{
  "aboutHtml": "<h3>Reflectionʼning Beam 1.0 modeli</h3><p>Beam 1.0 501B A23B — dasturlash, mulohaza va bir nechta bog‘liq bosqichga bo‘lingan vazifalar uchun Reflection matn modeli. Uning siyrak MoE arxitekturasida jami 501,3 mlrd parametr va har bir tokenda taxminan 23,4 mlrd faol parametr bor. Model asosan ingliz tili uchun ishlab chiqilgan; manba kodi, texnik materiallar va batafsil ko‘rsatmalar tilini tanlashda buni hisobga olish foydali.</p><p>Arxitektura mahalliy va global diqqatni birlashtiradi, model tayyorgarligi esa manba kodi, texnik hujjatlar, ilmiy va o‘quv materiallarini qamraydi. Alohida o‘qitish bosqichi tuzilmali repozitoriylar, uzoq vazifalar va katta hujjatlarda uzun kontekstni rivojlantirgan. Mustahkamlash orqali o‘qitishda ishlab chiqaruvchi dasturlash, terminal bilan ishlash va STEM vazifalarini birlashtirgan.</p><h3>Model qaysi vazifalarga yo‘naltirilgan</h3><p>Reflection dasturiy injiniring, matematik va ilmiy mulohaza hamda izchil vazifa yechishni ajratadi. Bu yo‘nalishlardan mazmunli matnli muloqotda foydalanish mumkin: dastur qismlari orasidagi bog‘lanishni tushuntirish, mantiqiy xatoni tahlil qilish, algoritm o‘zgarishini taklif etish, farazni tekshirish rejasini tuzish yoki texnik material xulosasini olish.</p><p>Beam mulohaza tokenlaridan tejamli foydalanishga alohida urg‘u beradi. O‘qitishda ortiqcha tokenlarni qisqartirib, muvaffaqiyatli yechimga erishish rag‘batlantirilgan. Bu ishlab chiqaruvchi yondashuvi tavsifi; muayyan javob uzunligini baribir so‘rovda aniq belgilash lozim: masalan, qisqa xulosa, batafsil isbot yoki izohli patch.</p><h3>Ailootʼdagi katta matnli kontekst</h3><p>Ulanish kontekst oynasi — 1 048 576 token. U berilgan materiallar, ko‘rsatmalar, tarix va yaratilayotgan javobni o‘z ichiga oladi. Model metamaʼlumotlarida javob hajmi 131 072 tokengacha deb ko‘rsatilgan. Bu hajm bir nechta bog‘liq matnni: spetsifikatsiya, yechim parchalari, tekshiruv natijalari va ularga savollarni yuborish imkonini beradi. Materiallarga nom bering va xulosa qaysi parchaga tayanishini belgilashni so‘rang: katta tahlillarni tekshirish qulayroq bo‘ladi.</p><p>Masalan, kod vazifasida avval berilgan fayllar bo‘yicha bog‘liqliklar xaritasini so‘rash, keyin o‘zgartirish joyini ajratish va shundan so‘ng amalga oshirishni tayyorlash mumkin. Ilmiy vazifa uchun boshlang‘ich shartlar, belgilar va kerakli qatʼiylik darajasini bering. Bu foydali natijani asl materiallarga solishtirish mumkin bo‘lgan so‘rov tashkil etish usullaridir.</p><p>Ulanish Messages, Responses, Chat Completions va matnli generateContent orqali matn qabul qilib, matn qaytaradi. Kod va tavsiyalar model javobi sifatida keladi; buyruqlarni bajarish va tashqi maʼlumotlar bilan ishlash ilovangiz imkoniyatlariga bog‘liq.</p>",
  "aboutText": "### Reflectionʼning Beam 1.0 modeli\n\nBeam 1.0 501B A23B — dasturlash, mulohaza va bir nechta bog‘liq bosqichga bo‘lingan vazifalar uchun Reflection matn modeli. Uning siyrak MoE arxitekturasida jami 501,3 mlrd parametr va har bir tokenda taxminan 23,4 mlrd faol parametr bor. Model asosan ingliz tili uchun ishlab chiqilgan; manba kodi, texnik materiallar va batafsil ko‘rsatmalar tilini tanlashda buni hisobga olish foydali.\n\nArxitektura mahalliy va global diqqatni birlashtiradi, model tayyorgarligi esa manba kodi, texnik hujjatlar, ilmiy va o‘quv materiallarini qamraydi. Alohida o‘qitish bosqichi tuzilmali repozitoriylar, uzoq vazifalar va katta hujjatlarda uzun kontekstni rivojlantirgan. Mustahkamlash orqali o‘qitishda ishlab chiqaruvchi dasturlash, terminal bilan ishlash va STEM vazifalarini birlashtirgan.\n\n### Model qaysi vazifalarga yo‘naltirilgan\n\nReflection dasturiy injiniring, matematik va ilmiy mulohaza hamda izchil vazifa yechishni ajratadi. Bu yo‘nalishlardan mazmunli matnli muloqotda foydalanish mumkin: dastur qismlari orasidagi bog‘lanishni tushuntirish, mantiqiy xatoni tahlil qilish, algoritm o‘zgarishini taklif etish, farazni tekshirish rejasini tuzish yoki texnik material xulosasini olish.\n\nBeam mulohaza tokenlaridan tejamli foydalanishga alohida urg‘u beradi. O‘qitishda ortiqcha tokenlarni qisqartirib, muvaffaqiyatli yechimga erishish rag‘batlantirilgan. Bu ishlab chiqaruvchi yondashuvi tavsifi; muayyan javob uzunligini baribir so‘rovda aniq belgilash lozim: masalan, qisqa xulosa, batafsil isbot yoki izohli patch.\n\n### Ailootʼdagi katta matnli kontekst\n\nUlanish kontekst oynasi — 1 048 576 token. U berilgan materiallar, ko‘rsatmalar, tarix va yaratilayotgan javobni o‘z ichiga oladi. Model metamaʼlumotlarida javob hajmi 131 072 tokengacha deb ko‘rsatilgan. Bu hajm bir nechta bog‘liq matnni: spetsifikatsiya, yechim parchalari, tekshiruv natijalari va ularga savollarni yuborish imkonini beradi. Materiallarga nom bering va xulosa qaysi parchaga tayanishini belgilashni so‘rang: katta tahlillarni tekshirish qulayroq bo‘ladi.\n\nMasalan, kod vazifasida avval berilgan fayllar bo‘yicha bog‘liqliklar xaritasini so‘rash, keyin o‘zgartirish joyini ajratish va shundan so‘ng amalga oshirishni tayyorlash mumkin. Ilmiy vazifa uchun boshlang‘ich shartlar, belgilar va kerakli qatʼiylik darajasini bering. Bu foydali natijani asl materiallarga solishtirish mumkin bo‘lgan so‘rov tashkil etish usullaridir.\n\nUlanish Messages, Responses, Chat Completions va matnli generateContent orqali matn qabul qilib, matn qaytaradi. Kod va tavsiyalar model javobi sifatida keladi; buyruqlarni bajarish va tashqi maʼlumotlar bilan ishlash ilovangiz imkoniyatlariga bog‘liq.",
  "locale": "uz",
  "methods": [
    {
      "html": "<section class=\"meth\" data-route=\"Ultra\"><header><b><span class=\"alias\">Ultra</span> Anthropic Messages</b><code>POST /v1/messages</code></header><dl class=\"method-capabilities\" data-model-capabilities><div><dt>Kirish</dt><dd><code>text</code></dd></div><div><dt>Chiqish</dt><dd><code>text</code></dd></div><div><dt>Kontekst</dt><dd>Katalogda ko‘rsatilmagan</dd></div><div><dt>Maks. javob</dt><dd>Katalogda ko‘rsatilmagan</dd></div></dl><dl><div><dt>Kirish</dt><dd>0,0005 US$ <em>/ 1K</em></dd></div><div><dt>Chiqish</dt><dd>0,002 US$ <em>/ 1K</em></dd></div><div><dt>Keshdan o‘qish</dt><dd>0,0001 US$ <em>/ 1K</em></dd></div></dl></section>",
      "method_identity": {
        "effective_request_model_id": "reflection/beam-1.0-501b-a23b",
        "fmt": "messages",
        "http": "POST",
        "path": "/v1/messages",
        "request_model_id": "reflection/beam-1.0-501b-a23b",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|messages|/v1/messages|reflection%2Fbeam-1.0-501b-a23b",
      "ordinal": 0,
      "rawMethod": {
        "cache_read": 0.1,
        "cache_write": null,
        "cache_write_1h": null,
        "cache_write_5m": null,
        "conditional_meter": null,
        "ctx": null,
        "family": "anthropic",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "messages",
        "http": "POST",
        "img_in": null,
        "img_out": null,
        "in": 0.5,
        "input": [
          "text"
        ],
        "label": "Anthropic Messages",
        "max_out": null,
        "max_tool_calls": null,
        "out": 2.0,
        "output": [
          "text"
        ],
        "path": "/v1/messages",
        "request_model_id": "reflection/beam-1.0-501b-a23b",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "tool_prices": null,
        "unit": "token",
        "units": []
      }
    },
    {
      "html": "<section class=\"meth\" data-route=\"Ultra\"><header><b><span class=\"alias\">Ultra</span> OpenAI Responses</b><code>POST /v1/responses</code></header><dl class=\"method-capabilities\" data-model-capabilities><div><dt>Kirish</dt><dd><code>text</code></dd></div><div><dt>Chiqish</dt><dd><code>text</code></dd></div><div><dt>Kontekst</dt><dd>Katalogda ko‘rsatilmagan</dd></div><div><dt>Maks. javob</dt><dd>Katalogda ko‘rsatilmagan</dd></div></dl><dl><div><dt>Kirish</dt><dd>0,0005 US$ <em>/ 1K</em></dd></div><div><dt>Chiqish</dt><dd>0,002 US$ <em>/ 1K</em></dd></div><div><dt>Keshdan o‘qish</dt><dd>0,0001 US$ <em>/ 1K</em></dd></div></dl></section>",
      "method_identity": {
        "effective_request_model_id": "reflection/beam-1.0-501b-a23b",
        "fmt": "responses",
        "http": "POST",
        "path": "/v1/responses",
        "request_model_id": "reflection/beam-1.0-501b-a23b",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|responses|/v1/responses|reflection%2Fbeam-1.0-501b-a23b",
      "ordinal": 1,
      "rawMethod": {
        "cache_read": 0.1,
        "cache_write": null,
        "cache_write_1h": null,
        "cache_write_5m": null,
        "conditional_meter": null,
        "ctx": null,
        "family": "openai",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "responses",
        "http": "POST",
        "img_in": null,
        "img_out": null,
        "in": 0.5,
        "input": [
          "text"
        ],
        "label": "OpenAI Responses",
        "max_out": null,
        "max_tool_calls": null,
        "out": 2.0,
        "output": [
          "text"
        ],
        "path": "/v1/responses",
        "request_model_id": "reflection/beam-1.0-501b-a23b",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "tool_prices": null,
        "unit": "token",
        "units": []
      }
    },
    {
      "html": "<section class=\"meth\" data-route=\"Ultra\"><header><b><span class=\"alias\">Ultra</span> OpenAI Chat</b><code>POST /v1/chat/completions</code></header><dl class=\"method-capabilities\" data-model-capabilities><div><dt>Kirish</dt><dd><code>text</code></dd></div><div><dt>Chiqish</dt><dd><code>text</code></dd></div><div><dt>Kontekst</dt><dd>Katalogda ko‘rsatilmagan</dd></div><div><dt>Maks. javob</dt><dd>Katalogda ko‘rsatilmagan</dd></div></dl><dl><div><dt>Kirish</dt><dd>0,0005 US$ <em>/ 1K</em></dd></div><div><dt>Chiqish</dt><dd>0,002 US$ <em>/ 1K</em></dd></div><div><dt>Keshdan o‘qish</dt><dd>0,0001 US$ <em>/ 1K</em></dd></div></dl></section>",
      "method_identity": {
        "effective_request_model_id": "reflection/beam-1.0-501b-a23b",
        "fmt": "chat",
        "http": "POST",
        "path": "/v1/chat/completions",
        "request_model_id": "reflection/beam-1.0-501b-a23b",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|chat|/v1/chat/completions|reflection%2Fbeam-1.0-501b-a23b",
      "ordinal": 2,
      "rawMethod": {
        "cache_read": 0.1,
        "cache_write": null,
        "cache_write_1h": null,
        "cache_write_5m": null,
        "conditional_meter": null,
        "ctx": null,
        "family": "openai",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "chat",
        "http": "POST",
        "img_in": null,
        "img_out": null,
        "in": 0.5,
        "input": [
          "text"
        ],
        "label": "OpenAI Chat",
        "max_out": null,
        "max_tool_calls": null,
        "out": 2.0,
        "output": [
          "text"
        ],
        "path": "/v1/chat/completions",
        "request_model_id": "reflection/beam-1.0-501b-a23b",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "tool_prices": null,
        "unit": "token",
        "units": []
      }
    },
    {
      "html": "<section class=\"meth\" data-route=\"Ultra\"><header><b><span class=\"alias\">Ultra</span> Gemini generateContent</b><code>POST /v1beta/models/reflection/beam-1.0-501b-a23b:generateContent</code></header><dl class=\"method-capabilities\" data-model-capabilities><div><dt>Kirish</dt><dd><code>text</code></dd></div><div><dt>Chiqish</dt><dd><code>text</code></dd></div><div><dt>Kontekst</dt><dd>Katalogda ko‘rsatilmagan</dd></div><div><dt>Maks. javob</dt><dd>Katalogda ko‘rsatilmagan</dd></div></dl><dl><div><dt>Kirish</dt><dd>0,0005 US$ <em>/ 1K</em></dd></div><div><dt>Chiqish</dt><dd>0,002 US$ <em>/ 1K</em></dd></div><div><dt>Keshdan o‘qish</dt><dd>0,0001 US$ <em>/ 1K</em></dd></div></dl></section>",
      "method_identity": {
        "effective_request_model_id": "reflection/beam-1.0-501b-a23b",
        "fmt": "gemini",
        "http": "POST",
        "path": "/v1beta/models/reflection/beam-1.0-501b-a23b:generateContent",
        "request_model_id": "reflection/beam-1.0-501b-a23b",
        "route_label": "Ultra"
      },
      "method_key": "Ultra|gemini|/v1beta/models/reflection/beam-1.0-501b-a23b:generateContent|reflection%2Fbeam-1.0-501b-a23b",
      "ordinal": 3,
      "rawMethod": {
        "cache_read": 0.1,
        "cache_write": null,
        "cache_write_1h": null,
        "cache_write_5m": null,
        "conditional_meter": null,
        "ctx": null,
        "family": "gemini",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "gemini",
        "http": "POST",
        "img_in": null,
        "img_out": null,
        "in": 0.5,
        "input": [
          "text"
        ],
        "label": "Gemini generateContent",
        "max_out": null,
        "max_tool_calls": null,
        "out": 2.0,
        "output": [
          "text"
        ],
        "path": "/v1beta/models/reflection/beam-1.0-501b-a23b:generateContent",
        "request_model_id": "reflection/beam-1.0-501b-a23b",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "tool_prices": null,
        "unit": "token",
        "units": []
      }
    }
  ],
  "model_id": "reflection/beam-1.0-501b-a23b",
  "page": {
    "canonical": "https://ailoot.ai/models/uz/reflection-beam-1-0-501b-a23b/",
    "description": "Dasturiy injiniring, matematik mulohaza va vazifalarni izchil tahlil qilish uchun katta kontekstli Reflection matnli MoE modeli.",
    "locale": "uz",
    "md_url": "https://ailoot.ai/models/uz/reflection-beam-1-0-501b-a23b/index.md",
    "title": "Reflection: Beam 1.0 501B A23B",
    "url": "https://ailoot.ai/models/uz/reflection-beam-1-0-501b-a23b/"
  },
  "rawModel": {
    "about": "### Reflectionʼning Beam 1.0 modeli\n\nBeam 1.0 501B A23B — dasturlash, mulohaza va bir nechta bog‘liq bosqichga bo‘lingan vazifalar uchun Reflection matn modeli. Uning siyrak MoE arxitekturasida jami 501,3 mlrd parametr va har bir tokenda taxminan 23,4 mlrd faol parametr bor. Model asosan ingliz tili uchun ishlab chiqilgan; manba kodi, texnik materiallar va batafsil ko‘rsatmalar tilini tanlashda buni hisobga olish foydali.\n\nArxitektura mahalliy va global diqqatni birlashtiradi, model tayyorgarligi esa manba kodi, texnik hujjatlar, ilmiy va o‘quv materiallarini qamraydi. Alohida o‘qitish bosqichi tuzilmali repozitoriylar, uzoq vazifalar va katta hujjatlarda uzun kontekstni rivojlantirgan. Mustahkamlash orqali o‘qitishda ishlab chiqaruvchi dasturlash, terminal bilan ishlash va STEM vazifalarini birlashtirgan.\n\n### Model qaysi vazifalarga yo‘naltirilgan\n\nReflection dasturiy injiniring, matematik va ilmiy mulohaza hamda izchil vazifa yechishni ajratadi. Bu yo‘nalishlardan mazmunli matnli muloqotda foydalanish mumkin: dastur qismlari orasidagi bog‘lanishni tushuntirish, mantiqiy xatoni tahlil qilish, algoritm o‘zgarishini taklif etish, farazni tekshirish rejasini tuzish yoki texnik material xulosasini olish.\n\nBeam mulohaza tokenlaridan tejamli foydalanishga alohida urg‘u beradi. O‘qitishda ortiqcha tokenlarni qisqartirib, muvaffaqiyatli yechimga erishish rag‘batlantirilgan. Bu ishlab chiqaruvchi yondashuvi tavsifi; muayyan javob uzunligini baribir so‘rovda aniq belgilash lozim: masalan, qisqa xulosa, batafsil isbot yoki izohli patch.\n\n### Ailootʼdagi katta matnli kontekst\n\nUlanish kontekst oynasi — 1 048 576 token. U berilgan materiallar, ko‘rsatmalar, tarix va yaratilayotgan javobni o‘z ichiga oladi. Model metamaʼlumotlarida javob hajmi 131 072 tokengacha deb ko‘rsatilgan. Bu hajm bir nechta bog‘liq matnni: spetsifikatsiya, yechim parchalari, tekshiruv natijalari va ularga savollarni yuborish imkonini beradi. Materiallarga nom bering va xulosa qaysi parchaga tayanishini belgilashni so‘rang: katta tahlillarni tekshirish qulayroq bo‘ladi.\n\nMasalan, kod vazifasida avval berilgan fayllar bo‘yicha bog‘liqliklar xaritasini so‘rash, keyin o‘zgartirish joyini ajratish va shundan so‘ng amalga oshirishni tayyorlash mumkin. Ilmiy vazifa uchun boshlang‘ich shartlar, belgilar va kerakli qatʼiylik darajasini bering. Bu foydali natijani asl materiallarga solishtirish mumkin bo‘lgan so‘rov tashkil etish usullaridir.\n\nUlanish Messages, Responses, Chat Completions va matnli generateContent orqali matn qabul qilib, matn qaytaradi. Kod va tavsiyalar model javobi sifatida keladi; buyruqlarni bajarish va tashqi maʼlumotlar bilan ishlash ilovangiz imkoniyatlariga bog‘liq.",
    "alias_of": null,
    "aliases": [],
    "author": "Reflection",
    "canonical_model": "reflection/beam-1.0-501b-a23b",
    "ctx": null,
    "formats": [
      "chat",
      "gemini",
      "messages",
      "responses"
    ],
    "id": "reflection/beam-1.0-501b-a23b",
    "input": [
      "text"
    ],
    "kind": "text",
    "max_out": null,
    "methods": [
      {
        "cache_read": 0.1,
        "cache_write": null,
        "cache_write_1h": null,
        "cache_write_5m": null,
        "conditional_meter": null,
        "ctx": null,
        "family": "anthropic",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "messages",
        "http": "POST",
        "img_in": null,
        "img_out": null,
        "in": 0.5,
        "input": [
          "text"
        ],
        "label": "Anthropic Messages",
        "max_out": null,
        "max_tool_calls": null,
        "out": 2.0,
        "output": [
          "text"
        ],
        "path": "/v1/messages",
        "request_model_id": "reflection/beam-1.0-501b-a23b",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "tool_prices": null,
        "unit": "token",
        "units": []
      },
      {
        "cache_read": 0.1,
        "cache_write": null,
        "cache_write_1h": null,
        "cache_write_5m": null,
        "conditional_meter": null,
        "ctx": null,
        "family": "openai",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "responses",
        "http": "POST",
        "img_in": null,
        "img_out": null,
        "in": 0.5,
        "input": [
          "text"
        ],
        "label": "OpenAI Responses",
        "max_out": null,
        "max_tool_calls": null,
        "out": 2.0,
        "output": [
          "text"
        ],
        "path": "/v1/responses",
        "request_model_id": "reflection/beam-1.0-501b-a23b",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "tool_prices": null,
        "unit": "token",
        "units": []
      },
      {
        "cache_read": 0.1,
        "cache_write": null,
        "cache_write_1h": null,
        "cache_write_5m": null,
        "conditional_meter": null,
        "ctx": null,
        "family": "openai",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "chat",
        "http": "POST",
        "img_in": null,
        "img_out": null,
        "in": 0.5,
        "input": [
          "text"
        ],
        "label": "OpenAI Chat",
        "max_out": null,
        "max_tool_calls": null,
        "out": 2.0,
        "output": [
          "text"
        ],
        "path": "/v1/chat/completions",
        "request_model_id": "reflection/beam-1.0-501b-a23b",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "tool_prices": null,
        "unit": "token",
        "units": []
      },
      {
        "cache_read": 0.1,
        "cache_write": null,
        "cache_write_1h": null,
        "cache_write_5m": null,
        "conditional_meter": null,
        "ctx": null,
        "family": "gemini",
        "fast_multiplier": 1,
        "flex_multiplier": 1,
        "fmt": "gemini",
        "http": "POST",
        "img_in": null,
        "img_out": null,
        "in": 0.5,
        "input": [
          "text"
        ],
        "label": "Gemini generateContent",
        "max_out": null,
        "max_tool_calls": null,
        "out": 2.0,
        "output": [
          "text"
        ],
        "path": "/v1beta/models/reflection/beam-1.0-501b-a23b:generateContent",
        "request_model_id": "reflection/beam-1.0-501b-a23b",
        "route_label": "Ultra",
        "status_reads": [],
        "tiers": [],
        "tool_prices": null,
        "unit": "token",
        "units": []
      }
    ],
    "model_aliases": [
      {
        "canonical_model_id": "reflection/beam-1.0-501b-a23b",
        "request_model_id": "reflection/beam-1.0-501b-a23b",
        "route_label": "Ultra"
      }
    ],
    "name": "Reflection: Beam 1.0 501B A23B",
    "price": {
      "in": 0.5,
      "in_label": null,
      "out": 2.0,
      "out_label": null,
      "sort_in": 0.5,
      "sort_out": 2.0,
      "varies": false
    },
    "public_id": "reflection/beam-1.0-501b-a23b",
    "short": "Dasturiy injiniring, matematik mulohaza va vazifalarni izchil tahlil qilish uchun katta kontekstli Reflection matnli MoE modeli."
  },
  "renderer_schema": {
    "method_order": "rawModel.methods-ordinal",
    "prices": "exact-native-DTO-units-and-selected-renderer-html"
  },
  "representation": "exact-public-dto-and-native-renderer-html",
  "schema": "ailoot-static-model-facts-v1",
  "schema_version": 1,
  "shortText": "Dasturiy injiniring, matematik mulohaza va vazifalarni izchil tahlil qilish uchun katta kontekstli Reflection matnli MoE modeli.",
  "source": {
    "audit_sha256": "072c9818ac9422b6401bdb7a86b9b3a898e13f61adb5658f933e212d248ad822",
    "bridge_map_sha256": "072c9818ac9422b6401bdb7a86b9b3a898e13f61adb5658f933e212d248ad822",
    "projection_audit": {
      "parent_manifest_sha256": "dfd3a7a4bc6f960e128026cae1f98f769d1503ffa1c6cf0ccf80066382dbbb9e",
      "raw_model_json_sha256": "24e47a04f19e7c2cdc566a537eb915269620e8fe799d8a63e30aa82a6bc5a002",
      "renderer_sha256": "cc3e7eb5f31837bb63a187f76259b8aa0a10ebdf2c8cb67803b0a2a0f5f31bf0"
    },
    "projection_kind": "catalogue-refresh-native-dto-v1",
    "raw_model_json_sha256": "24e47a04f19e7c2cdc566a537eb915269620e8fe799d8a63e30aa82a6bc5a002",
    "renderer_sha256": "cc3e7eb5f31837bb63a187f76259b8aa0a10ebdf2c8cb67803b0a2a0f5f31bf0"
  },
  "sourceRevision": "95b5708e0ed36ab777d6d7f88065c8891ada0fea519dac0b263b56a01d9dfde2"
}
