{ "aboutHtml": "

qwen2-0.5b-instruct — ushbu Tongyi Qianwen matnli suhbat profili uchun aniq model tanlash qiymati. Ko‘rsatmalar va manba materialini matn sifatida yuboring va aniq yozma natija so‘rang. Amaliy prompt lar mijoz xabarini taklif etilgan javobga aylantirishi, yig‘ilish qaydlarini qayta tartiblashi yoki berilgan parchadan kichik faktlar to‘plamini ajratib olishi mumkin. Ilova foydalanadigan aniq natija bo‘lishi uchun auditoriya va kerakli javob tuzilmasini belgilang.

model ni qwen2-0.5b-instruct qilib va bo‘sh bo‘lmagan messages massivi bilan POST /v1/chat/completions dan foydalaning. Har bir xabar role va matnli content ni beradi. Keyingi savol suhbatga tayansa, tegishli oldingi xabarlarni kiriting. Hujjat bilan ishlash uchun hujjatdan tegishli UTF-8 matnini o‘qing va aynan shu haqiqiy matnni xabarga joylang. Javob bir nechta parchani ajratishi kerak bo‘lsa, har bir parcha bilan bo‘lim sarlavhasi yoki manba belgisini saqlang.

Matnli kirish hajmi 32 000 token. Ko‘rsatmalar, manba parchalari va suhbat tarixini shu kirish hajmi ichida saqlang. Uzun hujjat uchun joriy savolga kerakli bo‘limlarni tanlang yoki aniq belgilangan parchalarni alohida vazifalar sifatida ishlang. Javob qaysi parchadan foydalanishi kerakligini va so‘ralgan maydon unda bo‘lmasa nima qaytarilishini ayting.

Javob loyihasi vazifasi uchun mijozning so‘zlarini tasdiqlangan mahsulot ma’lumotlari bilan birga bering va aniq keyingi qadamli javob so‘rang. Ajratib olish uchun muammo, so‘ralgan amal va keltirilgan dalil kabi maydonlarni sanang, keyin modeldan faqat berilgan matnda topilgan faktlarni qaytarishni so‘rang. Kod tushuntirishi uchun funksiyani joylang va uning kirishlari, o‘zgartirishlari hamda chiqishi tavsifini so‘rang. Bu ko‘rsatmalar prompt ichida vazifani belgilaydi va natijani siz bergan materialga bog‘lab turadi.

Ixtiyoriy mantiqiy stream boshqaruvi javob yetkazilishini tanlaydi: standart stream:false yig‘ilgan JSON javobni qaytaradi; stream:true javob yaratilishi davomida hodisalarni yetkazadi. JSON uchun qaytarilgan choices va xabar mazmunini o‘qing va berilgan bo‘lsa usage ni saqlang. Oqimli yetkazishda javobni to‘liq deb saqlashdan avval har bir choice uchun content deltalarini yig‘ing va yakuniy finish ma’lumotlarini saqlang. So‘rov noaniq tugasa, dastlabki javob va so‘rov identifikatorini saqlang.

", "aboutText": "qwen2-0.5b-instruct — ushbu Tongyi Qianwen matnli suhbat profili uchun aniq model tanlash qiymati. Ko‘rsatmalar va manba materialini matn sifatida yuboring va aniq yozma natija so‘rang. Amaliy prompt lar mijoz xabarini taklif etilgan javobga aylantirishi, yig‘ilish qaydlarini qayta tartiblashi yoki berilgan parchadan kichik faktlar to‘plamini ajratib olishi mumkin. Ilova foydalanadigan aniq natija bo‘lishi uchun auditoriya va kerakli javob tuzilmasini belgilang.\n\nmodel ni qwen2-0.5b-instruct qilib va bo‘sh bo‘lmagan messages massivi bilan POST /v1/chat/completions dan foydalaning. Har bir xabar role va matnli content ni beradi. Keyingi savol suhbatga tayansa, tegishli oldingi xabarlarni kiriting. Hujjat bilan ishlash uchun hujjatdan tegishli UTF-8 matnini o‘qing va aynan shu haqiqiy matnni xabarga joylang. Javob bir nechta parchani ajratishi kerak bo‘lsa, har bir parcha bilan bo‘lim sarlavhasi yoki manba belgisini saqlang.\n\nMatnli kirish hajmi 32 000 token. Ko‘rsatmalar, manba parchalari va suhbat tarixini shu kirish hajmi ichida saqlang. Uzun hujjat uchun joriy savolga kerakli bo‘limlarni tanlang yoki aniq belgilangan parchalarni alohida vazifalar sifatida ishlang. Javob qaysi parchadan foydalanishi kerakligini va so‘ralgan maydon unda bo‘lmasa nima qaytarilishini ayting.\n\nJavob loyihasi vazifasi uchun mijozning so‘zlarini tasdiqlangan mahsulot ma’lumotlari bilan birga bering va aniq keyingi qadamli javob so‘rang. Ajratib olish uchun muammo, so‘ralgan amal va keltirilgan dalil kabi maydonlarni sanang, keyin modeldan faqat berilgan matnda topilgan faktlarni qaytarishni so‘rang. Kod tushuntirishi uchun funksiyani joylang va uning kirishlari, o‘zgartirishlari hamda chiqishi tavsifini so‘rang. Bu ko‘rsatmalar prompt ichida vazifani belgilaydi va natijani siz bergan materialga bog‘lab turadi.\n\nIxtiyoriy mantiqiy stream boshqaruvi javob yetkazilishini tanlaydi: standart stream:false yig‘ilgan JSON javobni qaytaradi; stream:true javob yaratilishi davomida hodisalarni yetkazadi. JSON uchun qaytarilgan choices va xabar mazmunini o‘qing va berilgan bo‘lsa usage ni saqlang. Oqimli yetkazishda javobni to‘liq deb saqlashdan avval har bir choice uchun content deltalarini yig‘ing va yakuniy finish ma’lumotlarini saqlang. So‘rov noaniq tugasa, dastlabki javob va so‘rov identifikatorini saqlang.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "482e0f447c38115b8e28017648c00ff6f62e8823325156f677cf463e3649144c", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "uz", "methods": [ { "html": "
Al

qwen2-0.5b-instruct

Alibaba /qwen2-0.5b-instruct

Boshlash

qwen2-0.5b-instruct — xabarlarga asoslangan so‘rovlar uchun Tongyi Qianwen matn modeli. Berilgan parchalar bilan ishlashda uning aniq ID qiymatidan Chat Completions bilan foydalaning: javob loyihasini yozing, qaydlarni tekshiruv ro‘yxatiga aylantiring, nomlangan maydonlarni ajratib oling yoki qisqa kod parchasini tushuntiring. Uning matnli kirish hajmi 32 000 token.

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.

qwen2-0.5b-instruct — ushbu Tongyi Qianwen matnli suhbat profili uchun aniq model tanlash qiymati. Ko‘rsatmalar va manba materialini matn sifatida yuboring va aniq yozma natija so‘rang. Amaliy prompt lar mijoz xabarini taklif etilgan javobga aylantirishi, yig‘ilish qaydlarini qayta tartiblashi yoki berilgan parchadan kichik faktlar to‘plamini ajratib olishi mumkin. Ilova foydalanadigan aniq natija bo‘lishi uchun auditoriya va kerakli javob tuzilmasini belgilang.

model ni qwen2-0.5b-instruct qilib va bo‘sh bo‘lmagan messages massivi bilan POST /v1/chat/completions dan foydalaning. Har bir xabar role va matnli content ni beradi. Keyingi savol suhbatga tayansa, tegishli oldingi xabarlarni kiriting. Hujjat bilan ishlash uchun hujjatdan tegishli UTF-8 matnini o‘qing va aynan shu haqiqiy matnni xabarga joylang. Javob bir nechta parchani ajratishi kerak bo‘lsa, har bir parcha bilan bo‘lim sarlavhasi yoki manba belgisini saqlang.

Matnli kirish hajmi 32 000 token. Ko‘rsatmalar, manba parchalari va suhbat tarixini shu kirish hajmi ichida saqlang. Uzun hujjat uchun joriy savolga kerakli bo‘limlarni tanlang yoki aniq belgilangan parchalarni alohida vazifalar sifatida ishlang. Javob qaysi parchadan foydalanishi kerakligini va so‘ralgan maydon unda bo‘lmasa nima qaytarilishini ayting.

Javob loyihasi vazifasi uchun mijozning so‘zlarini tasdiqlangan mahsulot ma’lumotlari bilan birga bering va aniq keyingi qadamli javob so‘rang. Ajratib olish uchun muammo, so‘ralgan amal va keltirilgan dalil kabi maydonlarni sanang, keyin modeldan faqat berilgan matnda topilgan faktlarni qaytarishni so‘rang. Kod tushuntirishi uchun funksiyani joylang va uning kirishlari, o‘zgartirishlari hamda chiqishi tavsifini so‘rang. Bu ko‘rsatmalar prompt ichida vazifani belgilaydi va natijani siz bergan materialga bog‘lab turadi.

Ixtiyoriy mantiqiy stream boshqaruvi javob yetkazilishini tanlaydi: standart stream:false yig‘ilgan JSON javobni qaytaradi; stream:true javob yaratilishi davomida hodisalarni yetkazadi. JSON uchun qaytarilgan choices va xabar mazmunini o‘qing va berilgan bo‘lsa usage ni saqlang. Oqimli yetkazishda javobni to‘liq deb saqlashdan avval har bir choice uchun content deltalarini yig‘ing va yakuniy finish ma’lumotlarini saqlang. So‘rov noaniq tugasa, dastlabki javob va so‘rov identifikatorini saqlang.

Ailoot da mavjud · kirish ma’lumotlari va usullar

Ailoot Ultra · Chat Completions POST /v1/chat/completions
Kirish
text
Chiqish
text
Kontekst
32 000 tokens
Chiqish formati
JSON or supported SSE
MaydonTurTalabQiymatlar va cheklovlarStandart
MaydonmodelTurstringTalabMajburiyQiymatlar va cheklovlar
qwen2-0.5b-instruct
Standart—
MaydonmessagesTurarrayTalabMajburiyQiymatlar va cheklovlarStandart—
MaydonstreamTurbooleanTalabIxtiyoriyQiymatlar va cheklovlarStandartfalse
Maydonmax_tokensTurintegerTalabIxtiyoriyQiymatlar va cheklovlar
Eng kam: 1
Standart—
Maydonmax_completion_tokensTurintegerTalabIxtiyoriyQiymatlar va cheklovlar
Eng kam: 1
Standart—
MaydonnTurintegerTalabIxtiyoriyQiymatlar va cheklovlar
Eng kam: 1
Standart—
MaydontemperatureTurnumberTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydontop_pTurnumberTalabIxtiyoriyQiymatlar va cheklovlarStandart—
MaydonseedTurintegerTalabIxtiyoriyQiymatlar va cheklovlarStandart—
MaydonstopTurTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydonfrequency_penaltyTurnumberTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydonpresence_penaltyTurnumberTalabIxtiyoriyQiymatlar va cheklovlarStandart—
MaydonuserTurstringTalabIxtiyoriyQiymatlar va cheklovlarStandart—
MaydontoolsTurarrayTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Maydonservice_tierTurstringTalabIxtiyoriyQiymatlar va cheklovlar
, auto, default
Standart—
MaydonmodalitiesTurarrayTalabIxtiyoriyQiymatlar va cheklovlarStandart—
Usul parametrlari va hayot sikli
ParametrParametr talablari
Parametrcontent_typeParametr talablariapplication/json
ParametrrequiredParametr talablarimodel, messages
ParametroptionalParametr talablaristream, max_tokens, max_completion_tokens, n, temperature, top_p, seed, stop, frequency_penalty, presence_penalty, user, tools, service_tier, modalities
Parametrmaterial_typesParametr talablaritext
ParametrconditionsParametr talablariUse text input and text output. Explicit cache, hosted tools and media are unavailable. Standard function tools remain a protocol feature and run in the client.

Hayot sikli: synchronous_json_or_sse

Tariflar

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

Tezkor rejim
Standart tarif Tezkor rejim tanlanganda
Flex rejimi
Standart tarif flex rejimi tanlanganda
Maksimal fikrlash darajasi
Standart tarif Maksimal fikrlash darajasi uchun standart (default) tuzatish qo‘llanganda
Ochiq ko‘rsatilgan keshlash narxlari
Yoqilmagan Ochiq ko‘rsatilgan keshlash so‘ralganda
Faqat matn uchun nol tariflar
Kesh, vositalar va media uchun alohida keltirilgan narxlash shartlari amal qiladi. Faqat matnli kirish va chiqish uchun

Ailoot tariflari

Sozlamalar bo‘yicha narxlar

SozlamaTariflar jadvaliTo‘lov
SozlamaTokenlar uchun bazaviy tariflarTariflar jadvaliAsosiy tarifTo‘lov
Kirish · Kirish tokenlari $0 / 1K token
Chiqish · Chiqish tokenlari $0 / 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 · Chat Completions

set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "qwen2-0.5b-instruct", "messages": [{"role": "user", "content": "Summarize the supplied text in three concise points."}], "stream": false}')\n\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n  --data-binary @request.json -D response-headers.txt -o result.json\n# Submit once. Retain the complete result, original identifiers and every response header.\n

Natijalarni olish va saqlash

Ailoot Ultra · Chat Completions

JSON or supported SSE

Barcha tayyor natijalarni saqlash

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

Retain the complete response, all actual results, original identifiers, usage and response headers.

Asl javobni tekshirish kerak bo‘lsa

Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.

Taxalluslar

Model taxalluslari

Ailoot Ultra qwen2-0.5b-instruct

Usul yo‘llari taxalluslari

Yuqorida ko‘rsatilgan usul yo‘llaridan foydalaning.

Chaqirish uchun tegishli ID lar

qwen2-0.5b-instruct

", "method_identity": { "effective_request_model_id": "qwen2-0.5b-instruct", "fmt": "chat", "http": "POST", "path": "/v1/chat/completions", "request_model_id": "qwen2-0.5b-instruct", "route_label": "Ultra" }, "method_key": "Ultra|chat|/v1/chat/completions|qwen2-0.5b-instruct", "ordinal": 0, "rawMethod": { "available": null, "cache_read": "0", "cache_write": "0", "cache_write_1h": null, "contract": { "data": { "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.", "authentication": { "api_key_source": "AILOOT_API_KEY", "header": "Authorization: Bearer" }, "example_body": { "messages": [ { "content": "Summarize the supplied text in three concise points.", "role": "user" } ], "model": "qwen2-0.5b-instruct", "stream": false }, "lifecycle": { "cancel_endpoint": null, "create": { "http": "POST", "path": "/v1/chat/completions" }, "mode": "synchronous_json_or_sse", "status": [], "unknown": "Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.", "unknown_no_replay": true }, "outputs": { "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.", "family": "text", "format": "JSON or supported SSE" }, "parameters": [ { "default_known": false, "enum": [ "qwen2-0.5b-instruct" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "default_known": false, "enum_known": false, "items": { "additionalProperties": true, "properties": { "content": { "oneOf": [ { "type": "string" }, { "items": { "type": "object" }, "type": "array" } ] }, "role": { "enum": [ "user", "assistant", "system", "developer" ], "type": "string" } }, "required": [ "role", "content" ], "type": "object" }, "minItems": 1, "name": "messages", "required": true, "type": "array" }, { "default": false, "default_known": true, "enum_known": false, "name": "stream", "required": false, "type": "boolean" }, { "default_known": false, "enum_known": false, "minimum": 1, "name": "max_tokens", "required": false, "type": "integer" }, { "default_known": false, "enum_known": false, "minimum": 1, "name": "max_completion_tokens", "required": false, "type": "integer" }, { "default_known": false, "enum_known": false, "minimum": 1, "name": "n", "required": false, "type": "integer" }, { "default_known": false, "enum_known": false, "name": "temperature", "required": false, "type": "number" }, { "default_known": false, "enum_known": false, "name": "top_p", "required": false, "type": "number" }, { "default_known": false, "enum_known": false, "name": "seed", "required": false, "type": "integer" }, { "default_known": false, "enum_known": false, "name": "stop", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "required": false }, { "default_known": false, "enum_known": false, "name": "frequency_penalty", "required": false, "type": "number" }, { "default_known": false, "enum_known": false, "name": "presence_penalty", "required": false, "type": "number" }, { "default_known": false, "enum_known": false, "name": "user", "required": false, "type": "string" }, { "default_known": false, "enum_known": false, "items": { "type": "object" }, "name": "tools", "required": false, "type": "array" }, { "default_known": false, "enum": [ "", "auto", "default" ], "enum_known": true, "name": "service_tier", "required": false, "type": "string" }, { "default_known": false, "enum_known": false, "items": { "enum": [ "text" ], "type": "string" }, "name": "modalities", "required": false, "type": "array" } ], "request_schema": { "additionalProperties": true, "properties": { "frequency_penalty": { "type": "number" }, "max_completion_tokens": { "minimum": 1, "type": "integer" }, "max_tokens": { "minimum": 1, "type": "integer" }, "messages": { "items": { "additionalProperties": true, "properties": { "content": { "oneOf": [ { "type": "string" }, { "items": { "type": "object" }, "type": "array" } ] }, "role": { "enum": [ "user", "assistant", "system", "developer" ], "type": "string" } }, "required": [ "role", "content" ], "type": "object" }, "minItems": 1, "type": "array" }, "modalities": { "items": { "enum": [ "text" ], "type": "string" }, "type": "array" }, "model": { "enum": [ "qwen2-0.5b-instruct" ], "type": "string" }, "n": { "minimum": 1, "type": "integer" }, "presence_penalty": { "type": "number" }, "seed": { "type": "integer" }, "service_tier": { "enum": [ "", "auto", "default" ], "type": "string" }, "stop": { "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ] }, "stream": { "default": false, "type": "boolean" }, "temperature": { "type": "number" }, "tools": { "items": { "type": "object" }, "type": "array" }, "top_p": { "type": "number" }, "user": { "type": "string" } }, "required": [ "model", "messages" ], "type": "object" }, "schema_template": { "body": { "messages": [ { "content": "Summarize the supplied text in three concise points.", "role": "user" } ], "model": "qwen2-0.5b-instruct", "stream": false }, "content_type": "application/json" } }, "input": { "conditions": [ "Use text input and text output. Explicit cache, hosted tools and media are unavailable. Standard function tools remain a protocol feature and run in the client." ], "content_type": "application/json", "material_types": [ "text" ], "optional": [ "stream", "max_tokens", "max_completion_tokens", "n", "temperature", "top_p", "seed", "stop", "frequency_penalty", "presence_penalty", "user", "tools", "service_tier", "modalities" ], "required": [ "model", "messages" ] }, "lifecycle": { "cancel_endpoint": null, "create": { "http": "POST", "path": "/v1/chat/completions" }, "mode": "synchronous_json_or_sse", "status": [], "unknown": "Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.", "unknown_no_replay": true }, "method": { "http": "POST", "path": "/v1/chat/completions" }, "method_aliases": [], "output": { "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.", "family": "text", "format": "JSON or supported SSE" } }, "ctx": 32000, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "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}\"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"qwen2-0.5b-instruct\", \"messages\": [{\"role\": \"user\", \"content\": \"Summarize the supplied text in three concise points.\"}], \"stream\": false}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\n# Submit once. Retain the complete result, original identifiers and every response header.\n", "label": "curl · create once and retain the complete result", "language": "bash" } ], "family": "openai", "fast_multiplier": 1, "flex_multiplier": 1, "fmt": "chat", "http": "POST", "img_in": null, "img_out": null, "in": "0", "input": [ "text" ], "label": "Chat Completions", "max_out": null, "max_reasoning_effort_multiplier": 1, "method_aliases": [], "out": "0", "output": [ "text" ], "path": "/v1/chat/completions", "price_conditions": [ { "label": "Fast mode", "value": "Standard rate", "when": "When fast mode is selected" }, { "label": "Flex mode", "value": "Standard rate", "when": "When flex mode is selected" }, { "label": "Maximum reasoning effort", "value": "Standard rate", "when": "When maximum reasoning effort uses the default adjustment" }, { "label": "Explicit cache pricing", "value": "Not enabled", "when": "When explicit cache is requested" }, { "label": "Text-only zero rates", "value": "Cache, tools and media follow their separately listed pricing conditions.", "when": "For text-only input and output" } ], "pricing": { "allow_explicit_cache": false, "billing_mode": "token", "cache_read_price": 0, "cache_write_1h_price": null, "cache_write_price": 0, "fast_multiplier": 1, "flex_multiplier": 1, "image_input_price": null, "image_output_price": null, "input_price": 0, "intervals": [], "max_reasoning_effort_multiplier": 1, "output_price": 0, "per_request_price": null }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "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": "qwen2-0.5b-instruct", "route_label": "Ultra", "status_reads": [], "tiers": [], "unit": "token", "units": [ { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "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": "qwen2-0.5b-instruct", "nativeProvenance": { "input_facts_canonical_sha256": "b66e415157d794f0ef800ccdc0efc8777b74ab8f5e87ce252c480f3ea046cc2a", "input_html_sha256": "0f79c073796990d47c370e6d8056b85b74f06727d918142a82ffb40fce6a51d8", "private_audit_sha256": "16d48cd5db2d8142c131db97755dc38e145397654b4e34f110890eae2972da36", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/uz/qwen2-0-5b-instruct/", "description": "qwen2-0.5b-instruct — xabarlarga asoslangan so‘rovlar uchun Tongyi Qianwen matn modeli. Berilgan parchalar bilan ishlashda uning aniq ID qiymatidan Chat Completions bilan foydalaning: javob loyihasini yozing, qaydlarni tekshiruv ro‘yxatiga aylantiring, nomlangan maydonlarni ajratib oling yoki qisqa kod parchasini tushuntiring. Uning matnli kirish hajmi 32 000 token.", "locale": "uz", "md_url": "https://ailoot.ai/models/uz/qwen2-0-5b-instruct/index.md", "title": "qwen2-0.5b-instruct — Ailoot", "url": "https://ailoot.ai/models/uz/qwen2-0-5b-instruct/" }, "rawModel": { "about": "qwen2-0.5b-instruct — ushbu Tongyi Qianwen matnli suhbat profili uchun aniq model tanlash qiymati. Ko‘rsatmalar va manba materialini matn sifatida yuboring va aniq yozma natija so‘rang. Amaliy prompt lar mijoz xabarini taklif etilgan javobga aylantirishi, yig‘ilish qaydlarini qayta tartiblashi yoki berilgan parchadan kichik faktlar to‘plamini ajratib olishi mumkin. Ilova foydalanadigan aniq natija bo‘lishi uchun auditoriya va kerakli javob tuzilmasini belgilang.\n\nmodel ni qwen2-0.5b-instruct qilib va bo‘sh bo‘lmagan messages massivi bilan POST /v1/chat/completions dan foydalaning. Har bir xabar role va matnli content ni beradi. Keyingi savol suhbatga tayansa, tegishli oldingi xabarlarni kiriting. Hujjat bilan ishlash uchun hujjatdan tegishli UTF-8 matnini o‘qing va aynan shu haqiqiy matnni xabarga joylang. Javob bir nechta parchani ajratishi kerak bo‘lsa, har bir parcha bilan bo‘lim sarlavhasi yoki manba belgisini saqlang.\n\nMatnli kirish hajmi 32 000 token. Ko‘rsatmalar, manba parchalari va suhbat tarixini shu kirish hajmi ichida saqlang. Uzun hujjat uchun joriy savolga kerakli bo‘limlarni tanlang yoki aniq belgilangan parchalarni alohida vazifalar sifatida ishlang. Javob qaysi parchadan foydalanishi kerakligini va so‘ralgan maydon unda bo‘lmasa nima qaytarilishini ayting.\n\nJavob loyihasi vazifasi uchun mijozning so‘zlarini tasdiqlangan mahsulot ma’lumotlari bilan birga bering va aniq keyingi qadamli javob so‘rang. Ajratib olish uchun muammo, so‘ralgan amal va keltirilgan dalil kabi maydonlarni sanang, keyin modeldan faqat berilgan matnda topilgan faktlarni qaytarishni so‘rang. Kod tushuntirishi uchun funksiyani joylang va uning kirishlari, o‘zgartirishlari hamda chiqishi tavsifini so‘rang. Bu ko‘rsatmalar prompt ichida vazifani belgilaydi va natijani siz bergan materialga bog‘lab turadi.\n\nIxtiyoriy mantiqiy stream boshqaruvi javob yetkazilishini tanlaydi: standart stream:false yig‘ilgan JSON javobni qaytaradi; stream:true javob yaratilishi davomida hodisalarni yetkazadi. JSON uchun qaytarilgan choices va xabar mazmunini o‘qing va berilgan bo‘lsa usage ni saqlang. Oqimli yetkazishda javobni to‘liq deb saqlashdan avval har bir choice uchun content deltalarini yig‘ing va yakuniy finish ma’lumotlarini saqlang. So‘rov noaniq tugasa, dastlabki javob va so‘rov identifikatorini saqlang.", "alias_of": null, "aliases": [], "author": "Alibaba", "ctx": 32000, "formats": [ "chat" ], "id": "qwen2-0.5b-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": "Retain the complete response, all actual results, original identifiers, usage and response headers.", "authentication": { "api_key_source": "AILOOT_API_KEY", "header": "Authorization: Bearer" }, "example_body": { "messages": [ { "content": "Summarize the supplied text in three concise points.", "role": "user" } ], "model": "qwen2-0.5b-instruct", "stream": false }, "lifecycle": { "cancel_endpoint": null, "create": { "http": "POST", "path": "/v1/chat/completions" }, "mode": "synchronous_json_or_sse", "status": [], "unknown": "Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.", "unknown_no_replay": true }, "outputs": { "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.", "family": "text", "format": "JSON or supported SSE" }, "parameters": [ { "default_known": false, "enum": [ "qwen2-0.5b-instruct" ], "enum_known": true, "name": "model", "required": true, "type": "string" }, { "default_known": false, "enum_known": false, "items": { "additionalProperties": true, "properties": { "content": { "oneOf": [ { "type": "string" }, { "items": { "type": "object" }, "type": "array" } ] }, "role": { "enum": [ "user", "assistant", "system", "developer" ], "type": "string" } }, "required": [ "role", "content" ], "type": "object" }, "minItems": 1, "name": "messages", "required": true, "type": "array" }, { "default": false, "default_known": true, "enum_known": false, "name": "stream", "required": false, "type": "boolean" }, { "default_known": false, "enum_known": false, "minimum": 1, "name": "max_tokens", "required": false, "type": "integer" }, { "default_known": false, "enum_known": false, "minimum": 1, "name": "max_completion_tokens", "required": false, "type": "integer" }, { "default_known": false, "enum_known": false, "minimum": 1, "name": "n", "required": false, "type": "integer" }, { "default_known": false, "enum_known": false, "name": "temperature", "required": false, "type": "number" }, { "default_known": false, "enum_known": false, "name": "top_p", "required": false, "type": "number" }, { "default_known": false, "enum_known": false, "name": "seed", "required": false, "type": "integer" }, { "default_known": false, "enum_known": false, "name": "stop", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "required": false }, { "default_known": false, "enum_known": false, "name": "frequency_penalty", "required": false, "type": "number" }, { "default_known": false, "enum_known": false, "name": "presence_penalty", "required": false, "type": "number" }, { "default_known": false, "enum_known": false, "name": "user", "required": false, "type": "string" }, { "default_known": false, "enum_known": false, "items": { "type": "object" }, "name": "tools", "required": false, "type": "array" }, { "default_known": false, "enum": [ "", "auto", "default" ], "enum_known": true, "name": "service_tier", "required": false, "type": "string" }, { "default_known": false, "enum_known": false, "items": { "enum": [ "text" ], "type": "string" }, "name": "modalities", "required": false, "type": "array" } ], "request_schema": { "additionalProperties": true, "properties": { "frequency_penalty": { "type": "number" }, "max_completion_tokens": { "minimum": 1, "type": "integer" }, "max_tokens": { "minimum": 1, "type": "integer" }, "messages": { "items": { "additionalProperties": true, "properties": { "content": { "oneOf": [ { "type": "string" }, { "items": { "type": "object" }, "type": "array" } ] }, "role": { "enum": [ "user", "assistant", "system", "developer" ], "type": "string" } }, "required": [ "role", "content" ], "type": "object" }, "minItems": 1, "type": "array" }, "modalities": { "items": { "enum": [ "text" ], "type": "string" }, "type": "array" }, "model": { "enum": [ "qwen2-0.5b-instruct" ], "type": "string" }, "n": { "minimum": 1, "type": "integer" }, "presence_penalty": { "type": "number" }, "seed": { "type": "integer" }, "service_tier": { "enum": [ "", "auto", "default" ], "type": "string" }, "stop": { "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ] }, "stream": { "default": false, "type": "boolean" }, "temperature": { "type": "number" }, "tools": { "items": { "type": "object" }, "type": "array" }, "top_p": { "type": "number" }, "user": { "type": "string" } }, "required": [ "model", "messages" ], "type": "object" }, "schema_template": { "body": { "messages": [ { "content": "Summarize the supplied text in three concise points.", "role": "user" } ], "model": "qwen2-0.5b-instruct", "stream": false }, "content_type": "application/json" } }, "input": { "conditions": [ "Use text input and text output. Explicit cache, hosted tools and media are unavailable. Standard function tools remain a protocol feature and run in the client." ], "content_type": "application/json", "material_types": [ "text" ], "optional": [ "stream", "max_tokens", "max_completion_tokens", "n", "temperature", "top_p", "seed", "stop", "frequency_penalty", "presence_penalty", "user", "tools", "service_tier", "modalities" ], "required": [ "model", "messages" ] }, "lifecycle": { "cancel_endpoint": null, "create": { "http": "POST", "path": "/v1/chat/completions" }, "mode": "synchronous_json_or_sse", "status": [], "unknown": "Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.", "unknown_no_replay": true }, "method": { "http": "POST", "path": "/v1/chat/completions" }, "method_aliases": [], "output": { "all_results": "Retain the complete response, all actual results, original identifiers, usage and response headers.", "family": "text", "format": "JSON or supported SSE" } }, "ctx": 32000, "declared_pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "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}\"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{\"model\": \"qwen2-0.5b-instruct\", \"messages\": [{\"role\": \"user\", \"content\": \"Summarize the supplied text in three concise points.\"}], \"stream\": false}')\n\nPath(\"request.json\").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n -H \"Authorization: Bearer $AILOOT_API_KEY\" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\n# Submit once. Retain the complete result, original identifiers and every response header.\n", "label": "curl · create once and retain the complete result", "language": "bash" } ], "family": "openai", "fast_multiplier": 1, "flex_multiplier": 1, "fmt": "chat", "http": "POST", "img_in": null, "img_out": null, "in": "0", "input": [ "text" ], "label": "Chat Completions", "max_out": null, "max_reasoning_effort_multiplier": 1, "method_aliases": [], "out": "0", "output": [ "text" ], "path": "/v1/chat/completions", "price_conditions": [ { "label": "Fast mode", "value": "Standard rate", "when": "When fast mode is selected" }, { "label": "Flex mode", "value": "Standard rate", "when": "When flex mode is selected" }, { "label": "Maximum reasoning effort", "value": "Standard rate", "when": "When maximum reasoning effort uses the default adjustment" }, { "label": "Explicit cache pricing", "value": "Not enabled", "when": "When explicit cache is requested" }, { "label": "Text-only zero rates", "value": "Cache, tools and media follow their separately listed pricing conditions.", "when": "For text-only input and output" } ], "pricing": { "allow_explicit_cache": false, "billing_mode": "token", "cache_read_price": 0, "cache_write_1h_price": null, "cache_write_price": 0, "fast_multiplier": 1, "flex_multiplier": 1, "image_input_price": null, "image_output_price": null, "input_price": 0, "intervals": [], "max_reasoning_effort_multiplier": 1, "output_price": 0, "per_request_price": null }, "pricing_profiles": [ { "dimensions": { "regime": "base" }, "label": "Base token rates", "units": [ { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "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": "qwen2-0.5b-instruct", "route_label": "Ultra", "status_reads": [], "tiers": [], "unit": "token", "units": [ { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "input_price", "label": "Input tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "input_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "in", "unit": "input_token" }, { "amount": "0", "amount_unit": "usd_per_thousand_tokens", "billing_input": true, "estimate_only": false, "key": "output_price", "label": "Output tokens", "native_amount_usd_per_unit": "0", "native_quantity": 1, "native_unit": "output_token", "public_unit_label": "/ 1K tokens", "quantity": 1000, "quantity_is_estimate": false, "rate_role": "typed_native_retail_rate", "side": "out", "unit": "output_token" }, { "amount": "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": "qwen2-0.5b-instruct", "request_model_id": "qwen2-0.5b-instruct", "route_label": "Ultra" } ], "name": "qwen2-0.5b-instruct", "price": { "in": "0", "in_label": null, "out": "0", "out_label": null, "sort_in": "0", "sort_out": "0", "varies": false }, "short": "qwen2-0.5b-instruct — xabarlarga asoslangan so‘rovlar uchun Tongyi Qianwen matn modeli. Berilgan parchalar bilan ishlashda uning aniq ID qiymatidan Chat Completions bilan foydalaning: javob loyihasini yozing, qaydlarni tekshiruv ro‘yxatiga aylantiring, nomlangan maydonlarni ajratib oling yoki qisqa kod parchasini tushuntiring. Uning matnli kirish hajmi 32 000 token." }, "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 } ], "role": "current-semantic-reference" }, "raw_input": { "examples_role": "historical-input-not-current-ready-examples", "json_pointers": [ "/rawModel", "/methods/0/rawMethod" ], "role": "historical-owner-input" }, "schema": "ailoot-native-model-representations-v1" }, "schema": "ailoot-static-model-facts-v1", "schema_version": 1, "shortText": "qwen2-0.5b-instruct — xabarlarga asoslangan so‘rovlar uchun Tongyi Qianwen matn modeli. Berilgan parchalar bilan ishlashda uning aniq ID qiymatidan Chat Completions bilan foydalaning: javob loyihasini yozing, qaydlarni tekshiruv ro‘yxatiga aylantiring, nomlangan maydonlarni ajratib oling yoki qisqa kod parchasini tushuntiring. Uning matnli kirish hajmi 32 000 token.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "9e8fe0fc61f4183746b131d9c56234289722d7d1e2783e9a421975081beb4c52", "renderer_sha256": "aa8a0fca1bd770c042b64987512b6187fca2067aea3ddc877f68b163d80e7df6" }, "sourceRevision": "5d2f6345706b35e6f2aaae9c9886b12562f5c3318c7b0f19fe1eaf4d8d7a042a" }