{ "aboutHtml": "

qwen2-0.5b-instruct adalah pilihan model persis untuk profil percakapan teks Tongyi Qianwen ini. Kirim instruksi dan materi sumber sebagai teks lalu minta hasil tertulis yang konkret. Prompt praktis dapat mengubah pesan pelanggan menjadi usulan balasan, menata ulang catatan rapat, atau mengekstrak sekumpulan kecil fakta dari bacaan yang diberikan. Tentukan sasaran pembaca dan struktur jawaban yang diinginkan agar aplikasi memiliki hasil yang jelas untuk digunakan.

Gunakan POST /v1/chat/completions dengan model diatur ke qwen2-0.5b-instruct dan array messages yang tidak kosong. Setiap pesan menyertakan role dan content berupa teks. Sertakan giliran sebelumnya yang relevan ketika tindak lanjut bergantung pada percakapan. Untuk pekerjaan dokumen, baca teks UTF-8 yang relevan dari dokumen dan masukkan teks aktual tersebut ke dalam pesan. Simpan judul bagian atau label sumber bersama setiap kutipan ketika jawaban perlu membedakan beberapa bacaan.

Alokasi masukan teks adalah 32.000 token. Pertahankan instruksi, bacaan sumber, dan riwayat percakapan dalam anggaran masukan tersebut. Untuk dokumen panjang, pilih bagian yang diperlukan bagi pertanyaan saat ini atau proses kutipan berlabel jelas sebagai tugas terpisah. Nyatakan bacaan yang harus digunakan jawaban dan apa yang harus dikembalikan ketika kolom yang diminta tidak terdapat di dalamnya.

Untuk tugas penyusunan balasan, berikan kata-kata pelanggan bersama informasi produk yang disetujui dan minta respons dengan langkah berikutnya yang spesifik. Untuk ekstraksi, daftarkan kolom seperti masalah, tindakan yang diminta, dan bukti kutipan, lalu minta model mengembalikan hanya fakta yang ditemukan dalam teks yang diberikan. Untuk penjelasan kode, tempel fungsi dan minta deskripsi masukan, transformasi, dan keluarannya. Instruksi tersebut menentukan tugas di dalam prompt dan menjaga hasil tetap terikat pada materi yang Anda berikan.

Kontrol boolean stream yang opsional memilih cara pengiriman respons: stream:false, nilai bawaan, mengembalikan respons JSON yang telah dirangkai; stream:true mengirim peristiwa saat respons dihasilkan. Untuk JSON, baca choices dan isi pesan yang dikembalikan serta simpan usage jika disertakan. Untuk streaming, rangkai delta content untuk setiap choice dan simpan informasi penyelesaian akhir sebelum menyimpan jawaban sebagai lengkap. Simpan respons asli dan identitas permintaan ketika permintaan berakhir dengan hasil yang ambigu.

", "aboutText": "qwen2-0.5b-instruct adalah pilihan model persis untuk profil percakapan teks Tongyi Qianwen ini. Kirim instruksi dan materi sumber sebagai teks lalu minta hasil tertulis yang konkret. Prompt praktis dapat mengubah pesan pelanggan menjadi usulan balasan, menata ulang catatan rapat, atau mengekstrak sekumpulan kecil fakta dari bacaan yang diberikan. Tentukan sasaran pembaca dan struktur jawaban yang diinginkan agar aplikasi memiliki hasil yang jelas untuk digunakan.\n\nGunakan POST /v1/chat/completions dengan model diatur ke qwen2-0.5b-instruct dan array messages yang tidak kosong. Setiap pesan menyertakan role dan content berupa teks. Sertakan giliran sebelumnya yang relevan ketika tindak lanjut bergantung pada percakapan. Untuk pekerjaan dokumen, baca teks UTF-8 yang relevan dari dokumen dan masukkan teks aktual tersebut ke dalam pesan. Simpan judul bagian atau label sumber bersama setiap kutipan ketika jawaban perlu membedakan beberapa bacaan.\n\nAlokasi masukan teks adalah 32.000 token. Pertahankan instruksi, bacaan sumber, dan riwayat percakapan dalam anggaran masukan tersebut. Untuk dokumen panjang, pilih bagian yang diperlukan bagi pertanyaan saat ini atau proses kutipan berlabel jelas sebagai tugas terpisah. Nyatakan bacaan yang harus digunakan jawaban dan apa yang harus dikembalikan ketika kolom yang diminta tidak terdapat di dalamnya.\n\nUntuk tugas penyusunan balasan, berikan kata-kata pelanggan bersama informasi produk yang disetujui dan minta respons dengan langkah berikutnya yang spesifik. Untuk ekstraksi, daftarkan kolom seperti masalah, tindakan yang diminta, dan bukti kutipan, lalu minta model mengembalikan hanya fakta yang ditemukan dalam teks yang diberikan. Untuk penjelasan kode, tempel fungsi dan minta deskripsi masukan, transformasi, dan keluarannya. Instruksi tersebut menentukan tugas di dalam prompt dan menjaga hasil tetap terikat pada materi yang Anda berikan.\n\nKontrol boolean stream yang opsional memilih cara pengiriman respons: stream:false, nilai bawaan, mengembalikan respons JSON yang telah dirangkai; stream:true mengirim peristiwa saat respons dihasilkan. Untuk JSON, baca choices dan isi pesan yang dikembalikan serta simpan usage jika disertakan. Untuk streaming, rangkai delta content untuk setiap choice dan simpan informasi penyelesaian akhir sebelum menyimpan jawaban sebagai lengkap. Simpan respons asli dan identitas permintaan ketika permintaan berakhir dengan hasil yang ambigu.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "943f1853e4c67946e6c13c692b5345355f76af0d58ebced07b8896e38af7a235", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "id", "methods": [ { "html": "
Al

qwen2-0.5b-instruct

Alibaba /qwen2-0.5b-instruct

Mulai

qwen2-0.5b-instruct adalah model teks Tongyi Qianwen untuk permintaan berbasis pesan. Gunakan ID persisnya dengan Chat Completions untuk mengolah bacaan yang diberikan: menyusun balasan, mengubah catatan menjadi daftar periksa, mengekstrak kolom tertentu, atau menjelaskan fragmen kode singkat. Alokasi masukan teksnya adalah 32.000 token.

Masukan yang diterima
Teks
Hasil
Teks
Tentang model

Deskripsi ini mencakup kemampuan model. Masukan, keluaran, dan opsi yang tersedia di Ailoot tercantum di bawah untuk metode yang dipilih.

qwen2-0.5b-instruct adalah pilihan model persis untuk profil percakapan teks Tongyi Qianwen ini. Kirim instruksi dan materi sumber sebagai teks lalu minta hasil tertulis yang konkret. Prompt praktis dapat mengubah pesan pelanggan menjadi usulan balasan, menata ulang catatan rapat, atau mengekstrak sekumpulan kecil fakta dari bacaan yang diberikan. Tentukan sasaran pembaca dan struktur jawaban yang diinginkan agar aplikasi memiliki hasil yang jelas untuk digunakan.

Gunakan POST /v1/chat/completions dengan model diatur ke qwen2-0.5b-instruct dan array messages yang tidak kosong. Setiap pesan menyertakan role dan content berupa teks. Sertakan giliran sebelumnya yang relevan ketika tindak lanjut bergantung pada percakapan. Untuk pekerjaan dokumen, baca teks UTF-8 yang relevan dari dokumen dan masukkan teks aktual tersebut ke dalam pesan. Simpan judul bagian atau label sumber bersama setiap kutipan ketika jawaban perlu membedakan beberapa bacaan.

Alokasi masukan teks adalah 32.000 token. Pertahankan instruksi, bacaan sumber, dan riwayat percakapan dalam anggaran masukan tersebut. Untuk dokumen panjang, pilih bagian yang diperlukan bagi pertanyaan saat ini atau proses kutipan berlabel jelas sebagai tugas terpisah. Nyatakan bacaan yang harus digunakan jawaban dan apa yang harus dikembalikan ketika kolom yang diminta tidak terdapat di dalamnya.

Untuk tugas penyusunan balasan, berikan kata-kata pelanggan bersama informasi produk yang disetujui dan minta respons dengan langkah berikutnya yang spesifik. Untuk ekstraksi, daftarkan kolom seperti masalah, tindakan yang diminta, dan bukti kutipan, lalu minta model mengembalikan hanya fakta yang ditemukan dalam teks yang diberikan. Untuk penjelasan kode, tempel fungsi dan minta deskripsi masukan, transformasi, dan keluarannya. Instruksi tersebut menentukan tugas di dalam prompt dan menjaga hasil tetap terikat pada materi yang Anda berikan.

Kontrol boolean stream yang opsional memilih cara pengiriman respons: stream:false, nilai bawaan, mengembalikan respons JSON yang telah dirangkai; stream:true mengirim peristiwa saat respons dihasilkan. Untuk JSON, baca choices dan isi pesan yang dikembalikan serta simpan usage jika disertakan. Untuk streaming, rangkai delta content untuk setiap choice dan simpan informasi penyelesaian akhir sebelum menyimpan jawaban sebagai lengkap. Simpan respons asli dan identitas permintaan ketika permintaan berakhir dengan hasil yang ambigu.

Tersedia di Ailoot · masukan & metode

Ailoot Ultra · Chat Completions POST /v1/chat/completions
Masukan
text
Keluaran
text
Konteks
32.000 tokens
Format keluaran
JSON or supported SSE
KolomJenisPersyaratanNilai & batasanBawaan
KolommodelJenisstringPersyaratanWajibNilai & batasan
qwen2-0.5b-instruct
Bawaan—
KolommessagesJenisarrayPersyaratanWajibNilai & batasanBawaan—
KolomstreamJenisbooleanPersyaratanOpsionalNilai & batasanBawaanfalse
Kolommax_tokensJenisintegerPersyaratanOpsionalNilai & batasan
Minimum: 1
Bawaan—
Kolommax_completion_tokensJenisintegerPersyaratanOpsionalNilai & batasan
Minimum: 1
Bawaan—
KolomnJenisintegerPersyaratanOpsionalNilai & batasan
Minimum: 1
Bawaan—
KolomtemperatureJenisnumberPersyaratanOpsionalNilai & batasanBawaan—
Kolomtop_pJenisnumberPersyaratanOpsionalNilai & batasanBawaan—
KolomseedJenisintegerPersyaratanOpsionalNilai & batasanBawaan—
KolomstopJenisPersyaratanOpsionalNilai & batasanBawaan—
Kolomfrequency_penaltyJenisnumberPersyaratanOpsionalNilai & batasanBawaan—
Kolompresence_penaltyJenisnumberPersyaratanOpsionalNilai & batasanBawaan—
KolomuserJenisstringPersyaratanOpsionalNilai & batasanBawaan—
KolomtoolsJenisarrayPersyaratanOpsionalNilai & batasanBawaan—
Kolomservice_tierJenisstringPersyaratanOpsionalNilai & batasan
, auto, default
Bawaan—
KolommodalitiesJenisarrayPersyaratanOpsionalNilai & batasanBawaan—
Parameter metode dan siklus hidup
ParameterKontrak
Parametercontent_typeKontrakapplication/json
ParameterrequiredKontrakmodel, messages
ParameteroptionalKontrakstream, max_tokens, max_completion_tokens, n, temperature, top_p, seed, stop, frequency_penalty, presence_penalty, user, tools, service_tier, modalities
Parametermaterial_typesKontraktext
ParameterconditionsKontrakUse 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.

Siklus hidup: synchronous_json_or_sse

Paket

Referensi tarif tidak menambah opsi permintaan. Parameter yang didukung tercantum pada metode yang dipilih.

Mode Fast
Tarif standar Saat mode fast dipilih
Mode Flex
Tarif standar Saat mode flex dipilih
Upaya penalaran maksimum
Tarif standar Saat upaya penalaran maksimum menggunakan penyesuaian default
Harga cache eksplisit
Tidak diaktifkan Saat cache eksplisit diminta
Tarif nol khusus teks
Cache, alat, dan media mengikuti ketentuan harga masing-masing yang tercantum secara terpisah. Untuk masukan dan keluaran yang hanya berupa teks

Tarif Ailoot

Harga menurut konfigurasi

KonfigurasiJadwal tarifBiaya
KonfigurasiTarif dasar tokenJadwal tarifTarif dasarBiaya
Masukan · Token masukan $0 / 1K token
Keluaran · Token keluaran $0 / 1K token
Masukan · Pembacaan cache $0 / 1K token
Masukan · Penulisan cache $0 / 1K token
Masukan · Penulisan cache, 1 jam — / 1K token

Ailoot mencadangkan perkiraan dana sebelum permintaan. Setelah hasil diterima, tagihan akhir diselesaikan berdasarkan penggunaan yang terukur. Penyelesaian tagihan streaming menggunakan catatan penggunaan akhir. Cara kerja cadangan dan penyelesaian tagihan →

Contoh permintaan

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

Terima dan simpan hasil

Ailoot Ultra · Chat Completions

JSON or supported SSE

Simpan semua hasil yang selesai

Simpan setiap respons lengkap, berkas, dan bagian konten yang dikembalikan.

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

Jika respons asli perlu diperiksa

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

Alias

Alias model

Ailoot Ultra qwen2-0.5b-instruct

Alias jalur metode

Gunakan jalur metode yang ditampilkan di atas.

ID pemanggil terkait

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": "bcb089e5c015526cf38eaec1b486843fbdc4bee87dc8ebb6fa101bd658ebd732", "input_html_sha256": "c59388268424679b8dec57b021d49a5b9b07da35393aa6015cff9437b23713ee", "private_audit_sha256": "d1f2e1370fc592574cdaa6a5e4015d7e523f7f9a703b66e8d5fed8cbcc52a1c8", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/id/qwen2-0-5b-instruct/", "description": "qwen2-0.5b-instruct adalah model teks Tongyi Qianwen untuk permintaan berbasis pesan. Gunakan ID persisnya dengan Chat Completions untuk mengolah bacaan yang diberikan: menyusun balasan, mengubah catatan menjadi daftar periksa, mengekstrak kolom tertentu, atau menjelaskan fragmen kode singkat. Alokasi masukan teksnya adalah 32.000 token.", "locale": "id", "md_url": "https://ailoot.ai/models/id/qwen2-0-5b-instruct/index.md", "title": "qwen2-0.5b-instruct — Ailoot", "url": "https://ailoot.ai/models/id/qwen2-0-5b-instruct/" }, "rawModel": { "about": "qwen2-0.5b-instruct adalah pilihan model persis untuk profil percakapan teks Tongyi Qianwen ini. Kirim instruksi dan materi sumber sebagai teks lalu minta hasil tertulis yang konkret. Prompt praktis dapat mengubah pesan pelanggan menjadi usulan balasan, menata ulang catatan rapat, atau mengekstrak sekumpulan kecil fakta dari bacaan yang diberikan. Tentukan sasaran pembaca dan struktur jawaban yang diinginkan agar aplikasi memiliki hasil yang jelas untuk digunakan.\n\nGunakan POST /v1/chat/completions dengan model diatur ke qwen2-0.5b-instruct dan array messages yang tidak kosong. Setiap pesan menyertakan role dan content berupa teks. Sertakan giliran sebelumnya yang relevan ketika tindak lanjut bergantung pada percakapan. Untuk pekerjaan dokumen, baca teks UTF-8 yang relevan dari dokumen dan masukkan teks aktual tersebut ke dalam pesan. Simpan judul bagian atau label sumber bersama setiap kutipan ketika jawaban perlu membedakan beberapa bacaan.\n\nAlokasi masukan teks adalah 32.000 token. Pertahankan instruksi, bacaan sumber, dan riwayat percakapan dalam anggaran masukan tersebut. Untuk dokumen panjang, pilih bagian yang diperlukan bagi pertanyaan saat ini atau proses kutipan berlabel jelas sebagai tugas terpisah. Nyatakan bacaan yang harus digunakan jawaban dan apa yang harus dikembalikan ketika kolom yang diminta tidak terdapat di dalamnya.\n\nUntuk tugas penyusunan balasan, berikan kata-kata pelanggan bersama informasi produk yang disetujui dan minta respons dengan langkah berikutnya yang spesifik. Untuk ekstraksi, daftarkan kolom seperti masalah, tindakan yang diminta, dan bukti kutipan, lalu minta model mengembalikan hanya fakta yang ditemukan dalam teks yang diberikan. Untuk penjelasan kode, tempel fungsi dan minta deskripsi masukan, transformasi, dan keluarannya. Instruksi tersebut menentukan tugas di dalam prompt dan menjaga hasil tetap terikat pada materi yang Anda berikan.\n\nKontrol boolean stream yang opsional memilih cara pengiriman respons: stream:false, nilai bawaan, mengembalikan respons JSON yang telah dirangkai; stream:true mengirim peristiwa saat respons dihasilkan. Untuk JSON, baca choices dan isi pesan yang dikembalikan serta simpan usage jika disertakan. Untuk streaming, rangkai delta content untuk setiap choice dan simpan informasi penyelesaian akhir sebelum menyimpan jawaban sebagai lengkap. Simpan respons asli dan identitas permintaan ketika permintaan berakhir dengan hasil yang ambigu.", "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 adalah model teks Tongyi Qianwen untuk permintaan berbasis pesan. Gunakan ID persisnya dengan Chat Completions untuk mengolah bacaan yang diberikan: menyusun balasan, mengubah catatan menjadi daftar periksa, mengekstrak kolom tertentu, atau menjelaskan fragmen kode singkat. Alokasi masukan teksnya adalah 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 adalah model teks Tongyi Qianwen untuk permintaan berbasis pesan. Gunakan ID persisnya dengan Chat Completions untuk mengolah bacaan yang diberikan: menyusun balasan, mengubah catatan menjadi daftar periksa, mengekstrak kolom tertentu, atau menjelaskan fragmen kode singkat. Alokasi masukan teksnya adalah 32.000 token.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "0be9a4c424e772724e92c32c61208cbec93a693bbbb5912c1c2a4fc488688768", "renderer_sha256": "2a0471475a29000c23db326ca1e315df73dee7524f6bc2907818ee3458813f2d" }, "sourceRevision": "060f3bd93f5f267555437ad761cafb87a21e98b14d63965e14a3c0539acc09f8" }