{ "aboutHtml": "

qwen2-0.5b-instruct ialah pilihan model tepat untuk profil perbualan teks Tongyi Qianwen ini. Hantar arahan dan bahan sumber sebagai teks dan minta hasil bertulis yang khusus. Prompt praktikal boleh mengubah mesej pelanggan menjadi cadangan balasan, menyusun semula nota mesyuarat atau mengekstrak set fakta kecil daripada petikan yang dibekalkan. Tentukan khalayak dan struktur jawapan yang dikehendaki supaya aplikasi mempunyai hasil yang jelas untuk digunakan.

Gunakan POST /v1/chat/completions dengan model ditetapkan kepada qwen2-0.5b-instruct dan tatasusunan messages yang tidak kosong. Setiap mesej membekalkan role dan content teks. Sertakan giliran terdahulu yang berkaitan apabila susulan bergantung pada perbualan. Untuk kerja dokumen, baca teks UTF-8 yang berkaitan daripada dokumen dan letakkan teks sebenar itu dalam mesej. Kekalkan tajuk bahagian atau label sumber bersama setiap petikan apabila jawapan perlu membezakan beberapa petikan.

Peruntukan input teks ialah 32,000 token. Pastikan arahan, petikan sumber dan sejarah perbualan berada dalam peruntukan input itu. Untuk dokumen yang panjang, pilih bahagian yang diperlukan untuk soalan semasa atau proses petikan berlabel jelas sebagai tugasan berasingan. Nyatakan petikan yang patut digunakan oleh jawapan dan perkara yang perlu dikembalikan apabila medan yang diminta tiada dalam petikan.

Untuk tugasan mendraf balasan, bekalkan kata-kata pelanggan bersama maklumat produk yang diluluskan dan minta balasan dengan langkah seterusnya yang khusus. Untuk pengekstrakan, senaraikan medan seperti isu, tindakan yang diminta dan bukti yang dipetik, kemudian minta model mengembalikan hanya fakta yang terdapat dalam teks yang dibekalkan. Untuk penerangan kod, tampalkan fungsi dan minta penerangan tentang input, transformasi dan outputnya. Arahan ini mentakrifkan tugasan dalam prompt dan memastikan hasil terikat pada bahan yang anda bekalkan.

Kawalan boolean stream pilihan memilih cara penghantaran balasan: stream:false, nilai lalai, mengembalikan balasan JSON yang telah digabungkan; stream:true menghantar peristiwa semasa balasan dihasilkan. Untuk JSON, baca choices dan kandungan mesej yang dikembalikan serta simpan usage apabila dibekalkan. Untuk penstriman, gabungkan delta kandungan bagi setiap pilihan dan simpan maklumat penamat akhir sebelum menyimpan jawapan sebagai lengkap. Simpan balasan asal dan identiti permintaan apabila permintaan berakhir secara kabur.

", "aboutText": "qwen2-0.5b-instruct ialah pilihan model tepat untuk profil perbualan teks Tongyi Qianwen ini. Hantar arahan dan bahan sumber sebagai teks dan minta hasil bertulis yang khusus. Prompt praktikal boleh mengubah mesej pelanggan menjadi cadangan balasan, menyusun semula nota mesyuarat atau mengekstrak set fakta kecil daripada petikan yang dibekalkan. Tentukan khalayak dan struktur jawapan yang dikehendaki supaya aplikasi mempunyai hasil yang jelas untuk digunakan.\n\nGunakan POST /v1/chat/completions dengan model ditetapkan kepada qwen2-0.5b-instruct dan tatasusunan messages yang tidak kosong. Setiap mesej membekalkan role dan content teks. Sertakan giliran terdahulu yang berkaitan apabila susulan bergantung pada perbualan. Untuk kerja dokumen, baca teks UTF-8 yang berkaitan daripada dokumen dan letakkan teks sebenar itu dalam mesej. Kekalkan tajuk bahagian atau label sumber bersama setiap petikan apabila jawapan perlu membezakan beberapa petikan.\n\nPeruntukan input teks ialah 32,000 token. Pastikan arahan, petikan sumber dan sejarah perbualan berada dalam peruntukan input itu. Untuk dokumen yang panjang, pilih bahagian yang diperlukan untuk soalan semasa atau proses petikan berlabel jelas sebagai tugasan berasingan. Nyatakan petikan yang patut digunakan oleh jawapan dan perkara yang perlu dikembalikan apabila medan yang diminta tiada dalam petikan.\n\nUntuk tugasan mendraf balasan, bekalkan kata-kata pelanggan bersama maklumat produk yang diluluskan dan minta balasan dengan langkah seterusnya yang khusus. Untuk pengekstrakan, senaraikan medan seperti isu, tindakan yang diminta dan bukti yang dipetik, kemudian minta model mengembalikan hanya fakta yang terdapat dalam teks yang dibekalkan. Untuk penerangan kod, tampalkan fungsi dan minta penerangan tentang input, transformasi dan outputnya. Arahan ini mentakrifkan tugasan dalam prompt dan memastikan hasil terikat pada bahan yang anda bekalkan.\n\nKawalan boolean stream pilihan memilih cara penghantaran balasan: stream:false, nilai lalai, mengembalikan balasan JSON yang telah digabungkan; stream:true menghantar peristiwa semasa balasan dihasilkan. Untuk JSON, baca choices dan kandungan mesej yang dikembalikan serta simpan usage apabila dibekalkan. Untuk penstriman, gabungkan delta kandungan bagi setiap pilihan dan simpan maklumat penamat akhir sebelum menyimpan jawapan sebagai lengkap. Simpan balasan asal dan identiti permintaan apabila permintaan berakhir secara kabur.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "9269e2e9e05bac03d402a7c38189777f258fb3796812d7016f03afefe6891141", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "ms", "methods": [ { "html": "
Al

qwen2-0.5b-instruct

Alibaba /qwen2-0.5b-instruct

Pengenalan

qwen2-0.5b-instruct ialah model teks Tongyi Qianwen untuk permintaan berasaskan mesej. Gunakan ID tepatnya dengan Chat Completions untuk mengolah petikan yang dibekalkan: mendraf balasan, mengubah nota menjadi senarai semak, mengekstrak medan yang dinyatakan atau menerangkan cebisan kod pendek. Peruntukan input teksnya ialah 32,000 token.

Input yang diterima
Teks
Hasil
Teks
Tentang model

Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih.

qwen2-0.5b-instruct ialah pilihan model tepat untuk profil perbualan teks Tongyi Qianwen ini. Hantar arahan dan bahan sumber sebagai teks dan minta hasil bertulis yang khusus. Prompt praktikal boleh mengubah mesej pelanggan menjadi cadangan balasan, menyusun semula nota mesyuarat atau mengekstrak set fakta kecil daripada petikan yang dibekalkan. Tentukan khalayak dan struktur jawapan yang dikehendaki supaya aplikasi mempunyai hasil yang jelas untuk digunakan.

Gunakan POST /v1/chat/completions dengan model ditetapkan kepada qwen2-0.5b-instruct dan tatasusunan messages yang tidak kosong. Setiap mesej membekalkan role dan content teks. Sertakan giliran terdahulu yang berkaitan apabila susulan bergantung pada perbualan. Untuk kerja dokumen, baca teks UTF-8 yang berkaitan daripada dokumen dan letakkan teks sebenar itu dalam mesej. Kekalkan tajuk bahagian atau label sumber bersama setiap petikan apabila jawapan perlu membezakan beberapa petikan.

Peruntukan input teks ialah 32,000 token. Pastikan arahan, petikan sumber dan sejarah perbualan berada dalam peruntukan input itu. Untuk dokumen yang panjang, pilih bahagian yang diperlukan untuk soalan semasa atau proses petikan berlabel jelas sebagai tugasan berasingan. Nyatakan petikan yang patut digunakan oleh jawapan dan perkara yang perlu dikembalikan apabila medan yang diminta tiada dalam petikan.

Untuk tugasan mendraf balasan, bekalkan kata-kata pelanggan bersama maklumat produk yang diluluskan dan minta balasan dengan langkah seterusnya yang khusus. Untuk pengekstrakan, senaraikan medan seperti isu, tindakan yang diminta dan bukti yang dipetik, kemudian minta model mengembalikan hanya fakta yang terdapat dalam teks yang dibekalkan. Untuk penerangan kod, tampalkan fungsi dan minta penerangan tentang input, transformasi dan outputnya. Arahan ini mentakrifkan tugasan dalam prompt dan memastikan hasil terikat pada bahan yang anda bekalkan.

Kawalan boolean stream pilihan memilih cara penghantaran balasan: stream:false, nilai lalai, mengembalikan balasan JSON yang telah digabungkan; stream:true menghantar peristiwa semasa balasan dihasilkan. Untuk JSON, baca choices dan kandungan mesej yang dikembalikan serta simpan usage apabila dibekalkan. Untuk penstriman, gabungkan delta kandungan bagi setiap pilihan dan simpan maklumat penamat akhir sebelum menyimpan jawapan sebagai lengkap. Simpan balasan asal dan identiti permintaan apabila permintaan berakhir secara kabur.

Tersedia di Ailoot · input & kaedah

Ailoot Ultra · Chat Completions POST /v1/chat/completions
Input
text
Output
text
Konteks
32,000 tokens
Format output
JSON or supported SSE
MedanJenisKeperluanNilai & hadLalai
MedanmodelJenisstringKeperluanDiperlukanNilai & had
qwen2-0.5b-instruct
Lalai—
MedanmessagesJenisarrayKeperluanDiperlukanNilai & hadLalai—
MedanstreamJenisbooleanKeperluanPilihanNilai & hadLalaifalse
Medanmax_tokensJenisintegerKeperluanPilihanNilai & had
Minimum: 1
Lalai—
Medanmax_completion_tokensJenisintegerKeperluanPilihanNilai & had
Minimum: 1
Lalai—
MedannJenisintegerKeperluanPilihanNilai & had
Minimum: 1
Lalai—
MedantemperatureJenisnumberKeperluanPilihanNilai & hadLalai—
Medantop_pJenisnumberKeperluanPilihanNilai & hadLalai—
MedanseedJenisintegerKeperluanPilihanNilai & hadLalai—
MedanstopJenisKeperluanPilihanNilai & hadLalai—
Medanfrequency_penaltyJenisnumberKeperluanPilihanNilai & hadLalai—
Medanpresence_penaltyJenisnumberKeperluanPilihanNilai & hadLalai—
MedanuserJenisstringKeperluanPilihanNilai & hadLalai—
MedantoolsJenisarrayKeperluanPilihanNilai & hadLalai—
Medanservice_tierJenisstringKeperluanPilihanNilai & had
, auto, default
Lalai—
MedanmodalitiesJenisarrayKeperluanPilihanNilai & hadLalai—
Parameter kaedah dan kitar hayat
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.

Kitar hayat: synchronous_json_or_sse

Pelan harga

Rujukan tarif tidak menambah pilihan permintaan. Parameter yang disokong disenaraikan untuk kaedah yang dipilih.

Mod pantas
Kadar standard Apabila mod pantas dipilih
Mod flex
Kadar standard Apabila mod flex dipilih
Usaha penaakulan maksimum
Kadar standard Apabila usaha penaakulan maksimum menggunakan pelarasan default
Harga cache eksplisit
Tidak diaktifkan Apabila cache eksplisit diminta
Kadar sifar untuk teks sahaja
Data cache, alat dan media tertakluk pada syarat harga masing-masing yang disenaraikan secara berasingan. Untuk input dan output teks sahaja

Kadar Ailoot

Harga mengikut konfigurasi

KonfigurasiJadual kadarCaj
KonfigurasiKadar asas tokenJadual kadarKadar asasCaj
Input · Token input $0 / 1K token
Output · Token output $0 / 1K token
Input · Bacaan cache $0 / 1K token
Input · Penulisan cache $0 / 1K token
Input · Penulisan cache, 1 jam — / 1K token

Ailoot merizabkan anggaran amaun sebelum permintaan. Selepas hasil diterima, caj akhir diselesaikan berdasarkan penggunaan terukur. Penyelesaian caj penstriman menggunakan rekod penggunaan akhir. Cara rizab dan penyelesaian caj berfungsi →

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, fail dan bahagian kandungan yang dikembalikan.

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

Jika respons asal perlu disemak

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 laluan kaedah

Gunakan laluan kaedah yang dipaparkan di atas.

ID pemanggil berkaitan

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": "a4eb8be7ef26e7f4ad7ce00d87fa3f7db00e20f3c722d8013d669599fb9453de", "input_html_sha256": "4639a38adf2f870b11947e08d5259f1faaec89459c4be4e75e4987c31eba1d15", "private_audit_sha256": "c2c80e45bc714b90be6898fb023ee65d036e0e115b219c58b887fdbfcd349edf", "role": "historical-normalized-input-audit-hashes", "schema": "ailoot-native-model-provenance-v2" }, "page": { "canonical": "https://ailoot.ai/models/ms/qwen2-0-5b-instruct/", "description": "qwen2-0.5b-instruct ialah model teks Tongyi Qianwen untuk permintaan berasaskan mesej. Gunakan ID tepatnya dengan Chat Completions untuk mengolah petikan yang dibekalkan: mendraf balasan, mengubah nota menjadi senarai semak, mengekstrak medan yang dinyatakan atau menerangkan cebisan kod pendek. Peruntukan input teksnya ialah 32,000 token.", "locale": "ms", "md_url": "https://ailoot.ai/models/ms/qwen2-0-5b-instruct/index.md", "title": "qwen2-0.5b-instruct — Ailoot", "url": "https://ailoot.ai/models/ms/qwen2-0-5b-instruct/" }, "rawModel": { "about": "qwen2-0.5b-instruct ialah pilihan model tepat untuk profil perbualan teks Tongyi Qianwen ini. Hantar arahan dan bahan sumber sebagai teks dan minta hasil bertulis yang khusus. Prompt praktikal boleh mengubah mesej pelanggan menjadi cadangan balasan, menyusun semula nota mesyuarat atau mengekstrak set fakta kecil daripada petikan yang dibekalkan. Tentukan khalayak dan struktur jawapan yang dikehendaki supaya aplikasi mempunyai hasil yang jelas untuk digunakan.\n\nGunakan POST /v1/chat/completions dengan model ditetapkan kepada qwen2-0.5b-instruct dan tatasusunan messages yang tidak kosong. Setiap mesej membekalkan role dan content teks. Sertakan giliran terdahulu yang berkaitan apabila susulan bergantung pada perbualan. Untuk kerja dokumen, baca teks UTF-8 yang berkaitan daripada dokumen dan letakkan teks sebenar itu dalam mesej. Kekalkan tajuk bahagian atau label sumber bersama setiap petikan apabila jawapan perlu membezakan beberapa petikan.\n\nPeruntukan input teks ialah 32,000 token. Pastikan arahan, petikan sumber dan sejarah perbualan berada dalam peruntukan input itu. Untuk dokumen yang panjang, pilih bahagian yang diperlukan untuk soalan semasa atau proses petikan berlabel jelas sebagai tugasan berasingan. Nyatakan petikan yang patut digunakan oleh jawapan dan perkara yang perlu dikembalikan apabila medan yang diminta tiada dalam petikan.\n\nUntuk tugasan mendraf balasan, bekalkan kata-kata pelanggan bersama maklumat produk yang diluluskan dan minta balasan dengan langkah seterusnya yang khusus. Untuk pengekstrakan, senaraikan medan seperti isu, tindakan yang diminta dan bukti yang dipetik, kemudian minta model mengembalikan hanya fakta yang terdapat dalam teks yang dibekalkan. Untuk penerangan kod, tampalkan fungsi dan minta penerangan tentang input, transformasi dan outputnya. Arahan ini mentakrifkan tugasan dalam prompt dan memastikan hasil terikat pada bahan yang anda bekalkan.\n\nKawalan boolean stream pilihan memilih cara penghantaran balasan: stream:false, nilai lalai, mengembalikan balasan JSON yang telah digabungkan; stream:true menghantar peristiwa semasa balasan dihasilkan. Untuk JSON, baca choices dan kandungan mesej yang dikembalikan serta simpan usage apabila dibekalkan. Untuk penstriman, gabungkan delta kandungan bagi setiap pilihan dan simpan maklumat penamat akhir sebelum menyimpan jawapan sebagai lengkap. Simpan balasan asal dan identiti permintaan apabila permintaan berakhir secara kabur.", "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 ialah model teks Tongyi Qianwen untuk permintaan berasaskan mesej. Gunakan ID tepatnya dengan Chat Completions untuk mengolah petikan yang dibekalkan: mendraf balasan, mengubah nota menjadi senarai semak, mengekstrak medan yang dinyatakan atau menerangkan cebisan kod pendek. Peruntukan input teksnya ialah 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 ialah model teks Tongyi Qianwen untuk permintaan berasaskan mesej. Gunakan ID tepatnya dengan Chat Completions untuk mengolah petikan yang dibekalkan: mendraf balasan, mengubah nota menjadi senarai semak, mengekstrak medan yang dinyatakan atau menerangkan cebisan kod pendek. Peruntukan input teksnya ialah 32,000 token.", "source": { "audit_sha256": "2e00086bcbfc28e64d0cebe97aa47c17d8d5618963027f8ffb9dc6123d7d25b1", "bridge_map_sha256": "1125275768b9636fc8461bc7da5061239a81ddfe508e2557843ca42c58ab0036", "raw_model_json_sha256": "ff6a12400c6135eca45e4dc755f414d41294544a17aab1584e2ffd6032e71c13", "renderer_sha256": "1dab5ba62521e9568c8b7c82005622cabbf330a075df7cce1f83274a04009460" }, "sourceRevision": "c9993588c2be5320b01ae53047b3eb4c5206be3b5460aff4f5350253c185fd96" }