{ "aboutHtml": "

Qwen2-7B-Instruct — suhbat va tabiiy tilda ifodalangan vazifalar uchun Qwen2’ning yo‘riqnomalarga moslab sozlangan, 7B hajmdagi versiyasi. U matn kirishi va matn chiqishiga ega model: javob izoh, tarjima, hujjat qoralamasi yoki dastur kodi bo‘lishi mumkin. Ishlab chiqaruvchi tilni tushunish, matn yaratish, ko‘p tilli ish, dasturlash, matematika va fikr yuritishni asosiy foydalanish sohalari deb ko‘rsatadi.

Model tafsilotlari

Qwen2 alohida asosiy va yo‘riqnomalarga moslab sozlangan relizlarga ega. Bu karta Qwen2-7B asosiy modeli yoki oilaning mixture-of-experts relizi emas, Instruct versiyasi haqida. Ishlab chiqaruvchining model ma’lumotlari jadvali 7B hajmdagi model uchun 7,07 milliard parametrni ko‘rsatadi. Uning dekoder Transformeri SwiGLU, diqqat mexanizmidagi QKV bias va guruhlangan so‘rovli diqqatdan, tabiiy tillar va kodga mo‘ljallangan tokenizerdan foydalanadi. Bular arxitektura va ifodalash tanlovlari, qo‘shimcha kirish turlari emas.

Reliz e’lonida ingliz va xitoy tillari hamda qo‘shimcha 27 tilda o‘qitish tasvirlangan. Ular rus, chex va polyak; nemis, fransuz va ispan; arab, fors va turk; yapon va koreys tillari hamda Janubiy va Janubi-sharqiy Osiyodagi boshqa tillarni o‘z ichiga oladi. Shuning uchun ko‘p tilli yordamchi yo‘riqnoma va manba materialini turli tillarda qabul qilishi mumkin, lekin yakuniy artefaktning tili, atamalari va auditoriyasini baribir belgilash tavsiya etiladi.

O‘qitish va Instruct farqi

Dastlabki o‘qitishdan keyin ishlab chiqaruvchi nazoratli nozik sozlash va to‘g‘ridan-to‘g‘ri afzalliklarni optimallashtirishdan foydalanadi. Yo‘riqnomalarga moslab sozlangan versiya suhbatdagi so‘rovga javob berishga mo‘ljallangan, asosiy versiya esa til modeli poydevori sifatida boshqa rolga ega. Model tanlash yoki yo‘riqnoma xatti-harakatini solishtirishda versiya nomini aniq saqlang. Modelni ko‘rsatmalarga rioya qilishga o‘qitish va foydalanishda aniq yo‘riqnoma berish shu jarayonning bir-birini to‘ldiruvchi qismlaridir.

Suhbat kirishini tayyorlash

Model kartasining tezkor boshlash qo‘llanmasi tizim yo‘riqnomasini foydalanuvchi savolidan ajratib, yaratishdan oldin mos chat shablonini qo‘llaydi. Ilova darajasidagi vazifada ham shu amaliy farq foydali: yordamchi roli va javob qoidalarini u ishlashi kerak bo‘lgan manba parchasi yoki koddan alohida belgilang. Hujjat ichida yozilgan matnli yorliq atrofdagi interfeysda to‘g‘ri formatlangan suhbat rollarining o‘rnini bosmaydi.

Texnik izoh vazifasi funksiya va bitta kirish misolini berib, har bir tarmoq nima qilishini va qaysi shartlar natijaga ta’sir etishini so‘rashi mumkin. Dasturlash vazifasi qabul qilinadigan kirish, kutilgan qaytish qiymati va mavjud xatti-harakatni o‘zgartirish cheklovlarini belgilashi mumkin. Amalga oshirishni, keyin odatiy va chegaraviy holat misollarini so‘rang. Bular hujjatlashtirilgan til va dasturlash imkoniyatlarining qo‘llanishlari; kod yaratish matn natijasidir, uni ishga tushirish esa ilova muhitiga tegishli.

Ko‘p tilli tahrir uchun asl parcha, maqsad auditoriya va qisqa atamalar ro‘yxatini bering. Asl nom va shartlarni saqlaydigan tahrirni, so‘ng o‘zgarishlar haqida qisqa izohni so‘rang. Bu matnni yaxshilash so‘rovidan aniqroq: qaysi ma’no saqlanishi va qanday foydali chiqish qaytarilishi kerakligini belgilaydi.

Uzun matnlarni ishlash

Ishlab chiqaruvchi 131 072 tokengacha kontekstni hujjatlashtiradi, lekin uzun kontekst yo‘riqnomalari muhim. 32 768 tokendan oshadigan kirish hujjatlashtirilgan YaRN kengaytirish konfiguratsiyasini talab qiladi. E’lon qilingan misol 32 768 boshlang‘ich pozitsiya sozlamasi bilan 4,0 masshtablash koeffitsiyentidan foydalanadi. Ishlab chiqaruvchi statik kengaytirish sozlamasi qisqaroq matn xatti-harakatiga ta’sir qilishi mumkinligini aytib, uni uzun kontekst amalda kerak bo‘lganda yoqishni tavsiya qiladi.

Uzun hujjat vazifasini aniq savol, nomlangan manba bo‘limlari va ravshan chiqish tuzilmasi bilan tayyorlang. Masalan, texnik spetsifikatsiyaning bir nechta qismini berib, muayyan o‘zgarishga tegishli talablarni so‘rang va har bir topilma yonida manba bo‘limini saqlang. Katta kirish oynasi shu qismlarni birga ushlab turishda foydali; javob baribir butun hujjatni qayta hikoya qilish o‘rniga ixcham jadval bo‘lishi mumkin.

Muhim chegaralar

131 072 token raqami kengaytirilgan kontekst qo‘llab-quvvatlanishidir, uzun kontekst konfiguratsiyasini e’tiborsiz qoldirishga ruxsat emas. Tezkor boshlash misolidagi yaratiladigan 512 token namunaviy sozlama, e’lon qilingan maksimal chiqish imkoniyati emas. Ushbu Instruct relizi matn modeli bo‘lib, Qwen-VL’dan rasm yoki video imkoniyatlarini yoxud Qwen2.5-Math’ning vositalar bilan integratsiyalangan maxsus matematik o‘qitishini meros qilib olmasligi kerak. Nomlangan versiyani tanlang va matnni kerakli muayyan izoh, kod yoki hujjat uchun tayyorlang.

", "aboutText": "Qwen2-7B-Instruct — suhbat va tabiiy tilda ifodalangan vazifalar uchun Qwen2’ning yo‘riqnomalarga moslab sozlangan, 7B hajmdagi versiyasi. U matn kirishi va matn chiqishiga ega model: javob izoh, tarjima, hujjat qoralamasi yoki dastur kodi bo‘lishi mumkin. Ishlab chiqaruvchi tilni tushunish, matn yaratish, ko‘p tilli ish, dasturlash, matematika va fikr yuritishni asosiy foydalanish sohalari deb ko‘rsatadi.\n\nModel tafsilotlari\n\nQwen2 alohida asosiy va yo‘riqnomalarga moslab sozlangan relizlarga ega. Bu karta Qwen2-7B asosiy modeli yoki oilaning mixture-of-experts relizi emas, Instruct versiyasi haqida. Ishlab chiqaruvchining model ma’lumotlari jadvali 7B hajmdagi model uchun 7,07 milliard parametrni ko‘rsatadi. Uning dekoder Transformeri SwiGLU, diqqat mexanizmidagi QKV bias va guruhlangan so‘rovli diqqatdan, tabiiy tillar va kodga mo‘ljallangan tokenizerdan foydalanadi. Bular arxitektura va ifodalash tanlovlari, qo‘shimcha kirish turlari emas.\n\nReliz e’lonida ingliz va xitoy tillari hamda qo‘shimcha 27 tilda o‘qitish tasvirlangan. Ular rus, chex va polyak; nemis, fransuz va ispan; arab, fors va turk; yapon va koreys tillari hamda Janubiy va Janubi-sharqiy Osiyodagi boshqa tillarni o‘z ichiga oladi. Shuning uchun ko‘p tilli yordamchi yo‘riqnoma va manba materialini turli tillarda qabul qilishi mumkin, lekin yakuniy artefaktning tili, atamalari va auditoriyasini baribir belgilash tavsiya etiladi.\n\nO‘qitish va Instruct farqi\n\nDastlabki o‘qitishdan keyin ishlab chiqaruvchi nazoratli nozik sozlash va to‘g‘ridan-to‘g‘ri afzalliklarni optimallashtirishdan foydalanadi. Yo‘riqnomalarga moslab sozlangan versiya suhbatdagi so‘rovga javob berishga mo‘ljallangan, asosiy versiya esa til modeli poydevori sifatida boshqa rolga ega. Model tanlash yoki yo‘riqnoma xatti-harakatini solishtirishda versiya nomini aniq saqlang. Modelni ko‘rsatmalarga rioya qilishga o‘qitish va foydalanishda aniq yo‘riqnoma berish shu jarayonning bir-birini to‘ldiruvchi qismlaridir.\n\nSuhbat kirishini tayyorlash\n\nModel kartasining tezkor boshlash qo‘llanmasi tizim yo‘riqnomasini foydalanuvchi savolidan ajratib, yaratishdan oldin mos chat shablonini qo‘llaydi. Ilova darajasidagi vazifada ham shu amaliy farq foydali: yordamchi roli va javob qoidalarini u ishlashi kerak bo‘lgan manba parchasi yoki koddan alohida belgilang. Hujjat ichida yozilgan matnli yorliq atrofdagi interfeysda to‘g‘ri formatlangan suhbat rollarining o‘rnini bosmaydi.\n\nTexnik izoh vazifasi funksiya va bitta kirish misolini berib, har bir tarmoq nima qilishini va qaysi shartlar natijaga ta’sir etishini so‘rashi mumkin. Dasturlash vazifasi qabul qilinadigan kirish, kutilgan qaytish qiymati va mavjud xatti-harakatni o‘zgartirish cheklovlarini belgilashi mumkin. Amalga oshirishni, keyin odatiy va chegaraviy holat misollarini so‘rang. Bular hujjatlashtirilgan til va dasturlash imkoniyatlarining qo‘llanishlari; kod yaratish matn natijasidir, uni ishga tushirish esa ilova muhitiga tegishli.\n\nKo‘p tilli tahrir uchun asl parcha, maqsad auditoriya va qisqa atamalar ro‘yxatini bering. Asl nom va shartlarni saqlaydigan tahrirni, so‘ng o‘zgarishlar haqida qisqa izohni so‘rang. Bu matnni yaxshilash so‘rovidan aniqroq: qaysi ma’no saqlanishi va qanday foydali chiqish qaytarilishi kerakligini belgilaydi.\n\nUzun matnlarni ishlash\n\nIshlab chiqaruvchi 131 072 tokengacha kontekstni hujjatlashtiradi, lekin uzun kontekst yo‘riqnomalari muhim. 32 768 tokendan oshadigan kirish hujjatlashtirilgan YaRN kengaytirish konfiguratsiyasini talab qiladi. E’lon qilingan misol 32 768 boshlang‘ich pozitsiya sozlamasi bilan 4,0 masshtablash koeffitsiyentidan foydalanadi. Ishlab chiqaruvchi statik kengaytirish sozlamasi qisqaroq matn xatti-harakatiga ta’sir qilishi mumkinligini aytib, uni uzun kontekst amalda kerak bo‘lganda yoqishni tavsiya qiladi.\n\nUzun hujjat vazifasini aniq savol, nomlangan manba bo‘limlari va ravshan chiqish tuzilmasi bilan tayyorlang. Masalan, texnik spetsifikatsiyaning bir nechta qismini berib, muayyan o‘zgarishga tegishli talablarni so‘rang va har bir topilma yonida manba bo‘limini saqlang. Katta kirish oynasi shu qismlarni birga ushlab turishda foydali; javob baribir butun hujjatni qayta hikoya qilish o‘rniga ixcham jadval bo‘lishi mumkin.\n\nMuhim chegaralar\n\n131 072 token raqami kengaytirilgan kontekst qo‘llab-quvvatlanishidir, uzun kontekst konfiguratsiyasini e’tiborsiz qoldirishga ruxsat emas. Tezkor boshlash misolidagi yaratiladigan 512 token namunaviy sozlama, e’lon qilingan maksimal chiqish imkoniyati emas. Ushbu Instruct relizi matn modeli bo‘lib, Qwen-VL’dan rasm yoki video imkoniyatlarini yoxud Qwen2.5-Math’ning vositalar bilan integratsiyalangan maxsus matematik o‘qitishini meros qilib olmasligi kerak. Nomlangan versiyani tanlang va matnni kerakli muayyan izoh, kod yoki hujjat uchun tayyorlang.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "28f3391328da8ff312b504834b249b6b0540cbf74ac0835acb1917d425a4a42c", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "uz", "methods": [ { "html": "
Al

Qwen2 7B Instruct

Alibaba /qwen2-7b-instruct

Boshlash

Qwen2-7B-Instruct — ko‘p tilli muloqot, dasturlash, matematika va fikr yuritish uchun yo‘riqnomalarga moslab sozlangan matn modeli. Ishlab chiqaruvchi uzun kontekst konfiguratsiyasi yoqilganda 131 072 tokengacha kontekstni hujjatlashtirgan.

Qabul qilinadigan kirish ma’lumotlari
Matn
Natijalar
Matn
Model haqida

Bu tavsif model imkoniyatlarini yoritadi. Ailoot da tanlangan usul uchun mavjud kirish, chiqish va parametrlar quyida keltirilgan.

Qwen2-7B-Instruct — suhbat va tabiiy tilda ifodalangan vazifalar uchun Qwen2’ning yo‘riqnomalarga moslab sozlangan, 7B hajmdagi versiyasi. U matn kirishi va matn chiqishiga ega model: javob izoh, tarjima, hujjat qoralamasi yoki dastur kodi bo‘lishi mumkin. Ishlab chiqaruvchi tilni tushunish, matn yaratish, ko‘p tilli ish, dasturlash, matematika va fikr yuritishni asosiy foydalanish sohalari deb ko‘rsatadi.

Model tafsilotlari

Qwen2 alohida asosiy va yo‘riqnomalarga moslab sozlangan relizlarga ega. Bu karta Qwen2-7B asosiy modeli yoki oilaning mixture-of-experts relizi emas, Instruct versiyasi haqida. Ishlab chiqaruvchining model ma’lumotlari jadvali 7B hajmdagi model uchun 7,07 milliard parametrni ko‘rsatadi. Uning dekoder Transformeri SwiGLU, diqqat mexanizmidagi QKV bias va guruhlangan so‘rovli diqqatdan, tabiiy tillar va kodga mo‘ljallangan tokenizerdan foydalanadi. Bular arxitektura va ifodalash tanlovlari, qo‘shimcha kirish turlari emas.

Reliz e’lonida ingliz va xitoy tillari hamda qo‘shimcha 27 tilda o‘qitish tasvirlangan. Ular rus, chex va polyak; nemis, fransuz va ispan; arab, fors va turk; yapon va koreys tillari hamda Janubiy va Janubi-sharqiy Osiyodagi boshqa tillarni o‘z ichiga oladi. Shuning uchun ko‘p tilli yordamchi yo‘riqnoma va manba materialini turli tillarda qabul qilishi mumkin, lekin yakuniy artefaktning tili, atamalari va auditoriyasini baribir belgilash tavsiya etiladi.

O‘qitish va Instruct farqi

Dastlabki o‘qitishdan keyin ishlab chiqaruvchi nazoratli nozik sozlash va to‘g‘ridan-to‘g‘ri afzalliklarni optimallashtirishdan foydalanadi. Yo‘riqnomalarga moslab sozlangan versiya suhbatdagi so‘rovga javob berishga mo‘ljallangan, asosiy versiya esa til modeli poydevori sifatida boshqa rolga ega. Model tanlash yoki yo‘riqnoma xatti-harakatini solishtirishda versiya nomini aniq saqlang. Modelni ko‘rsatmalarga rioya qilishga o‘qitish va foydalanishda aniq yo‘riqnoma berish shu jarayonning bir-birini to‘ldiruvchi qismlaridir.

Suhbat kirishini tayyorlash

Model kartasining tezkor boshlash qo‘llanmasi tizim yo‘riqnomasini foydalanuvchi savolidan ajratib, yaratishdan oldin mos chat shablonini qo‘llaydi. Ilova darajasidagi vazifada ham shu amaliy farq foydali: yordamchi roli va javob qoidalarini u ishlashi kerak bo‘lgan manba parchasi yoki koddan alohida belgilang. Hujjat ichida yozilgan matnli yorliq atrofdagi interfeysda to‘g‘ri formatlangan suhbat rollarining o‘rnini bosmaydi.

Texnik izoh vazifasi funksiya va bitta kirish misolini berib, har bir tarmoq nima qilishini va qaysi shartlar natijaga ta’sir etishini so‘rashi mumkin. Dasturlash vazifasi qabul qilinadigan kirish, kutilgan qaytish qiymati va mavjud xatti-harakatni o‘zgartirish cheklovlarini belgilashi mumkin. Amalga oshirishni, keyin odatiy va chegaraviy holat misollarini so‘rang. Bular hujjatlashtirilgan til va dasturlash imkoniyatlarining qo‘llanishlari; kod yaratish matn natijasidir, uni ishga tushirish esa ilova muhitiga tegishli.

Ko‘p tilli tahrir uchun asl parcha, maqsad auditoriya va qisqa atamalar ro‘yxatini bering. Asl nom va shartlarni saqlaydigan tahrirni, so‘ng o‘zgarishlar haqida qisqa izohni so‘rang. Bu matnni yaxshilash so‘rovidan aniqroq: qaysi ma’no saqlanishi va qanday foydali chiqish qaytarilishi kerakligini belgilaydi.

Uzun matnlarni ishlash

Ishlab chiqaruvchi 131 072 tokengacha kontekstni hujjatlashtiradi, lekin uzun kontekst yo‘riqnomalari muhim. 32 768 tokendan oshadigan kirish hujjatlashtirilgan YaRN kengaytirish konfiguratsiyasini talab qiladi. E’lon qilingan misol 32 768 boshlang‘ich pozitsiya sozlamasi bilan 4,0 masshtablash koeffitsiyentidan foydalanadi. Ishlab chiqaruvchi statik kengaytirish sozlamasi qisqaroq matn xatti-harakatiga ta’sir qilishi mumkinligini aytib, uni uzun kontekst amalda kerak bo‘lganda yoqishni tavsiya qiladi.

Uzun hujjat vazifasini aniq savol, nomlangan manba bo‘limlari va ravshan chiqish tuzilmasi bilan tayyorlang. Masalan, texnik spetsifikatsiyaning bir nechta qismini berib, muayyan o‘zgarishga tegishli talablarni so‘rang va har bir topilma yonida manba bo‘limini saqlang. Katta kirish oynasi shu qismlarni birga ushlab turishda foydali; javob baribir butun hujjatni qayta hikoya qilish o‘rniga ixcham jadval bo‘lishi mumkin.

Muhim chegaralar

131 072 token raqami kengaytirilgan kontekst qo‘llab-quvvatlanishidir, uzun kontekst konfiguratsiyasini e’tiborsiz qoldirishga ruxsat emas. Tezkor boshlash misolidagi yaratiladigan 512 token namunaviy sozlama, e’lon qilingan maksimal chiqish imkoniyati emas. Ushbu Instruct relizi matn modeli bo‘lib, Qwen-VL’dan rasm yoki video imkoniyatlarini yoxud Qwen2.5-Math’ning vositalar bilan integratsiyalangan maxsus matematik o‘qitishini meros qilib olmasligi kerak. Nomlangan versiyani tanlang va matnni kerakli muayyan izoh, kod yoki hujjat uchun tayyorlang.

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot Ultra · OpenAI Chat POST /v1/chat/completions
Kirish
text
Chiqish
text
Chiqish formati
json yoki SSE
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
qwen2-7b-instruct
Standart—
MaydonmessagesTurmessage[]TalabMajburiyQiymatlar va cheklovlar
Elementlarning eng kam soni: 1
Standart—
Maydonmessages[].roleTurstringTalabMajburiyQiymatlar va cheklovlar
Modelga bogʻliq
Standart—
Maydonmessages[].contentTurstringTalabIxtiyoriyQiymatlar va cheklovlar
Quyidagi holatda qo‘llanadi: Matnli suhbat
Standart—
MaydonstreamTurbooleanTalabIxtiyoriyQiymatlar va cheklovlarStandartfalse
Kirish materiallari birikmalari

Matn · UTF8 matni

messages[].content

Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrcontent_typeParametr talablariapplication/json
ParametrrequiredParametr talablarimodel, messages
Parametrmaterial_typesParametr talablaritext
ParametrmessagesParametr talablarirole/content tuzilishidagi xabarlar massivi
Parametrfile_materialParametr talablariHaqiqiy UTF-8 hujjati aynan matn sifatida kiritiladi; fayl IDsi faraz qilinmaydi
Parametroptional_controlsParametr talablaristream
Parametrcontrol_scopeParametr talablariMatnli suhbat; stream mantiqiy qiymat bo‘lib, standart holatda false.
Parametroutput_modalitiesParametr talablaritext
Parametrhosted_operationsParametr talablarifalse
Parametrmultimodal_input_bindingParametr talablarinull

Usul taxalluslari: /chat/completions

Hayot sikli: synchronous_or_streamed

Aniq model IDsini messages bilan yuboring.

Kontekst uzunligi chiqishning yuqori chegarasi emas.

Matn fayli aynan matnni taqdim etishi mumkin; tasvir, audio va fayl obyekti kirishlari aniq bog‘lashni talab qiladi.

Tariflar

Tarif ma’lumotlari so‘rov imkoniyatlarini qo‘shmaydi. Qo‘llab-quvvatlanadigan parametrlar tanlangan usulda ko‘rsatilgan.

Tezkor rejim
Ko‘rsatilmagan Tezkor rejim tanlanganda
Flex rejimi
Ko‘rsatilmagan flex rejimi tanlanganda
Maksimal fikrlash darajasi
Ko‘rsatilmagan Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda

Ailoot tariflari

Sozlamalar bo‘yicha narxlar

SozlamaTariflar jadvaliTo‘lov
SozlamaTokenlar uchun bazaviy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.000286 / 1K token
Chiqish · Chiqish tokenlari $0.00058 / 1K token
Kirish · Keshdan o‘qish $0 / 1K token
Kirish · Keshga yozish $0 / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token

Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. Summani band qilish va to‘lovni hisoblash tartibi →

So‘rov namunalari

Ailoot Ultra · OpenAI Chat

set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model":"qwen2-7b-instruct","messages":[{"role":"user","content":"hello"}],"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\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("choices",[]):\n    if x.get("message",{}).get("content"): print(x["message"]["content"])\nPY_RESULT\n

Natijalarni olish va saqlash

Ailoot Ultra · OpenAI Chat

json yoki SSE

Barcha tayyor natijalarni saqlash

Qaytarilgan har bir to‘liq javob, fayl va kontent qismini saqlang.

Har bir javob variantini va uning kontenti, vosita chaqiruvlari hamda fikrlash ma’lumotlarini saqlang. SSEda o‘zgarish qismlarini variant va vosita indeksi bo‘yicha yig‘ing, yakuniy tugash sababi va sarf ma’lumotlarini saqlang.

Asl javobni tekshirish kerak bo‘lsa

So‘rov va javob IDlari hamda sarlavhalarini saqlang. Matn vazifasini olish uchun umumiy URL taqdim etilmagan.

Javob maydonlari
MaydonTur
Maydonchoices[].indexTurinteger
Maydonchoices[].message.contentTurstring|null
Maydonchoices[].message.tool_callsTurtool_call[]
Maydonchoices[].message.reasoning_contentTurstring
Maydonchoices[].message.reasoning_detailsTurobject|array
Maydonchoices[].finish_reasonTurstring|null
MaydonusageTurobject
Xatolar va keyingi qadamlar
JavobShartKeyingi qadam
Javob400 · invalid_request_errorShartMajburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘riKeyingi qadamYangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.

Taxalluslar

Model taxalluslari

Ailoot Ultra qwen2-7b-instruct

Usul yo‘llari taxalluslari

/chat/completions

Chaqirish uchun tegishli ID lar

qwen2-7b-instruct

", "method_identity": { "effective_request_model_id": "qwen2-7b-instruct", "fmt": "chat", "http": "POST", "path": "/v1/chat/completions", "request_model_id": "qwen2-7b-instruct", "route_label": "Ultra" }, "method_key": "Ultra|chat|/v1/chat/completions|qwen2-7b-instruct", "ordinal": 0, "rawMethod": { "available": null, "cache_read": 0, "cache_write": 0, "cache_write_1h": null, "contract": { "data": { "all_results": "Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.", "authentication": { "api_key_source": "AILOOT_API_KEY", "header": "Authorization: Bearer" }, "errors": [ { "code": null, "condition": "Majburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri", "http_status": 400, "recovery": "Yangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.", "type": "invalid_request_error" } ], "hints": [ "Model context is not an output cap.", "Send the conversation as text messages. Stream must be a boolean; omitted stream defaults to false." ], "ignored_fields": [], "lifecycle": { "create": { "http": "POST", "path": "/v1/chat/completions" }, "job_read": null, "mode": "synchronous_or_streamed", "unknown": "So‘rov va javob IDlari hamda sarlavhalarini saqlang. Matn vazifasini olish uchun umumiy URL taqdim etilmagan." }, "material_combinations": [ { "mode": "text", "representation": "messages[].content", "required_material": "UTF8 matni" } ], "operation_modes": [ "conversation" ], "outputs": { "all_results": "Har bir javob variantini va uning kontenti, vosita chaqiruvlari hamda fikrlash ma’lumotlarini saqlang. SSEda o‘zgarish qismlarini variant va vosita indeksi bo‘yicha yig‘ing, yakuniy tugash sababi va sarf ma’lumotlarini saqlang.", "family": "text", "fields": [ { "description": null, "name": "choices[].index", "type": "integer" }, { "description": null, "name": "choices[].message.content", "type": "string|null" }, { "description": null, "name": "choices[].message.tool_calls", "type": "tool_call[]" }, { "description": null, "name": "choices[].message.reasoning_content", "type": "string" }, { "description": null, "name": "choices[].message.reasoning_details", "type": "object|array" }, { "description": null, "name": "choices[].finish_reason", "type": "string|null" }, { "description": null, "name": "usage", "type": "object" } ], "format": "json yoki SSE" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "qwen2-7b-instruct" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "min_items": 1, "name": "messages", "required": true, "type": "message[]" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].role", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].content", "required": false, "type": "string", "when": "Matnli suhbat" }, { "availability": "source_interface", "default": false, "default_known": true, "enum": null, "enum_known": false, "name": "stream", "required": false, "type": "boolean" } ], "profile_binding": "Text conversation at the exact model ID and method path.", "unsupported": [ "additional models/models list", "plugins", "route", "hosted tools; ordinary client function tools remain separate", "non-text output modalities", "provider.options overrides", "one-hour prompt-cache TTL" ] }, "hints": [ "Aniq model IDsini messages bilan yuboring.", "Kontekst uzunligi chiqishning yuqori chegarasi emas.", "Matn fayli aynan matnni taqdim etishi mumkin; tasvir, audio va fayl obyekti kirishlari aniq bog‘lashni talab qiladi." ], "input": { "content_type": "application/json", "control_scope": "Matnli suhbat; stream mantiqiy qiymat bo‘lib, standart holatda false.", "file_material": "Haqiqiy UTF-8 hujjati aynan matn sifatida kiritiladi; fayl IDsi faraz qilinmaydi", "hosted_operations": false, "material_types": [ "text" ], "messages": "role/content tuzilishidagi xabarlar massivi", "multimodal_input_binding": null, "optional_controls": [ "stream" ], "output_modalities": [ "text" ], "required": [ "model", "messages" ] }, "lifecycle": { "mode": "synchronous_or_streamed", "unknown": "preserve original request id; no generic chat job retrieval path" }, "manufacturer_capability": null, "method": { "http": "POST", "path": "/v1/chat/completions" }, "method_aliases": [ "/chat/completions" ], "operation_modes": [ "text_chat" ], "output": { "content": "choices[].message.content or stream delta", "family": "text", "format": "json or supported SSE", "tool_calls": "retain actual returned function calls" } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Tokenlar uchun bazaviy tariflar", "units": [ { "amount": "0.000286000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000000286", "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": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "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}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\":\"qwen2-7b-instruct\",\"messages\":[{\"role\":\"user\",\"content\":\"hello\"}],\"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\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"choices\",[]):\n if x.get(\"message\",{}).get(\"content\"): print(x[\"message\"][\"content\"])\nPY_RESULT\n", "label": "curl · retained exact creation", "language": "bash" }, { "code": "from openai import OpenAI\nimport os\n\nif not os.environ.get(\"AILOOT_API_KEY\", \"\").strip(): raise SystemExit(\"Set AILOOT_API_KEY before making a request\")\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.chat.completions.create(model=\"qwen2-7b-instruct\", messages=[{\"role\":\"user\",\"content\":\"hello\"}], stream=False)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem=\"result\"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + \".json\").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b\"-_\", validate=True)\n ext = \"png\" if raw.startswith(b\"\\x89PNG\\r\\n\\x1a\\n\") else \"jpg\" if raw.startswith(b\"\\xff\\xd8\") else \"webp\" if raw[:4] == b\"RIFF\" and raw[8:12] == b\"WEBP\" else suffix\n file = Path(stem + \"-\" + label + \".\" + ext)\n file.write_bytes(raw)\n print(\"Saved\", file)\n for index, block in enumerate(data.get(\"content\") or []):\n print(\"Content\", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get(\"choices\") or []:\n print(\"Choice\", choice.get(\"index\"), json.dumps(choice.get(\"message\", choice.get(\"delta\", {})), ensure_ascii=False))\n for index, item in enumerate(data.get(\"output\") or []):\n print(\"Output\", index, json.dumps(item, ensure_ascii=False))\n if item.get(\"type\") == \"image_generation_call\" and item.get(\"result\"):\n save_blob(item[\"result\"], item.get(\"output_format\") or \"bin\", \"output-\" + str(index))\n for ci, candidate in enumerate(data.get(\"candidates\") or []):\n for pi, part in enumerate((candidate.get(\"content\") or {}).get(\"parts\") or []):\n print(\"Candidate/part\", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get(\"inlineData\") or part.get(\"inline_data\")\n if blob and blob.get(\"data\"):\n mime = blob.get(\"mimeType\") or blob.get(\"mime_type\")\n suffix = {\"image/png\":\"png\", \"image/jpeg\":\"jpg\", \"image/webp\":\"webp\", \"audio/wav\":\"wav\", \"audio/mpeg\":\"mp3\", \"video/mp4\":\"mp4\"}.get(mime, \"bin\")\n save_blob(blob[\"data\"], suffix, \"candidate-\" + str(ci) + \"-part-\" + str(pi))\n\nsave_result(result.to_dict(mode=\"json\"))\n", "label": "Python SDK · all choices", "language": "python" } ], "family": "openai", "fast_multiplier": null, "flex_multiplier": null, "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", "in": 0.286, "input": [ "text" ], "label": "OpenAI Chat", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [ "/chat/completions" ], "out": 0.58, "output": [ "text" ], "path": "/v1/chat/completions", "price_conditions": [ { "label": "Fast mode", "value": "Not specified", "when": "When fast mode is selected" }, { "label": "Flex mode", "value": "Not specified", "when": "When flex mode is selected" }, { "label": "Maximum reasoning effort", "value": "Not specified", "when": "When maximum reasoning effort uses the default adjustment" } ], "pricing": { "billing_mode": "token", "image_input_price": null, "image_output_price": null, "per_request_price": null }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0.000286000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000000286", "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": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "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": "qwen2-7b-instruct", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.000286000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000000286", "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": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "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": "
Al

Qwen2 7B Instruct

Alibaba /qwen2-7b-instruct

Boshlash

Qwen2-7B-Instruct — ko‘p tilli muloqot, dasturlash, matematika va fikr yuritish uchun yo‘riqnomalarga moslab sozlangan matn modeli. Ishlab chiqaruvchi uzun kontekst konfiguratsiyasi yoqilganda 131 072 tokengacha kontekstni hujjatlashtirgan.

Qabul qilinadigan kirish ma’lumotlari
Matn
Natijalar
json · sse
Model haqida

Bu tavsif model imkoniyatlarini yoritadi. Ailoot da tanlangan usul uchun mavjud kirish, chiqish va parametrlar quyida keltirilgan.

Qwen2-7B-Instruct — suhbat va tabiiy tilda ifodalangan vazifalar uchun Qwen2’ning yo‘riqnomalarga moslab sozlangan, 7B hajmdagi versiyasi. U matn kirishi va matn chiqishiga ega model: javob izoh, tarjima, hujjat qoralamasi yoki dastur kodi bo‘lishi mumkin. Ishlab chiqaruvchi tilni tushunish, matn yaratish, ko‘p tilli ish, dasturlash, matematika va fikr yuritishni asosiy foydalanish sohalari deb ko‘rsatadi.

Model tafsilotlari

Qwen2 alohida asosiy va yo‘riqnomalarga moslab sozlangan relizlarga ega. Bu karta Qwen2-7B asosiy modeli yoki oilaning mixture-of-experts relizi emas, Instruct versiyasi haqida. Ishlab chiqaruvchining model ma’lumotlari jadvali 7B hajmdagi model uchun 7,07 milliard parametrni ko‘rsatadi. Uning dekoder Transformeri SwiGLU, diqqat mexanizmidagi QKV bias va guruhlangan so‘rovli diqqatdan, tabiiy tillar va kodga mo‘ljallangan tokenizerdan foydalanadi. Bular arxitektura va ifodalash tanlovlari, qo‘shimcha kirish turlari emas.

Reliz e’lonida ingliz va xitoy tillari hamda qo‘shimcha 27 tilda o‘qitish tasvirlangan. Ular rus, chex va polyak; nemis, fransuz va ispan; arab, fors va turk; yapon va koreys tillari hamda Janubiy va Janubi-sharqiy Osiyodagi boshqa tillarni o‘z ichiga oladi. Shuning uchun ko‘p tilli yordamchi yo‘riqnoma va manba materialini turli tillarda qabul qilishi mumkin, lekin yakuniy artefaktning tili, atamalari va auditoriyasini baribir belgilash tavsiya etiladi.

O‘qitish va Instruct farqi

Dastlabki o‘qitishdan keyin ishlab chiqaruvchi nazoratli nozik sozlash va to‘g‘ridan-to‘g‘ri afzalliklarni optimallashtirishdan foydalanadi. Yo‘riqnomalarga moslab sozlangan versiya suhbatdagi so‘rovga javob berishga mo‘ljallangan, asosiy versiya esa til modeli poydevori sifatida boshqa rolga ega. Model tanlash yoki yo‘riqnoma xatti-harakatini solishtirishda versiya nomini aniq saqlang. Modelni ko‘rsatmalarga rioya qilishga o‘qitish va foydalanishda aniq yo‘riqnoma berish shu jarayonning bir-birini to‘ldiruvchi qismlaridir.

Suhbat kirishini tayyorlash

Model kartasining tezkor boshlash qo‘llanmasi tizim yo‘riqnomasini foydalanuvchi savolidan ajratib, yaratishdan oldin mos chat shablonini qo‘llaydi. Ilova darajasidagi vazifada ham shu amaliy farq foydali: yordamchi roli va javob qoidalarini u ishlashi kerak bo‘lgan manba parchasi yoki koddan alohida belgilang. Hujjat ichida yozilgan matnli yorliq atrofdagi interfeysda to‘g‘ri formatlangan suhbat rollarining o‘rnini bosmaydi.

Texnik izoh vazifasi funksiya va bitta kirish misolini berib, har bir tarmoq nima qilishini va qaysi shartlar natijaga ta’sir etishini so‘rashi mumkin. Dasturlash vazifasi qabul qilinadigan kirish, kutilgan qaytish qiymati va mavjud xatti-harakatni o‘zgartirish cheklovlarini belgilashi mumkin. Amalga oshirishni, keyin odatiy va chegaraviy holat misollarini so‘rang. Bular hujjatlashtirilgan til va dasturlash imkoniyatlarining qo‘llanishlari; kod yaratish matn natijasidir, uni ishga tushirish esa ilova muhitiga tegishli.

Ko‘p tilli tahrir uchun asl parcha, maqsad auditoriya va qisqa atamalar ro‘yxatini bering. Asl nom va shartlarni saqlaydigan tahrirni, so‘ng o‘zgarishlar haqida qisqa izohni so‘rang. Bu matnni yaxshilash so‘rovidan aniqroq: qaysi ma’no saqlanishi va qanday foydali chiqish qaytarilishi kerakligini belgilaydi.

Uzun matnlarni ishlash

Ishlab chiqaruvchi 131 072 tokengacha kontekstni hujjatlashtiradi, lekin uzun kontekst yo‘riqnomalari muhim. 32 768 tokendan oshadigan kirish hujjatlashtirilgan YaRN kengaytirish konfiguratsiyasini talab qiladi. E’lon qilingan misol 32 768 boshlang‘ich pozitsiya sozlamasi bilan 4,0 masshtablash koeffitsiyentidan foydalanadi. Ishlab chiqaruvchi statik kengaytirish sozlamasi qisqaroq matn xatti-harakatiga ta’sir qilishi mumkinligini aytib, uni uzun kontekst amalda kerak bo‘lganda yoqishni tavsiya qiladi.

Uzun hujjat vazifasini aniq savol, nomlangan manba bo‘limlari va ravshan chiqish tuzilmasi bilan tayyorlang. Masalan, texnik spetsifikatsiyaning bir nechta qismini berib, muayyan o‘zgarishga tegishli talablarni so‘rang va har bir topilma yonida manba bo‘limini saqlang. Katta kirish oynasi shu qismlarni birga ushlab turishda foydali; javob baribir butun hujjatni qayta hikoya qilish o‘rniga ixcham jadval bo‘lishi mumkin.

Muhim chegaralar

131 072 token raqami kengaytirilgan kontekst qo‘llab-quvvatlanishidir, uzun kontekst konfiguratsiyasini e’tiborsiz qoldirishga ruxsat emas. Tezkor boshlash misolidagi yaratiladigan 512 token namunaviy sozlama, e’lon qilingan maksimal chiqish imkoniyati emas. Ushbu Instruct relizi matn modeli bo‘lib, Qwen-VL’dan rasm yoki video imkoniyatlarini yoxud Qwen2.5-Math’ning vositalar bilan integratsiyalangan maxsus matematik o‘qitishini meros qilib olmasligi kerak. Nomlangan versiyani tanlang va matnni kerakli muayyan izoh, kod yoki hujjat uchun tayyorlang.

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot Ultra · Anthropic Messages POST /v1/messages
Kirish
text
Chiqish
json, sse
Chiqish formati
JSON yoki SSE
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
qwen2-7b-instruct
Standart—
MaydonmessagesTurmessage[]TalabMajburiyQiymatlar va cheklovlarStandart—
Maydonmessages[].roleTurstringTalabMajburiyQiymatlar va cheklovlar
user, assistant
Standart—
Maydonmessages[].contentTurstringTalabMajburiyQiymatlar va cheklovlarStandart—
Maydonmax_tokensTurintegerTalabMajburiyQiymatlar va cheklovlarStandart—
MaydonstreamTurbooleanTalabIxtiyoriyQiymatlar va cheklovlarStandartfalse
Kirish materiallari birikmalari

Matn · UTF-8 matni

messages[].content satri

Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrcontent_typeParametr talablariapplication/json
ParametrrequiredParametr talablarimodel, messages, max_tokens
Parametroptional_controlsParametr talablaristream
Parametrmaterial_typesParametr talablaritext
Parametrtext_file_materialParametr talablariHaqiqiy UTF-8 hujjatini o‘qing va uning matnini ko‘rsatilgan kirish yoki xabar kontenti satriga joylashtiring.

Hayot sikli: synchronous_or_streamed

Tariflar

Tarif ma’lumotlari so‘rov imkoniyatlarini qo‘shmaydi. Qo‘llab-quvvatlanadigan parametrlar tanlangan usulda ko‘rsatilgan.

Tezkor rejim
Ko‘rsatilmagan Tezkor rejim tanlanganda
Flex rejimi
Ko‘rsatilmagan flex rejimi tanlanganda
Maksimal fikrlash darajasi
Ko‘rsatilmagan Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda

Ailoot tariflari

Sozlamalar bo‘yicha narxlar

SozlamaTariflar jadvaliTo‘lov
SozlamaTokenlar uchun bazaviy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0.000286 / 1K token
Chiqish · Chiqish tokenlari $0.00058 / 1K token
Kirish · Keshdan o‘qish $0 / 1K token
Kirish · Keshga yozish $0 / 1K token
Kirish · Keshga yozish, 1 soat — / 1K token

Ailoot so‘rovdan oldin taxminiy summani band qiladi. Natijadan keyin o‘lchangan sarf asosidagi yakuniy to‘lovni yechadi. Oqimli javob bo‘yicha yakuniy hisob-kitob yakuniy sarf yozuviga asoslanadi. Summani band qilish va to‘lovni hisoblash tartibi →

So‘rov namunalari

Ailoot Ultra · Anthropic Messages

{\n  "model": "qwen2-7b-instruct",\n  "max_tokens": 128,\n  "messages": [\n    {\n      "role": "user",\n      "content": "Summarize the incident report."\n    }\n  ],\n  "stream": false\n}

Natijalarni olish va saqlash

Ailoot Ultra · Anthropic Messages

JSON yoki SSE

Barcha tayyor natijalarni saqlash

Qaytarilgan har bir to‘liq javob, fayl va kontent qismini saqlang.

Barcha kontent bloklari, fikrlash imzolari va haqiqiy vosita ishlatish IDlarini saqlang. SSE bloklarining barcha o‘zgarish qismlarini yig‘ing; birinchi matn bloki to‘liq chiqish emas.

Asl javobni tekshirish kerak bo‘lsa

So‘rov va javob IDlari hamda sarlavhalarini saqlang. Matn vazifasini olish uchun umumiy URL taqdim etilmagan.

Javob maydonlari
MaydonTur
MaydonidTurstring
MaydonmodelTurstring
Maydoncontent[]Turtext|thinking|tool_use|other content block
Maydonstop_reasonTurstring|null
MaydonusageTurobject
Xatolar va keyingi qadamlar
JavobShartKeyingi qadam
Javob400 · invalid_request_errorShartMajburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘riKeyingi qadamYangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.

Taxalluslar

Model taxalluslari

Ailoot Ultra qwen2-7b-instruct

Usul yo‘llari taxalluslari

/chat/completions

Chaqirish uchun tegishli ID lar

qwen2-7b-instruct

", "method_identity": { "effective_request_model_id": "qwen2-7b-instruct", "fmt": "messages", "http": "POST", "path": "/v1/messages", "request_model_id": "qwen2-7b-instruct", "route_label": "Ultra" }, "method_key": "Ultra|messages|/v1/messages|qwen2-7b-instruct", "ordinal": 1, "rawMethod": { "available": null, "cache_read": 0, "cache_write": 0, "cache_write_1h": null, "contract": { "data": { "authentication": { "api_key_source": "AILOOT_API_KEY", "headers": [ "Authorization: Bearer", "x-api-key", "anthropic-version" ], "version_example": "2023-06-01" }, "errors": [ { "code": null, "condition": "Majburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri", "http_status": 400, "recovery": "Yangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.", "type": "invalid_request_error" } ], "example_body": { "max_tokens": 128, "messages": [ { "content": "Summarize the incident report.", "role": "user" } ], "model": "qwen2-7b-instruct", "stream": false }, "lifecycle": { "create": { "http": "POST", "path": "/v1/messages" }, "job_read": null, "mode": "synchronous_or_streamed", "unknown": "So‘rov va javob IDlari hamda sarlavhalarini saqlang. Matn vazifasini olish uchun umumiy URL taqdim etilmagan." }, "material_combinations": [ { "mode": "text", "representation": "messages[].content satri", "required_material": "UTF-8 matni" } ], "operation_modes": [ "synchronous_json", "streamed_SSE" ], "outputs": { "all_results": "Barcha kontent bloklari, fikrlash imzolari va haqiqiy vosita ishlatish IDlarini saqlang. SSE bloklarining barcha o‘zgarish qismlarini yig‘ing; birinchi matn bloki to‘liq chiqish emas.", "family": "protocol", "fields": [ { "description": null, "name": "id", "type": "string" }, { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "content[]", "type": "text|thinking|tool_use|other content block" }, { "description": null, "name": "stop_reason", "type": "string|null" }, { "description": null, "name": "usage", "type": "object" } ], "format": "JSON yoki SSE" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "qwen2-7b-instruct" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages", "required": true, "type": "message[]" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "user", "assistant" ], "enum_known": true, "name": "messages[].role", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].content", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "max_tokens", "required": true, "type": "integer" }, { "availability": "source_interface", "default": false, "default_known": true, "enum": null, "enum_known": false, "name": "stream", "required": false, "type": "boolean" } ], "profile_binding": "Documented current stock Messages bridge; optional model capabilities are separate from this minimal text request.", "request_schema": { "additionalProperties": false, "properties": { "max_tokens": { "minimum": 1, "type": "integer" }, "messages": { "items": { "additionalProperties": false, "properties": { "content": { "oneOf": [ { "type": "string" }, { "items": { "oneOf": [ { "properties": { "text": { "type": "string" }, "type": { "const": "text" } }, "required": [ "type", "text" ], "type": "object" }, { "properties": { "source": { "oneOf": [ { "properties": { "type": { "const": "url" }, "url": { "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "properties": { "data": { "type": "string" }, "media_type": { "type": "string" }, "type": { "const": "base64" } }, "required": [ "type", "media_type", "data" ], "type": "object" } ] }, "type": { "const": "image" } }, "required": [ "type", "source" ], "type": "object" } ] }, "type": "array" } ] }, "role": { "enum": [ "user", "assistant" ], "type": "string" } }, "required": [ "role", "content" ], "type": "object" }, "minItems": 1, "type": "array" }, "model": { "enum": [ "qwen2-7b-instruct" ], "type": "string" }, "stream": { "default": false, "type": "boolean" } }, "required": [ "model", "messages", "max_tokens" ], "type": "object" }, "schema_template": { "max_tokens": 128, "messages": [ { "content": "Summarize the incident report.", "role": "user" } ], "model": "qwen2-7b-instruct", "stream": false }, "unsupported": [ "No query API key.", "Hosted/server-side tools and model-specific optional controls are not part of this minimal family example." ] }, "input": { "content_type": "application/json", "material_types": [ "text" ], "optional_controls": [ "stream" ], "required": [ "model", "messages", "max_tokens" ], "text_file_material": "Haqiqiy UTF-8 hujjatini o‘qing va uning matnini ko‘rsatilgan kirish yoki xabar kontenti satriga joylashtiring." }, "lifecycle": { "create": { "http": "POST", "path": "/v1/messages" }, "job_read": null, "mode": "synchronous_or_streamed", "unknown": "Keep request/response IDs and headers. No generic text job retrieval URL is supplied." }, "manufacturer_capability": null, "method": { "http": "POST", "path": "/v1/messages" }, "output": { "all_results": "Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.", "family": "protocol", "format": "JSON or SSE" } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Tokenlar uchun bazaviy tariflar", "units": [ { "amount": "0.000286000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000000286", "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": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "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": "{\n \"model\": \"qwen2-7b-instruct\",\n \"max_tokens\": 128,\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"Summarize the incident report.\"\n }\n ],\n \"stream\": false\n}", "label": "Complete minimal JSON body", "language": "json" }, { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}\"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'qwen2-7b-instruct', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H \"x-api-key: $AILOOT_API_KEY\" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\n", "label": "curl · use your UTF-8 document", "language": "bash" }, { "code": "import json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'qwen2-7b-instruct', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\n", "label": "Python · save the complete result", "language": "python" } ], "family": "anthropic", "fast_multiplier": null, "flex_multiplier": null, "fmt": "messages", "http": "POST", "in": 0.286, "input": [ "text" ], "label": "Anthropic Messages", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [], "out": 0.58, "output": [ "json", "sse" ], "path": "/v1/messages", "price_conditions": [ { "label": "Fast mode", "value": "Not specified", "when": "When fast mode is selected" }, { "label": "Flex mode", "value": "Not specified", "when": "When flex mode is selected" }, { "label": "Maximum reasoning effort", "value": "Not specified", "when": "When maximum reasoning effort uses the default adjustment" } ], "pricing": { "billing_mode": "token", "image_input_price": null, "image_output_price": null, "per_request_price": null }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0.000286000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000000286", "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": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "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": "qwen2-7b-instruct", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.000286000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000000286", "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": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "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": "qwen2-7b-instruct", "nativeProvenance": { "input_facts_canonical_sha256": "39dbd97b5e02da01801697b34941b10169361a104756dc543f049f7da68ec90a", "input_html_sha256": "0498b254a1b1ece767f00092bdc57c882ff2a6cfbd5965448fab7f699461f36f", "private_audit_sha256": "5b35c484ff0308807836c73ef2adc8cba6df8046be3aed5a19550b3d4ac1bbf4", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/uz/qwen2-7b-instruct/", "description": "Qwen2-7B-Instruct — ko‘p tilli muloqot, dasturlash, matematika va fikr yuritish uchun yo‘riqnomalarga moslab sozlangan matn modeli. Ishlab chiqaruvchi uzun kontekst konfiguratsiyasi yoqilganda 131 072 tokengacha kontekstni hujjatlashtirgan.", "locale": "uz", "md_url": "https://ailoot.ai/models/uz/qwen2-7b-instruct/index.md", "title": "Qwen2 7B Instruct — Ailoot", "url": "https://ailoot.ai/models/uz/qwen2-7b-instruct/" }, "rawModel": { "about": "Qwen2-7B-Instruct — suhbat va tabiiy tilda ifodalangan vazifalar uchun Qwen2’ning yo‘riqnomalarga moslab sozlangan, 7B hajmdagi versiyasi. U matn kirishi va matn chiqishiga ega model: javob izoh, tarjima, hujjat qoralamasi yoki dastur kodi bo‘lishi mumkin. Ishlab chiqaruvchi tilni tushunish, matn yaratish, ko‘p tilli ish, dasturlash, matematika va fikr yuritishni asosiy foydalanish sohalari deb ko‘rsatadi.\n\nModel tafsilotlari\n\nQwen2 alohida asosiy va yo‘riqnomalarga moslab sozlangan relizlarga ega. Bu karta Qwen2-7B asosiy modeli yoki oilaning mixture-of-experts relizi emas, Instruct versiyasi haqida. Ishlab chiqaruvchining model ma’lumotlari jadvali 7B hajmdagi model uchun 7,07 milliard parametrni ko‘rsatadi. Uning dekoder Transformeri SwiGLU, diqqat mexanizmidagi QKV bias va guruhlangan so‘rovli diqqatdan, tabiiy tillar va kodga mo‘ljallangan tokenizerdan foydalanadi. Bular arxitektura va ifodalash tanlovlari, qo‘shimcha kirish turlari emas.\n\nReliz e’lonida ingliz va xitoy tillari hamda qo‘shimcha 27 tilda o‘qitish tasvirlangan. Ular rus, chex va polyak; nemis, fransuz va ispan; arab, fors va turk; yapon va koreys tillari hamda Janubiy va Janubi-sharqiy Osiyodagi boshqa tillarni o‘z ichiga oladi. Shuning uchun ko‘p tilli yordamchi yo‘riqnoma va manba materialini turli tillarda qabul qilishi mumkin, lekin yakuniy artefaktning tili, atamalari va auditoriyasini baribir belgilash tavsiya etiladi.\n\nO‘qitish va Instruct farqi\n\nDastlabki o‘qitishdan keyin ishlab chiqaruvchi nazoratli nozik sozlash va to‘g‘ridan-to‘g‘ri afzalliklarni optimallashtirishdan foydalanadi. Yo‘riqnomalarga moslab sozlangan versiya suhbatdagi so‘rovga javob berishga mo‘ljallangan, asosiy versiya esa til modeli poydevori sifatida boshqa rolga ega. Model tanlash yoki yo‘riqnoma xatti-harakatini solishtirishda versiya nomini aniq saqlang. Modelni ko‘rsatmalarga rioya qilishga o‘qitish va foydalanishda aniq yo‘riqnoma berish shu jarayonning bir-birini to‘ldiruvchi qismlaridir.\n\nSuhbat kirishini tayyorlash\n\nModel kartasining tezkor boshlash qo‘llanmasi tizim yo‘riqnomasini foydalanuvchi savolidan ajratib, yaratishdan oldin mos chat shablonini qo‘llaydi. Ilova darajasidagi vazifada ham shu amaliy farq foydali: yordamchi roli va javob qoidalarini u ishlashi kerak bo‘lgan manba parchasi yoki koddan alohida belgilang. Hujjat ichida yozilgan matnli yorliq atrofdagi interfeysda to‘g‘ri formatlangan suhbat rollarining o‘rnini bosmaydi.\n\nTexnik izoh vazifasi funksiya va bitta kirish misolini berib, har bir tarmoq nima qilishini va qaysi shartlar natijaga ta’sir etishini so‘rashi mumkin. Dasturlash vazifasi qabul qilinadigan kirish, kutilgan qaytish qiymati va mavjud xatti-harakatni o‘zgartirish cheklovlarini belgilashi mumkin. Amalga oshirishni, keyin odatiy va chegaraviy holat misollarini so‘rang. Bular hujjatlashtirilgan til va dasturlash imkoniyatlarining qo‘llanishlari; kod yaratish matn natijasidir, uni ishga tushirish esa ilova muhitiga tegishli.\n\nKo‘p tilli tahrir uchun asl parcha, maqsad auditoriya va qisqa atamalar ro‘yxatini bering. Asl nom va shartlarni saqlaydigan tahrirni, so‘ng o‘zgarishlar haqida qisqa izohni so‘rang. Bu matnni yaxshilash so‘rovidan aniqroq: qaysi ma’no saqlanishi va qanday foydali chiqish qaytarilishi kerakligini belgilaydi.\n\nUzun matnlarni ishlash\n\nIshlab chiqaruvchi 131 072 tokengacha kontekstni hujjatlashtiradi, lekin uzun kontekst yo‘riqnomalari muhim. 32 768 tokendan oshadigan kirish hujjatlashtirilgan YaRN kengaytirish konfiguratsiyasini talab qiladi. E’lon qilingan misol 32 768 boshlang‘ich pozitsiya sozlamasi bilan 4,0 masshtablash koeffitsiyentidan foydalanadi. Ishlab chiqaruvchi statik kengaytirish sozlamasi qisqaroq matn xatti-harakatiga ta’sir qilishi mumkinligini aytib, uni uzun kontekst amalda kerak bo‘lganda yoqishni tavsiya qiladi.\n\nUzun hujjat vazifasini aniq savol, nomlangan manba bo‘limlari va ravshan chiqish tuzilmasi bilan tayyorlang. Masalan, texnik spetsifikatsiyaning bir nechta qismini berib, muayyan o‘zgarishga tegishli talablarni so‘rang va har bir topilma yonida manba bo‘limini saqlang. Katta kirish oynasi shu qismlarni birga ushlab turishda foydali; javob baribir butun hujjatni qayta hikoya qilish o‘rniga ixcham jadval bo‘lishi mumkin.\n\nMuhim chegaralar\n\n131 072 token raqami kengaytirilgan kontekst qo‘llab-quvvatlanishidir, uzun kontekst konfiguratsiyasini e’tiborsiz qoldirishga ruxsat emas. Tezkor boshlash misolidagi yaratiladigan 512 token namunaviy sozlama, e’lon qilingan maksimal chiqish imkoniyati emas. Ushbu Instruct relizi matn modeli bo‘lib, Qwen-VL’dan rasm yoki video imkoniyatlarini yoxud Qwen2.5-Math’ning vositalar bilan integratsiyalangan maxsus matematik o‘qitishini meros qilib olmasligi kerak. Nomlangan versiyani tanlang va matnni kerakli muayyan izoh, kod yoki hujjat uchun tayyorlang.", "alias_of": null, "aliases": [], "author": "Alibaba", "available": null, "canonical_model": "qwen2-7b-instruct", "capabilities": { "context_length": 32000, "manufacturer_input": [], "manufacturer_output": [] }, "card_routes": [ { "label": "Ultra", "rates": [ { "amount": "0.000286000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000000286", "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": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000058", "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": "Konfiguratsiya narxlari tafsilotlarda to‘liq ko‘rsatilgan." } ], "ctx": null, "display_name": "Qwen2 7B Instruct", "entity_type": "model", "formats": [ "chat", "messages" ], "id": "qwen2-7b-instruct", "input": [ "text" ], "kind": "text", "max_out": null, "methods": [ { "available": null, "cache_read": 0, "cache_write": 0, "cache_write_1h": null, "contract": { "data": { "all_results": "Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.", "authentication": { "api_key_source": "AILOOT_API_KEY", "header": "Authorization: Bearer" }, "errors": [ { "code": null, "condition": "Majburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri", "http_status": 400, "recovery": "Yangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.", "type": "invalid_request_error" } ], "hints": [ "Model context is not an output cap.", "Send the conversation as text messages. Stream must be a boolean; omitted stream defaults to false." ], "ignored_fields": [], "lifecycle": { "create": { "http": "POST", "path": "/v1/chat/completions" }, "job_read": null, "mode": "synchronous_or_streamed", "unknown": "So‘rov va javob IDlari hamda sarlavhalarini saqlang. Matn vazifasini olish uchun umumiy URL taqdim etilmagan." }, "material_combinations": [ { "mode": "text", "representation": "messages[].content", "required_material": "UTF8 matni" } ], "operation_modes": [ "conversation" ], "outputs": { "all_results": "Har bir javob variantini va uning kontenti, vosita chaqiruvlari hamda fikrlash ma’lumotlarini saqlang. SSEda o‘zgarish qismlarini variant va vosita indeksi bo‘yicha yig‘ing, yakuniy tugash sababi va sarf ma’lumotlarini saqlang.", "family": "text", "fields": [ { "description": null, "name": "choices[].index", "type": "integer" }, { "description": null, "name": "choices[].message.content", "type": "string|null" }, { "description": null, "name": "choices[].message.tool_calls", "type": "tool_call[]" }, { "description": null, "name": "choices[].message.reasoning_content", "type": "string" }, { "description": null, "name": "choices[].message.reasoning_details", "type": "object|array" }, { "description": null, "name": "choices[].finish_reason", "type": "string|null" }, { "description": null, "name": "usage", "type": "object" } ], "format": "json yoki SSE" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "qwen2-7b-instruct" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "min_items": 1, "name": "messages", "required": true, "type": "message[]" }, { "availability": "model_dependent", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].role", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].content", "required": false, "type": "string", "when": "Matnli suhbat" }, { "availability": "source_interface", "default": false, "default_known": true, "enum": null, "enum_known": false, "name": "stream", "required": false, "type": "boolean" } ], "profile_binding": "Text conversation at the exact model ID and method path.", "unsupported": [ "additional models/models list", "plugins", "route", "hosted tools; ordinary client function tools remain separate", "non-text output modalities", "provider.options overrides", "one-hour prompt-cache TTL" ] }, "hints": [ "Aniq model IDsini messages bilan yuboring.", "Kontekst uzunligi chiqishning yuqori chegarasi emas.", "Matn fayli aynan matnni taqdim etishi mumkin; tasvir, audio va fayl obyekti kirishlari aniq bog‘lashni talab qiladi." ], "input": { "content_type": "application/json", "control_scope": "Matnli suhbat; stream mantiqiy qiymat bo‘lib, standart holatda false.", "file_material": "Haqiqiy UTF-8 hujjati aynan matn sifatida kiritiladi; fayl IDsi faraz qilinmaydi", "hosted_operations": false, "material_types": [ "text" ], "messages": "role/content tuzilishidagi xabarlar massivi", "multimodal_input_binding": null, "optional_controls": [ "stream" ], "output_modalities": [ "text" ], "required": [ "model", "messages" ] }, "lifecycle": { "mode": "synchronous_or_streamed", "unknown": "preserve original request id; no generic chat job retrieval path" }, "manufacturer_capability": null, "method": { "http": "POST", "path": "/v1/chat/completions" }, "method_aliases": [ "/chat/completions" ], "operation_modes": [ "text_chat" ], "output": { "content": "choices[].message.content or stream delta", "family": "text", "format": "json or supported SSE", "tool_calls": "retain actual returned function calls" } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Tokenlar uchun bazaviy tariflar", "units": [ { "amount": "0.000286000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000000286", "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": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "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}\"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\":\"qwen2-7b-instruct\",\"messages\":[{\"role\":\"user\",\"content\":\"hello\"}],\"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\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path(\"result.json\").read_text())\nfor x in r.get(\"choices\",[]):\n if x.get(\"message\",{}).get(\"content\"): print(x[\"message\"][\"content\"])\nPY_RESULT\n", "label": "curl · retained exact creation", "language": "bash" }, { "code": "from openai import OpenAI\nimport os\n\nif not os.environ.get(\"AILOOT_API_KEY\", \"\").strip(): raise SystemExit(\"Set AILOOT_API_KEY before making a request\")\nclient=OpenAI(base_url=\"https://api.ailoot.ai/v1\",api_key=os.environ[\"AILOOT_API_KEY\"],max_retries=0)\nresult=client.chat.completions.create(model=\"qwen2-7b-instruct\", messages=[{\"role\":\"user\",\"content\":\"hello\"}], stream=False)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem=\"result\"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + \".json\").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding=\"utf-8\")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b\"-_\", validate=True)\n ext = \"png\" if raw.startswith(b\"\\x89PNG\\r\\n\\x1a\\n\") else \"jpg\" if raw.startswith(b\"\\xff\\xd8\") else \"webp\" if raw[:4] == b\"RIFF\" and raw[8:12] == b\"WEBP\" else suffix\n file = Path(stem + \"-\" + label + \".\" + ext)\n file.write_bytes(raw)\n print(\"Saved\", file)\n for index, block in enumerate(data.get(\"content\") or []):\n print(\"Content\", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get(\"choices\") or []:\n print(\"Choice\", choice.get(\"index\"), json.dumps(choice.get(\"message\", choice.get(\"delta\", {})), ensure_ascii=False))\n for index, item in enumerate(data.get(\"output\") or []):\n print(\"Output\", index, json.dumps(item, ensure_ascii=False))\n if item.get(\"type\") == \"image_generation_call\" and item.get(\"result\"):\n save_blob(item[\"result\"], item.get(\"output_format\") or \"bin\", \"output-\" + str(index))\n for ci, candidate in enumerate(data.get(\"candidates\") or []):\n for pi, part in enumerate((candidate.get(\"content\") or {}).get(\"parts\") or []):\n print(\"Candidate/part\", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get(\"inlineData\") or part.get(\"inline_data\")\n if blob and blob.get(\"data\"):\n mime = blob.get(\"mimeType\") or blob.get(\"mime_type\")\n suffix = {\"image/png\":\"png\", \"image/jpeg\":\"jpg\", \"image/webp\":\"webp\", \"audio/wav\":\"wav\", \"audio/mpeg\":\"mp3\", \"video/mp4\":\"mp4\"}.get(mime, \"bin\")\n save_blob(blob[\"data\"], suffix, \"candidate-\" + str(ci) + \"-part-\" + str(pi))\n\nsave_result(result.to_dict(mode=\"json\"))\n", "label": "Python SDK · all choices", "language": "python" } ], "family": "openai", "fast_multiplier": null, "flex_multiplier": null, "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", "in": 0.286, "input": [ "text" ], "label": "OpenAI Chat", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [ "/chat/completions" ], "out": 0.58, "output": [ "text" ], "path": "/v1/chat/completions", "price_conditions": [ { "label": "Fast mode", "value": "Not specified", "when": "When fast mode is selected" }, { "label": "Flex mode", "value": "Not specified", "when": "When flex mode is selected" }, { "label": "Maximum reasoning effort", "value": "Not specified", "when": "When maximum reasoning effort uses the default adjustment" } ], "pricing": { "billing_mode": "token", "image_input_price": null, "image_output_price": null, "per_request_price": null }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0.000286000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000000286", "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": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "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": "qwen2-7b-instruct", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.000286000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000000286", "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": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "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": 0, "cache_write": 0, "cache_write_1h": null, "contract": { "data": { "authentication": { "api_key_source": "AILOOT_API_KEY", "headers": [ "Authorization: Bearer", "x-api-key", "anthropic-version" ], "version_example": "2023-06-01" }, "errors": [ { "code": null, "condition": "Majburiy maydonlar, material ifodasi yoki parametr qiymati noto‘g‘ri", "http_status": 400, "recovery": "Yangi yaratish so‘rovidan oldin ko‘rsatilgan maydonlarni tuzating.", "type": "invalid_request_error" } ], "example_body": { "max_tokens": 128, "messages": [ { "content": "Summarize the incident report.", "role": "user" } ], "model": "qwen2-7b-instruct", "stream": false }, "lifecycle": { "create": { "http": "POST", "path": "/v1/messages" }, "job_read": null, "mode": "synchronous_or_streamed", "unknown": "So‘rov va javob IDlari hamda sarlavhalarini saqlang. Matn vazifasini olish uchun umumiy URL taqdim etilmagan." }, "material_combinations": [ { "mode": "text", "representation": "messages[].content satri", "required_material": "UTF-8 matni" } ], "operation_modes": [ "synchronous_json", "streamed_SSE" ], "outputs": { "all_results": "Barcha kontent bloklari, fikrlash imzolari va haqiqiy vosita ishlatish IDlarini saqlang. SSE bloklarining barcha o‘zgarish qismlarini yig‘ing; birinchi matn bloki to‘liq chiqish emas.", "family": "protocol", "fields": [ { "description": null, "name": "id", "type": "string" }, { "description": null, "name": "model", "type": "string" }, { "description": null, "name": "content[]", "type": "text|thinking|tool_use|other content block" }, { "description": null, "name": "stop_reason", "type": "string|null" }, { "description": null, "name": "usage", "type": "object" } ], "format": "JSON yoki SSE" }, "parameters": [ { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "qwen2-7b-instruct" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages", "required": true, "type": "message[]" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": [ "user", "assistant" ], "enum_known": true, "name": "messages[].role", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "messages[].content", "required": true, "type": "string" }, { "availability": "source_interface", "default": null, "default_known": false, "enum": null, "enum_known": false, "name": "max_tokens", "required": true, "type": "integer" }, { "availability": "source_interface", "default": false, "default_known": true, "enum": null, "enum_known": false, "name": "stream", "required": false, "type": "boolean" } ], "profile_binding": "Documented current stock Messages bridge; optional model capabilities are separate from this minimal text request.", "request_schema": { "additionalProperties": false, "properties": { "max_tokens": { "minimum": 1, "type": "integer" }, "messages": { "items": { "additionalProperties": false, "properties": { "content": { "oneOf": [ { "type": "string" }, { "items": { "oneOf": [ { "properties": { "text": { "type": "string" }, "type": { "const": "text" } }, "required": [ "type", "text" ], "type": "object" }, { "properties": { "source": { "oneOf": [ { "properties": { "type": { "const": "url" }, "url": { "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "properties": { "data": { "type": "string" }, "media_type": { "type": "string" }, "type": { "const": "base64" } }, "required": [ "type", "media_type", "data" ], "type": "object" } ] }, "type": { "const": "image" } }, "required": [ "type", "source" ], "type": "object" } ] }, "type": "array" } ] }, "role": { "enum": [ "user", "assistant" ], "type": "string" } }, "required": [ "role", "content" ], "type": "object" }, "minItems": 1, "type": "array" }, "model": { "enum": [ "qwen2-7b-instruct" ], "type": "string" }, "stream": { "default": false, "type": "boolean" } }, "required": [ "model", "messages", "max_tokens" ], "type": "object" }, "schema_template": { "max_tokens": 128, "messages": [ { "content": "Summarize the incident report.", "role": "user" } ], "model": "qwen2-7b-instruct", "stream": false }, "unsupported": [ "No query API key.", "Hosted/server-side tools and model-specific optional controls are not part of this minimal family example." ] }, "input": { "content_type": "application/json", "material_types": [ "text" ], "optional_controls": [ "stream" ], "required": [ "model", "messages", "max_tokens" ], "text_file_material": "Haqiqiy UTF-8 hujjatini o‘qing va uning matnini ko‘rsatilgan kirish yoki xabar kontenti satriga joylashtiring." }, "lifecycle": { "create": { "http": "POST", "path": "/v1/messages" }, "job_read": null, "mode": "synchronous_or_streamed", "unknown": "Keep request/response IDs and headers. No generic text job retrieval URL is supplied." }, "manufacturer_capability": null, "method": { "http": "POST", "path": "/v1/messages" }, "output": { "all_results": "Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.", "family": "protocol", "format": "JSON or SSE" } }, "ctx": null, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Tokenlar uchun bazaviy tariflar", "units": [ { "amount": "0.000286000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000000286", "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": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "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": "{\n \"model\": \"qwen2-7b-instruct\",\n \"max_tokens\": 128,\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"Summarize the incident report.\"\n }\n ],\n \"stream\": false\n}", "label": "Complete minimal JSON body", "language": "json" }, { "code": "set -eu\n: \"${AILOOT_API_KEY:?Set your Ailoot API key}\"\n: \"${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}\"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'qwen2-7b-instruct', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H \"x-api-key: $AILOOT_API_KEY\" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\n", "label": "curl · use your UTF-8 document", "language": "bash" }, { "code": "import json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'qwen2-7b-instruct', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\n", "label": "Python · save the complete result", "language": "python" } ], "family": "anthropic", "fast_multiplier": null, "flex_multiplier": null, "fmt": "messages", "http": "POST", "in": 0.286, "input": [ "text" ], "label": "Anthropic Messages", "max_out": null, "max_reasoning_effort_multiplier": null, "method_aliases": [], "out": 0.58, "output": [ "json", "sse" ], "path": "/v1/messages", "price_conditions": [ { "label": "Fast mode", "value": "Not specified", "when": "When fast mode is selected" }, { "label": "Flex mode", "value": "Not specified", "when": "When flex mode is selected" }, { "label": "Maximum reasoning effort", "value": "Not specified", "when": "When maximum reasoning effort uses the default adjustment" } ], "pricing": { "billing_mode": "token", "image_input_price": null, "image_output_price": null, "per_request_price": null }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0.000286000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000000286", "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": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "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": "qwen2-7b-instruct", "route_label": "Ultra", "status_reads": [], "tiers": [], "units": [ { "amount": "0.000286000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000000286", "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": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "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": "qwen2-7b-instruct", "request_model_id": "qwen2-7b-instruct", "route_label": "Ultra" } ], "name": "Pro/Qwen/Qwen2-7B-Instruct", "price": { "in": 0.286, "in_label": null, "out": 0.58, "out_label": null, "scope_label": "Token rates", "sort_in": 0.286, "sort_out": 0.58, "varies": false }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "OpenAI Chat · Base token rates", "units": [ { "amount": "0.000286000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000000286", "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": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "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" } ] }, { "dimensions": { "regime": "base" }, "label": "Anthropic Messages · Base token rates", "units": [ { "amount": "0.000286000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0.000000286", "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": "0.00058000", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0.00000058", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_read_price", "label": "Cache read", "native_amount_usd_per_unit": "0", "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": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "cache_write_price", "label": "Cache write", "native_amount_usd_per_unit": "0", "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" } ] } ], "public_id": "qwen2-7b-instruct", "short": "Qwen2-7B-Instruct — ko‘p tilli muloqot, dasturlash, matematika va fikr yuritish uchun yo‘riqnomalarga moslab sozlangan matn modeli. Ishlab chiqaruvchi uzun kontekst konfiguratsiyasi yoqilganda 131 072 tokengacha kontekstni hujjatlashtirgan." }, "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": "Qwen2-7B-Instruct — ko‘p tilli muloqot, dasturlash, matematika va fikr yuritish uchun yo‘riqnomalarga moslab sozlangan matn modeli. Ishlab chiqaruvchi uzun kontekst konfiguratsiyasi yoqilganda 131 072 tokengacha kontekstni hujjatlashtirgan.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "2830cbb70e88eddbed7c83c4bcee5ce5dbee251ac5e92f16c7211c171fd86161", "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6" }, "sourceRevision": "daf8932283183bf6a9c796503e33a7f5700f10dc5430752aa0ffabe9e39c0f75" }