkih. Primer: »Samo na podlagi teh navodil za namestitev razloži, kateri koraki veljajo za navedeno okolje, in naštej predpogoje, ki ostajajo neznani.« Različice dokumentov in pogoje hranite ob besedilu. Od modela zahtevajte prepoznavanje manjkajočega ali protislovnega dejstva, namesto da izmisli celoten postopek. To bolje ustreza proizvajalčevemu priporočilu za naloge, utemeljene z viri, kot zahteva, naj kompaktni model deluje kot izčrpna shramba strokovnega znanja.\n\nLFM2.5-2.6B je model, namenjen izključno sklepanju: njegova pogovorna predloga začne odgovor pomočnika z oznako razmišljanja in pred končnim odgovorom sklepa. Proizvajalčeva kartica modela za generiranje priporoča temperature 0.1, top-k 50 in kazen za ponavljanje 1.1. To so nastavitve postopka za poimenovani model. Končni izdelek ločite od besedila sklepanja in v delovni zahtevi pustite prostor za oba. Za ta model ne predpostavljajte nastavitev ali načinov neposrednega odgovarjanja, ki pripadajo drugim izdajam LFM za sledenje navodilom.\n\nModel uporablja pogovorni format, podoben ChatML, z vlogami system, user, assistant in tool. Njegov dokumentirani funkcijski potek ima štiri faze: opredelite orodja, pustite modelu zahtevati funkcijo, izvedite funkcijo v okoliški aplikaciji in vrnite dejanski rezultat, da model lahko odgovori. Nativni klici so seznami v slogu Python, obdani z oznakami klica orodja; proizvajalec opisuje tudi zahtevo za format klica JSON prek sistemskega navodila. Uporabite ustrezen razčlenjevalnik, ime funkcije in argumente preverite glede na predložene definicije ter rezultat ohranite s klicem, ki ga je ustvaril.\n\nPri kompaktnem agentskem poteku razkrijte le funkcije, pomembne za trenutno nalogo, z jedrnatimi opisi in izrecnimi tipi argumentov. Tudi definicije orodij zasedajo kontekst. Uporabno zaporedje lahko poišče stanje predloženega zapisa, primerja vrnjeno stanje s pravilom in razloži, ali je potrebno nadaljnje ukrepanje. Identifikator zapisa predložite kot dokaz in zahtevajte končni odgovor, povezan z vrnjenim opažanjem. Model operacijo predlaga in razlaga; aplikacija zagotovi njeno dejansko izvedbo in rezultat.\n\nTrenutna proizvajalčeva kartica modela določa kontekst modela 131.072 žetonov, objava pa opisuje namensko fazo razširitve konteksta. Delajte znotraj okna uporabljene konfiguracije ter ohranite meje virov, pomembno zgodovino in prostor za nadaljevanje. Pri več dokumentih zahtevajte določeno primerjavo ali sledenje odvisnosti. Nativna kontrolna točka ter izdelki GGUF, ONNX in MLX služijo različnim potrebam namestitve, osnutkovni model za špekulativno dekodiranje pa je ločen spremljevalni izdelek. Podatki o virih ali hitrosti ene konfiguracije ne opisujejo samodejno vsakega formata modela in delovnega okna.\n\nLiquid izrecno ne priporoča LFM2.5-2.6B za agentsko programiranje ali naloge z velikimi zahtevami po znanju. Osredotočite ga na konkretne poteke s predloženimi dokazi, pravili izluščanja in opažanji orodij. Tudi predhodno učena kontrolna točka Base je ločen izdelek, namenjen prilagajanju, ta naknadno učena izdaja pa je agentski model. Pri končnem rezultatu preverite izluščene vrednosti glede na njihov vir, preverite, ali navedeni odlomek podpira odgovor, in ločite zahtevani klic orodja od operacije z zabeleženim izidom. Nerazrešene podatke ohranite tam, kjer vplivajo na sklep, namesto da jih tekoče besedilo zakrije.",
    "alias_of": null,
    "aliases": [],
    "author": "LiquidAI",
    "capabilities": {
      "context_length": 65536,
      "manufacturer_input": [
        "text"
      ],
      "manufacturer_output": [
        "text"
      ]
    },
    "card_routes": [
      {
        "label": "Ultra",
        "rates": [
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          }
        ],
        "summary": "Celoten cenik konfiguracije je v podrobnostih."
      }
    ],
    "ctx": null,
    "formats": [
      "chat",
      "messages"
    ],
    "id": "liquid/lfm-2.5-2.6b:free",
    "input": [
      "text"
    ],
    "kind": "text",
    "max_out": null,
    "methods": [
      {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.",
            "authentication": {
              "api_key_source": "AILOOT_API_KEY",
              "header": "Authorization: Bearer"
            },
            "example_body": {
              "messages": [
                {
                  "content": "Summarize the supplied text in three concise points.",
                  "role": "user"
                }
              ],
              "model": "liquid/lfm-2.5-2.6b:free",
              "stream": false
            },
            "lifecycle": {
              "cancel_endpoint": null,
              "create": {
                "http": "POST",
                "path": "/v1/chat/completions"
              },
              "mode": "synchronous_json",
              "status": [],
              "unknown": "Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.",
              "unknown_no_replay": true
            },
            "outputs": {
              "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.",
              "family": "text",
              "format": "JSON"
            },
            "parameters": [
              {
                "default_known": false,
                "enum": [
                  "liquid/lfm-2.5-2.6b:free"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "default_known": false,
                "enum_known": false,
                "items": {
                  "additionalProperties": false,
                  "properties": {
                    "content": {
                      "type": "string"
                    },
                    "role": {
                      "enum": [
                        "user",
                        "assistant",
                        "system",
                        "developer"
                      ],
                      "type": "string"
                    }
                  },
                  "required": [
                    "role",
                    "content"
                  ],
                  "type": "object"
                },
                "minItems": 1,
                "name": "messages",
                "required": true,
                "type": "array"
              },
              {
                "default": false,
                "default_known": true,
                "enum": [
                  false
                ],
                "enum_known": true,
                "name": "stream",
                "required": false,
                "type": "boolean"
              },
              {
                "default_known": false,
                "enum_known": false,
                "maximum": 8192,
                "minimum": 1,
                "name": "max_tokens",
                "required": false,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "maximum": 8192,
                "minimum": 1,
                "name": "max_completion_tokens",
                "required": false,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "maximum": 128,
                "minimum": 1,
                "name": "n",
                "required": false,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "temperature",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "top_p",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "seed",
                "required": false,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "stop",
                "oneOf": [
                  {
                    "type": "string"
                  },
                  {
                    "items": {
                      "type": "string"
                    },
                    "type": "array"
                  }
                ],
                "required": false
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "frequency_penalty",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "presence_penalty",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "user",
                "required": false,
                "type": "string"
              }
            ],
            "request_schema": {
              "additionalProperties": false,
              "properties": {
                "frequency_penalty": {
                  "type": "number"
                },
                "max_completion_tokens": {
                  "maximum": 8192,
                  "minimum": 1,
                  "type": "integer"
                },
                "max_tokens": {
                  "maximum": 8192,
                  "minimum": 1,
                  "type": "integer"
                },
                "messages": {
                  "items": {
                    "additionalProperties": false,
                    "properties": {
                      "content": {
                        "type": "string"
                      },
                      "role": {
                        "enum": [
                          "user",
                          "assistant",
                          "system",
                          "developer"
                        ],
                        "type": "string"
                      }
                    },
                    "required": [
                      "role",
                      "content"
                    ],
                    "type": "object"
                  },
                  "minItems": 1,
                  "type": "array"
                },
                "model": {
                  "enum": [
                    "liquid/lfm-2.5-2.6b:free"
                  ],
                  "type": "string"
                },
                "n": {
                  "maximum": 128,
                  "minimum": 1,
                  "type": "integer"
                },
                "presence_penalty": {
                  "type": "number"
                },
                "seed": {
                  "type": "integer"
                },
                "stop": {
                  "oneOf": [
                    {
                      "type": "string"
                    },
                    {
                      "items": {
                        "type": "string"
                      },
                      "type": "array"
                    }
                  ]
                },
                "stream": {
                  "default": false,
                  "enum": [
                    false
                  ],
                  "type": "boolean"
                },
                "temperature": {
                  "type": "number"
                },
                "top_p": {
                  "type": "number"
                },
                "user": {
                  "type": "string"
                }
              },
              "required": [
                "model",
                "messages"
              ],
              "type": "object"
            },
            "schema_template": {
              "body": {
                "messages": [
                  {
                    "content": "Summarize the supplied text in three concise points.",
                    "role": "user"
                  }
                ],
                "model": "liquid/lfm-2.5-2.6b:free",
                "stream": false
              },
              "content_type": "application/json"
            }
          },
          "input": {
            "conditions": [
              "Use literal text messages only. Cache controls, tools, image/audio/video content and streamed responses are unavailable for this method.",
              "The complete encoded JSON request must not exceed 65536 bytes."
            ],
            "content_type": "application/json",
            "material_types": [
              "text"
            ],
            "optional": [
              "stream",
              "max_tokens",
              "max_completion_tokens",
              "n",
              "temperature",
              "top_p",
              "seed",
              "stop",
              "frequency_penalty",
              "presence_penalty",
              "user"
            ],
            "required": [
              "model",
              "messages"
            ]
          },
          "lifecycle": {
            "cancel_endpoint": null,
            "create": {
              "http": "POST",
              "path": "/v1/chat/completions"
            },
            "mode": "synchronous_json",
            "status": [],
            "unknown": "Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.",
            "unknown_no_replay": true
          },
          "method": {
            "http": "POST",
            "path": "/v1/chat/completions"
          },
          "method_aliases": [],
          "output": {
            "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.",
            "family": "text",
            "format": "JSON"
          }
        },
        "ctx": 65536,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"liquid/lfm-2.5-2.6b:free\", \"messages\": [{\"role\": \"user\", \"content\": \"Summarize the supplied text in three concise points.\"}], \"stream\": false}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\n# Submit once. Retain the complete result, original identifiers and every response header.\n",
            "label": "curl · create once and retain the complete result",
            "language": "bash"
          }
        ],
        "family": "openai",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "chat",
        "hints": [
          "Send exact model ID with messages.",
          "Context length is not an output cap.",
          "A text file may supply literal text; image/audio/file-object inputs need exact binding."
        ],
        "http": "POST",
        "img_in": null,
        "img_out": null,
        "in": 0,
        "input": [
          "text"
        ],
        "label": "OpenAI Chat",
        "max_out": 8192,
        "max_reasoning_effort_multiplier": 1,
        "method_aliases": [],
        "out": 0,
        "output": [
          "text"
        ],
        "path": "/v1/chat/completions",
        "price_conditions": [
          {
            "label": "Fast mode",
            "value": "Standard rate",
            "when": "When fast mode is selected"
          },
          {
            "label": "Flex mode",
            "value": "Standard rate",
            "when": "When flex mode is selected"
          },
          {
            "label": "Maximum reasoning effort",
            "value": "Standard rate",
            "when": "When maximum reasoning effort uses the default adjustment"
          },
          {
            "label": "Explicit cache pricing",
            "value": "Not enabled",
            "when": "When explicit cache is requested"
          },
          {
            "label": "Text-only zero rates",
            "value": "Text input and output only. Cache, tools, media and streaming are unavailable.",
            "when": "For text-only input and output"
          }
        ],
        "pricing": {
          "allow_explicit_cache": false,
          "billing_mode": "token",
          "cache_read_price": null,
          "cache_write_1h_price": null,
          "cache_write_price": null,
          "fast_multiplier": 1,
          "flex_multiplier": 1,
          "image_input_price": null,
          "image_output_price": null,
          "input_price": 0,
          "intervals": [],
          "max_reasoning_effort_multiplier": 1,
          "output_price": 0,
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "request_model_id": "liquid/lfm-2.5-2.6b:free",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "unit": "token",
        "units": [
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_read_price",
            "label": "Cache read",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "cached_input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "cached_input_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_price",
            "label": "Cache write",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "generic_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "generic_cache_creation_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_1h_price",
            "label": "Cache write, 1 hour",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "one_hour_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "one_hour_cache_creation_token"
          }
        ]
      },
      {
        "available": null,
        "cache_read": null,
        "cache_write": null,
        "cache_write_1h": null,
        "contract": {
          "data": {
            "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.",
            "authentication": {
              "api_key_source": "AILOOT_API_KEY",
              "header": "Authorization: Bearer"
            },
            "example_body": {
              "max_tokens": 256,
              "messages": [
                {
                  "content": "Summarize the supplied text in three concise points.",
                  "role": "user"
                }
              ],
              "model": "liquid/lfm-2.5-2.6b:free",
              "stream": false
            },
            "lifecycle": {
              "cancel_endpoint": null,
              "create": {
                "http": "POST",
                "path": "/v1/messages"
              },
              "mode": "synchronous_json",
              "status": [],
              "unknown": "Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.",
              "unknown_no_replay": true
            },
            "outputs": {
              "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.",
              "family": "text",
              "format": "JSON"
            },
            "parameters": [
              {
                "default_known": false,
                "enum": [
                  "liquid/lfm-2.5-2.6b:free"
                ],
                "enum_known": true,
                "name": "model",
                "required": true,
                "type": "string"
              },
              {
                "default_known": false,
                "enum_known": false,
                "items": {
                  "additionalProperties": false,
                  "properties": {
                    "content": {
                      "type": "string"
                    },
                    "role": {
                      "enum": [
                        "user",
                        "assistant"
                      ],
                      "type": "string"
                    }
                  },
                  "required": [
                    "role",
                    "content"
                  ],
                  "type": "object"
                },
                "minItems": 1,
                "name": "messages",
                "required": true,
                "type": "array"
              },
              {
                "default": false,
                "default_known": true,
                "enum": [
                  false
                ],
                "enum_known": true,
                "name": "stream",
                "required": false,
                "type": "boolean"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "system",
                "required": false,
                "type": "string"
              },
              {
                "default_known": false,
                "enum_known": false,
                "maximum": 8192,
                "minimum": 1,
                "name": "max_tokens",
                "required": true,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "temperature",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "top_p",
                "required": false,
                "type": "number"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "top_k",
                "required": false,
                "type": "integer"
              },
              {
                "default_known": false,
                "enum_known": false,
                "items": {
                  "type": "string"
                },
                "name": "stop_sequences",
                "required": false,
                "type": "array"
              },
              {
                "default_known": false,
                "enum_known": false,
                "name": "metadata",
                "required": false,
                "type": "object"
              }
            ],
            "request_schema": {
              "additionalProperties": false,
              "properties": {
                "max_tokens": {
                  "maximum": 8192,
                  "minimum": 1,
                  "type": "integer"
                },
                "messages": {
                  "items": {
                    "additionalProperties": false,
                    "properties": {
                      "content": {
                        "type": "string"
                      },
                      "role": {
                        "enum": [
                          "user",
                          "assistant"
                        ],
                        "type": "string"
                      }
                    },
                    "required": [
                      "role",
                      "content"
                    ],
                    "type": "object"
                  },
                  "minItems": 1,
                  "type": "array"
                },
                "metadata": {
                  "type": "object"
                },
                "model": {
                  "enum": [
                    "liquid/lfm-2.5-2.6b:free"
                  ],
                  "type": "string"
                },
                "stop_sequences": {
                  "items": {
                    "type": "string"
                  },
                  "type": "array"
                },
                "stream": {
                  "default": false,
                  "enum": [
                    false
                  ],
                  "type": "boolean"
                },
                "system": {
                  "type": "string"
                },
                "temperature": {
                  "type": "number"
                },
                "top_k": {
                  "type": "integer"
                },
                "top_p": {
                  "type": "number"
                }
              },
              "required": [
                "model",
                "messages",
                "max_tokens"
              ],
              "type": "object"
            },
            "schema_template": {
              "body": {
                "max_tokens": 256,
                "messages": [
                  {
                    "content": "Summarize the supplied text in three concise points.",
                    "role": "user"
                  }
                ],
                "model": "liquid/lfm-2.5-2.6b:free",
                "stream": false
              },
              "content_type": "application/json"
            }
          },
          "input": {
            "conditions": [
              "Use literal text messages only. Cache controls, tools, image/audio/video content and streamed responses are unavailable for this method.",
              "The complete encoded JSON request must not exceed 65536 bytes."
            ],
            "content_type": "application/json",
            "material_types": [
              "text"
            ],
            "optional": [
              "stream",
              "system",
              "temperature",
              "top_p",
              "top_k",
              "stop_sequences",
              "metadata"
            ],
            "required": [
              "model",
              "messages",
              "max_tokens"
            ]
          },
          "lifecycle": {
            "cancel_endpoint": null,
            "create": {
              "http": "POST",
              "path": "/v1/messages"
            },
            "mode": "synchronous_json",
            "status": [],
            "unknown": "Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.",
            "unknown_no_replay": true
          },
          "method": {
            "http": "POST",
            "path": "/v1/messages"
          },
          "method_aliases": [],
          "output": {
            "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.",
            "family": "text",
            "format": "JSON"
          }
        },
        "ctx": 65536,
        "declared_pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "examples": [
          {
            "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"liquid/lfm-2.5-2.6b:free\", \"messages\": [{\"role\": \"user\", \"content\": \"Summarize the supplied text in three concise points.\"}], \"stream\": false, \"max_tokens\": 256}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n  -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\n# Submit once. Retain the complete result, original identifiers and every response header.\n",
            "label": "curl · create once and retain the complete result",
            "language": "bash"
          }
        ],
        "family": "anthropic",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "messages",
        "http": "POST",
        "img_in": null,
        "img_out": null,
        "in": 0,
        "input": [
          "text"
        ],
        "label": "Anthropic Messages",
        "max_out": 8192,
        "max_reasoning_effort_multiplier": 1,
        "method_aliases": [],
        "out": 0,
        "output": [
          "text"
        ],
        "path": "/v1/messages",
        "price_conditions": [
          {
            "label": "Fast mode",
            "value": "Standard rate",
            "when": "When fast mode is selected"
          },
          {
            "label": "Flex mode",
            "value": "Standard rate",
            "when": "When flex mode is selected"
          },
          {
            "label": "Maximum reasoning effort",
            "value": "Standard rate",
            "when": "When maximum reasoning effort uses the default adjustment"
          },
          {
            "label": "Text-only zero rates",
            "value": "Text input and output only. Cache, tools, media and streaming are unavailable.",
            "when": "For text-only input and output"
          }
        ],
        "pricing": {
          "billing_mode": "token",
          "cache_read_price": null,
          "cache_write_1h_price": null,
          "cache_write_price": null,
          "fast_multiplier": 1,
          "flex_multiplier": 1,
          "image_input_price": null,
          "image_output_price": null,
          "input_price": 0,
          "intervals": [],
          "max_reasoning_effort_multiplier": 1,
          "output_price": 0,
          "per_request_price": null
        },
        "pricing_profiles": [
          {
            "dimensions": {
              "regime": "base"
            },
            "label": "Base token rates",
            "units": [
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "input_price",
                "label": "Input tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "input_token"
              },
              {
                "amount": "0",
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "output_price",
                "label": "Output tokens",
                "native_amount_usd_per_unit": "0",
                "native_quantity": 1,
                "native_unit": "output_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "out",
                "unit": "output_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_read_price",
                "label": "Cache read",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "cached_input_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "cached_input_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_price",
                "label": "Cache write",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "generic_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "generic_cache_creation_token"
              },
              {
                "amount": null,
                "amount_unit": "usd_per_thousand_tokens",
                "billing_input": true,
                "estimate_only": false,
                "key": "cache_write_1h_price",
                "label": "Cache write, 1 hour",
                "native_amount_usd_per_unit": null,
                "native_quantity": 1,
                "native_unit": "one_hour_cache_creation_token",
                "public_unit_label": "/ 1K tokens",
                "quantity": 1000,
                "quantity_is_estimate": false,
                "rate_role": "typed_native_retail_rate",
                "side": "in",
                "unit": "one_hour_cache_creation_token"
              }
            ]
          }
        ],
        "request_model_id": "liquid/lfm-2.5-2.6b:free",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "unit": "token",
        "units": [
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "input_price",
            "label": "Input tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "input_token"
          },
          {
            "amount": "0",
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "output_price",
            "label": "Output tokens",
            "native_amount_usd_per_unit": "0",
            "native_quantity": 1,
            "native_unit": "output_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "out",
            "unit": "output_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_read_price",
            "label": "Cache read",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "cached_input_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "cached_input_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_price",
            "label": "Cache write",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "generic_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "generic_cache_creation_token"
          },
          {
            "amount": null,
            "amount_unit": "usd_per_thousand_tokens",
            "billing_input": true,
            "estimate_only": false,
            "key": "cache_write_1h_price",
            "label": "Cache write, 1 hour",
            "native_amount_usd_per_unit": null,
            "native_quantity": 1,
            "native_unit": "one_hour_cache_creation_token",
            "public_unit_label": "/ 1K tokens",
            "quantity": 1000,
            "quantity_is_estimate": false,
            "rate_role": "typed_native_retail_rate",
            "side": "in",
            "unit": "one_hour_cache_creation_token"
          }
        ]
      }
    ],
    "model_aliases": [
      {
        "canonical_model_id": "liquid/lfm-2.5-2.6b:free",
        "request_model_id": "liquid/lfm-2.5-2.6b:free",
        "route_label": "Ultra"
      }
    ],
    "name": "LiquidAI: LFM2.5-2.6B (free)",
    "price": {
      "in": 0,
      "in_label": null,
      "out": 0,
      "out_label": null,
      "scope_label": "Token rates",
      "sort_in": 0,
      "sort_out": 0,
      "varies": true
    },
    "short": "Liquid AI LFM2.5-2.6B je kompakten besedilni model za sklepanje, namenjen agentom, uporabi orodij, izluščanju podatkov in RAG. Njegova hibridna zasnova ima 2,69 milijarde parametrov. Pred odgovorom sklepa in najbolje deluje s konkretnim izvornim gradivom ter jasno določenimi orodji; proizvajalec odsvetuje agentsko programiranje in naloge z velikimi zahtevami po znanju."
  },
  "renderer_schema": {
    "method_order": "Original DTO methods order; every exact route/format/path/caller survives.",
    "prices": "Original public DTO fields and original native display HTML; no flattened or normalized offers."
  },
  "representation": "exact-public-dto-and-native-renderer-html",
  "representationNotes": {
    "current_reference": {
      "about_json_pointer": "/aboutHtml",
      "methods": [
        {
          "contract_json_pointer": "/methods/0/rawMethod/contract",
          "html_json_pointer": "/methods/0/html",
          "method_ordinal": 0
        },
        {
          "contract_json_pointer": "/methods/1/rawMethod/contract",
          "html_json_pointer": "/methods/1/html",
          "method_ordinal": 1
        }
      ],
      "role": "current-semantic-reference"
    },
    "raw_input": {
      "examples_role": "historical-input-not-current-ready-examples",
      "json_pointers": [
        "/rawModel",
        "/methods/0/rawMethod",
        "/methods/1/rawMethod"
      ],
      "role": "historical-owner-input"
    },
    "schema": "ailoot-native-model-representations-v1"
  },
  "schema": "ailoot-static-model-facts-v1",
  "schema_version": 1,
  "shortText": "Liquid AI LFM2.5-2.6B je kompakten besedilni model za sklepanje, namenjen agentom, uporabi orodij, izluščanju podatkov in RAG. Njegova hibridna zasnova ima 2,69 milijarde parametrov. Pred odgovorom sklepa in najbolje deluje s konkretnim izvornim gradivom ter jasno določenimi orodji; proizvajalec odsvetuje agentsko programiranje in naloge z velikimi zahtevami po znanju.",
  "source": {
    "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1",
    "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036",
    "raw_model_json_sha256": "617d1e37e75da756c77f79171bb751e4446228bdf511af16b6e5dee46ebd683d",
    "renderer_sha256": "dcba2271c18b0f9570a8b166e8d1e3166f121e096a58609c3a46da31562a1bf2"
  },
  "sourceRevision": "e58e061b5a4ab851e15d1a03ab39340292980a0c545509a63d3e963a36ce1cb4"
}