Source: https://ailoot.ai/models/ms/nvidia-nemotron-3-5-lightning-30b-a3b/ Canonical: https://ailoot.ai/models/ms/nvidia-nemotron-3-5-lightning-30b-a3b/ Language: ms # Nemotron 3.5 Lightning 30B A3B [Markdown](https://ailoot.ai/models/ms/nvidia-nemotron-3-5-lightning-30b-a3b/index.md) · [JSON](https://ailoot.ai/models/ms/nvidia-nemotron-3-5-lightning-30b-a3b/facts.json) ## [Nemotron 3.5 Lightning 30B A3B](https://ailoot.ai/models/ms/nvidia-nemotron-3-5-lightning-30b-a3b/) Nemotron 3.5 Lightning ialah model teks NVIDIA untuk tugasan ejen panjang, pengaturcaraan, carian dokumen dan dialog. - [**Ailoot · Anthropic Messages** `POST /v1/messages` `nvidia/nemotron-3.5-lightning-30b-a3b`](https://ailoot.ai/models/ms/nvidia-nemotron-3-5-lightning-30b-a3b/#method-0) - [**Ailoot · OpenAI Chat** `POST /v1/chat/completions` `nvidia/nemotron-3.5-lightning-30b-a3b`](https://ailoot.ai/models/ms/nvidia-nemotron-3-5-lightning-30b-a3b/#method-1) - [**Ailoot · OpenAI Chat** `POST /v1/chat/completions` `nemotron-lightning`](https://ailoot.ai/models/ms/nvidia-nemotron-3-5-lightning-30b-a3b/#method-2) - [**Ailoot · OpenAI Chat** `POST /v1/chat/completions` `nemotron-lightning-latest`](https://ailoot.ai/models/ms/nvidia-nemotron-3-5-lightning-30b-a3b/#method-3) Tentang model Huraian ini merangkumi keupayaan model. Input, output dan pilihan yang tersedia di Ailoot disenaraikan di bawah untuk kaedah yang dipilih. #### Model kerja untuk proses berpanjangan Nemotron 3.5 Lightning direka untuk ejen autonomi dan urutan tindakan yang diteruskan melalui beberapa langkah. Model turut digunakan dalam pembantu perbualan, carian berasaskan dokumen dan pelaksanaan arahan. Input dan output berupa teks. #### Seni bina dan konteks Campuran pakar hibrid menggabungkan Mamba-2 dan lapisan perhatian berasingan. Model mengandungi 30 bilion parameter keseluruhan, dengan 3 bilion aktif bagi setiap token. Konteks mencapai sejuta token. Lapisan ramalan beberapa token seterusnya dilatih tambahan untuk menyelaraskan cadangannya dengan taburan model utama. Kaedah penyahkodan spekulatif dibentangkan bersama keluaran ini. Kaedah ini membantu menyusun penjanaan, tetapi tidak menetapkan kelajuan permintaan yang dijamin. #### Lima peringkat persediaan 1. Pralatihan menggunakan lebih daripada 20 trilion token kod, matematik, sains dan pengetahuan umum dengan persediaan dalam format NVFP4. 2. Penerusan pralatihan bagi lapisan ramalan beberapa token akan datang: cadangannya diselaraskan dengan taburan model utama. 3. Latihan berdasarkan contoh, termasuk arahan, panggilan alat, hasil berstruktur, pencarian petikan yang berjauhan dan penggabungan dokumen. 4. Pembelajaran pengukuhan dalam beberapa persekitaran untuk penaakulan, kod, sains, tugasan alat berbilang langkah dan dialog. 5. Pemampatan angka seterusnya bagi varian yang diterbitkan. Peringkat terakhir mencirikan keluaran pengeluar khusus dan tidak menetapkan ketepatan pengiraan permintaan yang dilayan. Dalam proses luaran, model mencadangkan tindakan dan hasil teks; alat dilaksanakan oleh aplikasi. #### Bahasa dan versi Bahasa Inggeris, Sepanyol, Perancis, Jerman, Itali dan Jepun serta bahasa pengaturcaraan disenaraikan secara rasmi. Batas data sehingga September 2025 dinyatakan bagi pralatihan, manakala sehingga Mei 2026 bagi latihan seterusnya. Ciri keluaran yang diterbitkan dengan pemampatan angka menerangkan khususnya varian tersebut. Ketepatan pengiraan pelaksanaan tertentu tidak boleh ditentukan daripada nama model. Sejuta token ialah konteks keseluruhan, bukannya janji panjang output yang berasingan. Tugas praktikal ### Tugas praktikal Contoh teknikal dalam bahasa Inggeris ``` Apply a document-grounded procedure Use these procedure excerpts to draft the next action for case C-09. [Rule 1] Packages with a damaged seal go to inspection. [Rule 2] Only inspection-cleared packages enter stock. [Record] C-09 has a damaged seal and no inspection result. Return the current permitted step, blocked step and supporting rule labels. ``` #### Hasil yang dijangka ``` Three concise fields covering the permitted inspection step, blocked stocking and source evidence. ``` #### Semak hasil ``` Check that missing inspection clearance prevents stocking and does not become an assumed failed inspection. ``` **Ailoot · Anthropic Messages** `POST /v1/messages` `nvidia/nemotron-3.5-lightning-30b-a3b` [`nvidia/nemotron-3.5-lightning-30b-a3b`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nvidia%2Fnemotron-3.5-lightning-30b-a3b&fmt=messages) NVIDIA /`nvidia/nemotron-3.5-lightning-30b-a3b` ### Pengenalan Nemotron 3.5 Lightning ialah model teks NVIDIA untuk tugasan ejen panjang, pengaturcaraan, carian dokumen dan dialog. Input yang diterima Teks Hasil Teks ### Tersedia di Ailoot · input & kaedah **Ailoot · Anthropic Messages** `POST /v1/messages` Input `text` Output `text` Konteks 262,144 tokens Output maksimum 32,768 tokens Format output json atau SSE | Medan | Jenis | Keperluan | Nilai & had | Lalai | | --- | --- | --- | --- | --- | | Medan`model` | Jenis`string` | KeperluanDiperlukan | Nilai & had nvidia/nemotron-3.5-lightning-30b-a3b | Lalai— | | Medan`messages` | Jenis`message[]` | KeperluanDiperlukan | Nilai & had Item minimum: 1 | Lalai— | | Medan`messages[].role` | Jenis`string` | KeperluanDiperlukan | Nilai & had user, assistant | Lalai— | | Medan`messages[].content` | Jenis`string\|content_block[]` | KeperluanDiperlukan | Nilai & had | Lalai— | | Medan`system` | Jenis`string\|content_block[]` | KeperluanPilihan | Nilai & had | Lalai— | | Medan`max_tokens` | Jenis`integer` | KeperluanDiperlukan | Nilai & had Minimum: 1 Had terpakai pada: had maksimum tepat bagi model/kad yang dipilih | Lalai— | | Medan`stream` | Jenis`boolean` | KeperluanPilihan | Nilai & had | Lalaifalse | | Medan`temperature` | Jenis`number` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`top_p` | Jenis`number` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`stop_sequences` | Jenis`string[]` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`tools` | Jenis`tool[]` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`tools[].name` | Jenis`string` | KeperluanPilihan | Nilai & had Terpakai apabila: alat dibekalkan | Lalai— | | Medan`tools[].description` | Jenis`string` | KeperluanPilihan | Nilai & had | Lalai— | | Medan`tools[].input_schema` | Jenis`JSON_schema` | KeperluanPilihan | Nilai & had | Lalai— | | Medan`tool_choice` | Jenis`object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`thinking.type` | Jenis`string` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`thinking.budget_tokens` | Jenis`integer` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`output_config.effort` | Jenis`string` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`metadata` | Jenis`object` | KeperluanPilihan | Nilai & had | Lalai— | | Medan`cache_control` | Jenis`object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | Gabungan bahan input **Teks** · Teks UTF8 `messages[].content atau blok type=text` Parameter kaedah dan kitar hayat | Parameter | Kontrak | | --- | --- | | Parameter`material_types` | Kontraktext | | Parameter`required` | Kontrakmodel, messages, max\_tokens | | Parameter`optional_controls` | Kontraknull | | Parameter`content_type` | Kontrakapplication/json | | Parameter`text_file_material` | Kontrakkandungan UTF-8 sebenar dimasukkan sebagai teks; ID fail atau alamat URL (URLs) memerlukan sokongan tepat yang berasingan | Kitar hayat: `exact retained published method` Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan. ### Pelan harga #### Kadar kaedah Ailoot Input USD 0.00008 */ 1K* Output USD 0.0002 */ 1K* Bacaan cache USD 0.00008 */ 1K* Penulisan cache USD 0.00008 */ 1K* Penulisan cache, 5 minit USD 0.00008 */ 1K tokens* 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 →](https://ailoot.ai/docs/ms/billing/reserve/) ### Contoh permintaan #### Ailoot · Anthropic Messages Contoh teksPython SDKNode.js SDKPenstriman Python ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" : "${AILOOT_TEXT_FILE:?Set your actual UTF-8 source-material file}" export AILOOT_TEXT_FILE python3 - <<'PY_REQUEST' import json,os from pathlib import Path p=json.loads('{"model": "nvidia/nemotron-3.5-lightning-30b-a3b", "max_tokens": 1024, "messages": []}') f=Path(os.environ["AILOOT_TEXT_FILE"]) if not f.is_file():raise SystemExit("Choose your actual UTF-8 source material") text=f.read_text(encoding="utf-8") if not text.strip():raise SystemExit("Source material must contain text") p["messages"]=[{"role":"user","content":"Summarize this material and quote supporting source passages.\n\n"+text}] Path("request.json").write_text(json.dumps(p,ensure_ascii=False)) PY_REQUEST curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \ -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \ --data-binary @request.json -D response-headers.txt -o result.json python3 - <<'PY_RESULT' import json from pathlib import Path print(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2)) PY_RESULT ``` ```python import anthropic import os if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request") client = anthropic.Anthropic(base_url="https://api.ailoot.ai", api_key=os.environ["AILOOT_API_KEY"], max_retries=0) msg = client.messages.create( model="nvidia/nemotron-3.5-lightning-30b-a3b", max_tokens=1024, messages=[{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}], ) import base64, json from pathlib import Path def save_result(data, stem="result"): # Keep every field, including usage, IDs, reasoning and tool requests. Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8") print(json.dumps(data, ensure_ascii=False, indent=2)) def save_blob(encoded, suffix, label): raw = base64.b64decode(encoded, altchars=b"-_", validate=True) ext = "png" if raw.startswith(b"\x89PNG\r\n\x1a\n") else "jpg" if raw.startswith(b"\xff\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix file = Path(stem + "-" + label + "." + ext) file.write_bytes(raw) print("Saved", file) for index, block in enumerate(data.get("content") or []): print("Content", index, json.dumps(block, ensure_ascii=False)) for choice in data.get("choices") or []: print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False)) for index, item in enumerate(data.get("output") or []): print("Output", index, json.dumps(item, ensure_ascii=False)) if item.get("type") == "image_generation_call" and item.get("result"): save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index)) for ci, candidate in enumerate(data.get("candidates") or []): for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []): print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False)) blob = part.get("inlineData") or part.get("inline_data") if blob and blob.get("data"): mime = blob.get("mimeType") or blob.get("mime_type") suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin") save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi)) save_result(msg.to_dict(mode="json")) ``` ```js import Anthropic from "@anthropic-ai/sdk"; if (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request"); const client = new Anthropic({ baseURL: "https://api.ailoot.ai", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 }); const msg = await client.messages.create({ model: "nvidia/nemotron-3.5-lightning-30b-a3b", max_tokens: 1024, messages: [{ role: "user", content: "Explain the difference between an observation and a hypothesis in three sentences." }], }); import { writeFileSync } from "node:fs"; function saveResult(data, stem = "result") { writeFileSync(stem + ".json", JSON.stringify(data, null, 2)); console.log(JSON.stringify(data, null, 2)); const saveBlob = (encoded, suffix, label) => { const raw = Buffer.from(encoded, "base64"); const ext = raw.subarray(0,8).equals(Buffer.from([137,80,78,71,13,10,26,10])) ? "png" : raw[0]===255 && raw[1]===216 ? "jpg" : raw.subarray(0,4).toString()==="RIFF" && raw.subarray(8,12).toString()==="WEBP" ? "webp" : suffix; const file = `${stem}-${label}.${ext}`; writeFileSync(file, raw); console.log("Saved", file); }; for (const [i, block] of (data.content || []).entries()) console.log("Content", i, JSON.stringify(block)); for (const choice of data.choices || []) console.log("Choice", choice.index, JSON.stringify(choice.message || choice.delta || {})); for (const [i, item] of (data.output || []).entries()) { console.log("Output", i, JSON.stringify(item)); if (item.type === "image_generation_call" && item.result) saveBlob(item.result, item.output_format || "bin", `output-${i}`); } for (const [ci, candidate] of (data.candidates || []).entries()) { for (const [pi, part] of (candidate.content?.parts || []).entries()) { console.log("Candidate/part", ci, pi, JSON.stringify(part)); const blob = part.inlineData || part.inline_data; if (blob?.data) { const mime = blob.mimeType || blob.mime_type; const suffix = ({"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"})[mime] || "bin"; saveBlob(blob.data, suffix, `candidate-${ci}-part-${pi}`); } } } } saveResult(msg); ``` ```python import anthropic,os from pathlib import Path if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request") client=anthropic.Anthropic(base_url="https://api.ailoot.ai",api_key=os.environ["AILOOT_API_KEY"],max_retries=0) import base64, json from pathlib import Path def save_result(data, stem="result"): # Keep every field, including usage, IDs, reasoning and tool requests. Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8") print(json.dumps(data, ensure_ascii=False, indent=2)) def save_blob(encoded, suffix, label): raw = base64.b64decode(encoded, altchars=b"-_", validate=True) ext = "png" if raw.startswith(b"\x89PNG\r\n\x1a\n") else "jpg" if raw.startswith(b"\xff\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix file = Path(stem + "-" + label + "." + ext) file.write_bytes(raw) print("Saved", file) for index, block in enumerate(data.get("content") or []): print("Content", index, json.dumps(block, ensure_ascii=False)) for choice in data.get("choices") or []: print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False)) for index, item in enumerate(data.get("output") or []): print("Output", index, json.dumps(item, ensure_ascii=False)) if item.get("type") == "image_generation_call" and item.get("result"): save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index)) for ci, candidate in enumerate(data.get("candidates") or []): for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []): print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False)) blob = part.get("inlineData") or part.get("inline_data") if blob and blob.get("data"): mime = blob.get("mimeType") or blob.get("mime_type") suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin") save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi)) with client.messages.stream(model='nvidia/nemotron-3.5-lightning-30b-a3b',max_tokens=1024,messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream: with Path("stream-events.jsonl").open("w",encoding="utf-8") as log: for event in stream: payload=event.to_dict(mode="json") log.write(json.dumps(payload,ensure_ascii=False)+"\n"); log.flush() print(json.dumps(payload,ensure_ascii=False)) final=stream.get_final_message() save_result(final.to_dict(mode="json"),"stream-result") ``` ### Terima dan simpan hasil #### Ailoot · Anthropic Messages json atau SSE ##### Simpan semua hasil yang selesai Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan. Kekalkan semua blok kandungan, tandatangan penaakulan dan ID penggunaan alat sebenar. Gabungkan semua delta blok SSE; blok teks pertama bukan keseluruhan output. ##### Jika respons asal perlu disemak Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan. Medan respons | Medan | Jenis | | --- | --- | | Medan`id` | Jenisstring | | Medan`model` | Jenisstring | | Medan`content[]` | Jenistext\|thinking\|tool\_use\|other content block | | Medan`stop_reason` | Jenisstring\|null | | Medan`usage` | Jenisobject | Ralat dan langkah seterusnya | Respons | Syarat | Langkah seterusnya | | --- | --- | --- | | Respons400 · `invalid_request_error` | SyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sah | Langkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu. | ### Alias #### Alias model **Ailoot** [`nvidia/nemotron-3.5-lightning-30b-a3b`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nvidia%2Fnemotron-3.5-lightning-30b-a3b&fmt=messages), [`nemotron-lightning`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nemotron-lightning), [`nemotron-lightning-latest`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nemotron-lightning-latest) #### Alias laluan kaedah Gunakan laluan kaedah yang dipaparkan di atas. #### ID pemanggil berkaitan [`nvidia/nemotron-3.5-lightning-30b-a3b`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nvidia%2Fnemotron-3.5-lightning-30b-a3b&fmt=messages), [`nemotron-lightning`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nemotron-lightning), [`nemotron-lightning-latest`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nemotron-lightning-latest) **Ailoot · OpenAI Chat** `POST /v1/chat/completions` `nvidia/nemotron-3.5-lightning-30b-a3b` [`nvidia/nemotron-3.5-lightning-30b-a3b`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nvidia%2Fnemotron-3.5-lightning-30b-a3b&fmt=chat) NVIDIA /`nvidia/nemotron-3.5-lightning-30b-a3b` ID model - `nvidia/nemotron-3.5-lightning-30b-a3b` - `nemotron-lightning` - `nemotron-lightning-latest` ### Pengenalan Nemotron 3.5 Lightning ialah model teks NVIDIA untuk tugasan ejen panjang, pengaturcaraan, carian dokumen dan dialog. Input yang diterima Teks Hasil Teks ### Tersedia di Ailoot · input & kaedah **Ailoot · OpenAI Chat** `POST /v1/chat/completions` Input `text` Output `text` Konteks 4,096 tokens Output maksimum 1,024 tokens Format output json atau SSE | Medan | Jenis | Keperluan | Nilai & had | Lalai | | --- | --- | --- | --- | --- | | Medan`model` | Jenis`string` | KeperluanDiperlukan | Nilai & had nvidia/nemotron-3.5-lightning-30b-a3b | Lalai— | | Medan`messages` | Jenis`message[]` | KeperluanDiperlukan | Nilai & had Item minimum: 1 | Lalai— | | Medan`messages[].role` | Jenis`string` | KeperluanDiperlukan | Nilai & had Contoh: system, user, assistant, tool, function Syarat: Peranan ini diwakili secara eksplisit oleh jenis keserasian dalam kod sumber; set peranan model yang tepat kekal khusus untuk setiap model. Bergantung pada model | Lalai— | | Medan`messages[].content` | Jenis`string\|content_part[]\|null` | KeperluanPilihan | Nilai & had | Lalai— | | Medan`messages[].tool_calls` | Jenis`tool_call[]` | KeperluanPilihan | Nilai & had | Lalai— | | Medan`messages[].tool_call_id` | Jenis`string` | KeperluanPilihan | Nilai & had Terpakai apabila: role=tool; gunakan ID sebenar yang dipulangkan | Lalai— | | Medan`messages[].name` | Jenis`string` | KeperluanPilihan | Nilai & had | Lalai— | | Medan`messages[].reasoning_content` | Jenis`string` | KeperluanPilihan | Nilai & had Terpakai apabila: Hantar semula penaakulan sebenar yang dipulangkan apabila model ini menggunakannya | Lalai— | | Medan`messages[].reasoning_details` | Jenis`object\|array` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`max_tokens` | Jenis`integer` | KeperluanPilihan | Nilai & had Minimum: 1 Bergantung pada model | Lalai— | | Medan`max_completion_tokens` | Jenis`integer` | KeperluanPilihan | Nilai & had Minimum: 1 Bergantung pada model | Lalai— | | Medan`stream` | Jenis`boolean` | KeperluanPilihan | Nilai & had | Lalaifalse | | Medan`stream_options.include_usage` | Jenis`boolean` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`temperature` | Jenis`number` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`top_p` | Jenis`number` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`stop` | Jenis`string\|string[]` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`tools` | Jenis`function_tool[]` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`tools[].type` | Jenis`string` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`tools[].function.name` | Jenis`string` | KeperluanDiperlukan | Nilai & had Terpakai apabila: alat fungsi dibekalkan | Lalai— | | Medan`tools[].function.description` | Jenis`string` | KeperluanPilihan | Nilai & had | Lalai— | | Medan`tools[].function.parameters` | Jenis`JSON_schema` | KeperluanPilihan | Nilai & had | Lalai— | | Medan`tool_choice` | Jenis`string\|object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`parallel_tool_calls` | Jenis`boolean` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`response_format` | Jenis`object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`reasoning_effort` | Jenis`string` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`reasoning` | Jenis`object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`service_tier` | Jenis`string` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`instructions` | Jenis`string` | KeperluanPilihan | Nilai & had Medan keserasian | Lalai— | | Medan`functions` | Jenis`function[]` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`function_call` | Jenis`string\|object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`prompt_cache_options` | Jenis`object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | Gabungan bahan input **Teks** · Teks UTF8 `messages[].content` Parameter kaedah dan kitar hayat | Parameter | Kontrak | | --- | --- | | Parameter`material_types` | Kontraktext | | Parameter`required` | Kontrakmodel, messages | | Parameter`optional_controls` | Kontraknull | | Parameter`content_type` | Kontrakapplication/json | | Parameter`text_file_material` | Kontrakkandungan UTF-8 sebenar dimasukkan sebagai teks; ID fail atau alamat URL (URLs) memerlukan sokongan tepat yang berasingan | Kitar hayat: `exact retained published method` Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan. ### Pelan harga #### Kadar kaedah Ailoot Input USD 0.00008 */ 1K* Output USD 0.0002 */ 1K* Bacaan cache USD 0.00008 */ 1K* Penulisan cache USD 0.00008 */ 1K* Penulisan cache, 5 minit USD 0.00008 */ 1K tokens* 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 →](https://ailoot.ai/docs/ms/billing/reserve/) ### Contoh permintaan #### Ailoot · OpenAI Chat Contoh teksPython SDKNode.js SDKPenstriman Python ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" : "${AILOOT_TEXT_FILE:?Set your actual UTF-8 source-material file}" export AILOOT_TEXT_FILE python3 - <<'PY_REQUEST' import json,os from pathlib import Path p=json.loads('{"model": "nvidia/nemotron-3.5-lightning-30b-a3b", "messages": []}') f=Path(os.environ["AILOOT_TEXT_FILE"]) if not f.is_file():raise SystemExit("Choose your actual UTF-8 source material") text=f.read_text(encoding="utf-8") if not text.strip():raise SystemExit("Source material must contain text") p["messages"]=[{"role":"user","content":"Summarize this material and quote supporting source passages.\n\n"+text}] Path("request.json").write_text(json.dumps(p,ensure_ascii=False)) PY_REQUEST curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \ -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \ --data-binary @request.json -D response-headers.txt -o result.json python3 - <<'PY_RESULT' import json from pathlib import Path print(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2)) PY_RESULT ``` ```python from openai import OpenAI import os if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request") client = OpenAI(base_url="https://api.ailoot.ai/v1", api_key=os.environ["AILOOT_API_KEY"], max_retries=0) resp = client.chat.completions.create( model="nvidia/nemotron-3.5-lightning-30b-a3b", messages=[{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}], ) import base64, json from pathlib import Path def save_result(data, stem="result"): # Keep every field, including usage, IDs, reasoning and tool requests. Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8") print(json.dumps(data, ensure_ascii=False, indent=2)) def save_blob(encoded, suffix, label): raw = base64.b64decode(encoded, altchars=b"-_", validate=True) ext = "png" if raw.startswith(b"\x89PNG\r\n\x1a\n") else "jpg" if raw.startswith(b"\xff\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix file = Path(stem + "-" + label + "." + ext) file.write_bytes(raw) print("Saved", file) for index, block in enumerate(data.get("content") or []): print("Content", index, json.dumps(block, ensure_ascii=False)) for choice in data.get("choices") or []: print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False)) for index, item in enumerate(data.get("output") or []): print("Output", index, json.dumps(item, ensure_ascii=False)) if item.get("type") == "image_generation_call" and item.get("result"): save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index)) for ci, candidate in enumerate(data.get("candidates") or []): for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []): print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False)) blob = part.get("inlineData") or part.get("inline_data") if blob and blob.get("data"): mime = blob.get("mimeType") or blob.get("mime_type") suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin") save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi)) save_result(resp.to_dict(mode="json")) ``` ```js import OpenAI from "openai"; if (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request"); const client = new OpenAI({ baseURL: "https://api.ailoot.ai/v1", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 }); const resp = await client.chat.completions.create({ model: "nvidia/nemotron-3.5-lightning-30b-a3b", messages: [{ role: "user", content: "Explain the difference between an observation and a hypothesis in three sentences." }], }); import { writeFileSync } from "node:fs"; function saveResult(data, stem = "result") { writeFileSync(stem + ".json", JSON.stringify(data, null, 2)); console.log(JSON.stringify(data, null, 2)); const saveBlob = (encoded, suffix, label) => { const raw = Buffer.from(encoded, "base64"); const ext = raw.subarray(0,8).equals(Buffer.from([137,80,78,71,13,10,26,10])) ? "png" : raw[0]===255 && raw[1]===216 ? "jpg" : raw.subarray(0,4).toString()==="RIFF" && raw.subarray(8,12).toString()==="WEBP" ? "webp" : suffix; const file = `${stem}-${label}.${ext}`; writeFileSync(file, raw); console.log("Saved", file); }; for (const [i, block] of (data.content || []).entries()) console.log("Content", i, JSON.stringify(block)); for (const choice of data.choices || []) console.log("Choice", choice.index, JSON.stringify(choice.message || choice.delta || {})); for (const [i, item] of (data.output || []).entries()) { console.log("Output", i, JSON.stringify(item)); if (item.type === "image_generation_call" && item.result) saveBlob(item.result, item.output_format || "bin", `output-${i}`); } for (const [ci, candidate] of (data.candidates || []).entries()) { for (const [pi, part] of (candidate.content?.parts || []).entries()) { console.log("Candidate/part", ci, pi, JSON.stringify(part)); const blob = part.inlineData || part.inline_data; if (blob?.data) { const mime = blob.mimeType || blob.mime_type; const suffix = ({"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"})[mime] || "bin"; saveBlob(blob.data, suffix, `candidate-${ci}-part-${pi}`); } } } } saveResult(resp); ``` ```python from openai import OpenAI import os from pathlib import Path if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request") client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0) import base64, json from pathlib import Path def save_result(data, stem="result"): # Keep every field, including usage, IDs, reasoning and tool requests. Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8") print(json.dumps(data, ensure_ascii=False, indent=2)) def save_blob(encoded, suffix, label): raw = base64.b64decode(encoded, altchars=b"-_", validate=True) ext = "png" if raw.startswith(b"\x89PNG\r\n\x1a\n") else "jpg" if raw.startswith(b"\xff\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix file = Path(stem + "-" + label + "." + ext) file.write_bytes(raw) print("Saved", file) for index, block in enumerate(data.get("content") or []): print("Content", index, json.dumps(block, ensure_ascii=False)) for choice in data.get("choices") or []: print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False)) for index, item in enumerate(data.get("output") or []): print("Output", index, json.dumps(item, ensure_ascii=False)) if item.get("type") == "image_generation_call" and item.get("result"): save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index)) for ci, candidate in enumerate(data.get("candidates") or []): for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []): print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False)) blob = part.get("inlineData") or part.get("inline_data") if blob and blob.get("data"): mime = blob.get("mimeType") or blob.get("mime_type") suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin") save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi)) with client.chat.completions.stream(model='nvidia/nemotron-3.5-lightning-30b-a3b',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream: with Path("stream-events.jsonl").open("w",encoding="utf-8") as log: for event in stream: payload=event.to_dict(mode="json") log.write(json.dumps(payload,ensure_ascii=False)+"\n"); log.flush() print(json.dumps(payload,ensure_ascii=False)) final=stream.get_final_completion() save_result(final.to_dict(mode="json"),"stream-result") ``` ### Terima dan simpan hasil #### Ailoot · OpenAI Chat json atau SSE ##### Simpan semua hasil yang selesai Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan. Kekalkan setiap pilihan jawapan bersama kandungan, panggilan alat dan penaakulannya. Dalam SSE, gabungkan delta mengikut indeks pilihan jawapan/alat dan kekalkan sebab penamatan akhir serta data penggunaan. ##### Jika respons asal perlu disemak Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan. Medan respons | Medan | Jenis | | --- | --- | | Medan`choices[].index` | Jenisinteger | | Medan`choices[].message.content` | Jenisstring\|null | | Medan`choices[].message.tool_calls` | Jenistool\_call\[\] | | Medan`choices[].message.reasoning_content` | Jenisstring | | Medan`choices[].message.reasoning_details` | Jenisobject\|array | | Medan`choices[].finish_reason` | Jenisstring\|null | | Medan`usage` | Jenisobject | Ralat dan langkah seterusnya | Respons | Syarat | Langkah seterusnya | | --- | --- | --- | | Respons400 · `invalid_request_error` | SyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sah | Langkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu. | ### Alias #### Alias model **Ailoot** [`nvidia/nemotron-3.5-lightning-30b-a3b`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nvidia%2Fnemotron-3.5-lightning-30b-a3b&fmt=chat), [`nemotron-lightning`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nemotron-lightning&fmt=chat), [`nemotron-lightning-latest`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nemotron-lightning-latest&fmt=chat) #### Alias laluan kaedah Gunakan laluan kaedah yang dipaparkan di atas. #### ID pemanggil berkaitan [`nvidia/nemotron-3.5-lightning-30b-a3b`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nvidia%2Fnemotron-3.5-lightning-30b-a3b&fmt=chat), [`nemotron-lightning`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nemotron-lightning&fmt=chat), [`nemotron-lightning-latest`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nemotron-lightning-latest&fmt=chat) **Ailoot · OpenAI Chat** `POST /v1/chat/completions` `nemotron-lightning` [`nemotron-lightning`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nemotron-lightning&fmt=chat) NVIDIA /`nemotron-lightning` ID model - `nvidia/nemotron-3.5-lightning-30b-a3b` - `nemotron-lightning` - `nemotron-lightning-latest` ### Pengenalan Nemotron 3.5 Lightning ialah model teks NVIDIA untuk tugasan ejen panjang, pengaturcaraan, carian dokumen dan dialog. Input yang diterima Teks Hasil Teks ### Tersedia di Ailoot · input & kaedah **Ailoot · OpenAI Chat** `POST /v1/chat/completions` Input `text` Output `text` Konteks 4,096 tokens Output maksimum 1,024 tokens Format output json atau SSE | Medan | Jenis | Keperluan | Nilai & had | Lalai | | --- | --- | --- | --- | --- | | Medan`model` | Jenis`string` | KeperluanDiperlukan | Nilai & had nemotron-lightning | Lalai— | | Medan`messages` | Jenis`message[]` | KeperluanDiperlukan | Nilai & had Item minimum: 1 | Lalai— | | Medan`messages[].role` | Jenis`string` | KeperluanDiperlukan | Nilai & had Contoh: system, user, assistant, tool, function Syarat: Peranan ini diwakili secara eksplisit oleh jenis keserasian dalam kod sumber; set peranan model yang tepat kekal khusus untuk setiap model. Bergantung pada model | Lalai— | | Medan`messages[].content` | Jenis`string\|content_part[]\|null` | KeperluanPilihan | Nilai & had | Lalai— | | Medan`messages[].tool_calls` | Jenis`tool_call[]` | KeperluanPilihan | Nilai & had | Lalai— | | Medan`messages[].tool_call_id` | Jenis`string` | KeperluanPilihan | Nilai & had Terpakai apabila: role=tool; gunakan ID sebenar yang dipulangkan | Lalai— | | Medan`messages[].name` | Jenis`string` | KeperluanPilihan | Nilai & had | Lalai— | | Medan`messages[].reasoning_content` | Jenis`string` | KeperluanPilihan | Nilai & had Terpakai apabila: Hantar semula penaakulan sebenar yang dipulangkan apabila model ini menggunakannya | Lalai— | | Medan`messages[].reasoning_details` | Jenis`object\|array` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`max_tokens` | Jenis`integer` | KeperluanPilihan | Nilai & had Minimum: 1 Bergantung pada model | Lalai— | | Medan`max_completion_tokens` | Jenis`integer` | KeperluanPilihan | Nilai & had Minimum: 1 Bergantung pada model | Lalai— | | Medan`stream` | Jenis`boolean` | KeperluanPilihan | Nilai & had | Lalaifalse | | Medan`stream_options.include_usage` | Jenis`boolean` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`temperature` | Jenis`number` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`top_p` | Jenis`number` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`stop` | Jenis`string\|string[]` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`tools` | Jenis`function_tool[]` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`tools[].type` | Jenis`string` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`tools[].function.name` | Jenis`string` | KeperluanDiperlukan | Nilai & had Terpakai apabila: alat fungsi dibekalkan | Lalai— | | Medan`tools[].function.description` | Jenis`string` | KeperluanPilihan | Nilai & had | Lalai— | | Medan`tools[].function.parameters` | Jenis`JSON_schema` | KeperluanPilihan | Nilai & had | Lalai— | | Medan`tool_choice` | Jenis`string\|object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`parallel_tool_calls` | Jenis`boolean` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`response_format` | Jenis`object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`reasoning_effort` | Jenis`string` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`reasoning` | Jenis`object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`service_tier` | Jenis`string` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`instructions` | Jenis`string` | KeperluanPilihan | Nilai & had Medan keserasian | Lalai— | | Medan`functions` | Jenis`function[]` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`function_call` | Jenis`string\|object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`prompt_cache_options` | Jenis`object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | Gabungan bahan input **Teks** · Teks UTF8 `messages[].content` Parameter kaedah dan kitar hayat | Parameter | Kontrak | | --- | --- | | Parameter`material_types` | Kontraktext | | Parameter`required` | Kontrakmodel, messages | | Parameter`optional_controls` | Kontraknull | | Parameter`content_type` | Kontrakapplication/json | | Parameter`text_file_material` | Kontrakkandungan UTF-8 sebenar dimasukkan sebagai teks; ID fail atau alamat URL (URLs) memerlukan sokongan tepat yang berasingan | Kitar hayat: `exact retained published method` Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan. ### Pelan harga #### Kadar kaedah Ailoot Input USD 0.00008 */ 1K* Output USD 0.0002 */ 1K* Bacaan cache USD 0.00008 */ 1K* Penulisan cache USD 0.00008 */ 1K* Penulisan cache, 5 minit USD 0.00008 */ 1K tokens* 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 →](https://ailoot.ai/docs/ms/billing/reserve/) ### Contoh permintaan #### Ailoot · OpenAI Chat Contoh teksPython SDKNode.js SDKPenstriman Python ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" : "${AILOOT_TEXT_FILE:?Set your actual UTF-8 source-material file}" export AILOOT_TEXT_FILE python3 - <<'PY_REQUEST' import json,os from pathlib import Path p=json.loads('{"model": "nemotron-lightning", "messages": []}') f=Path(os.environ["AILOOT_TEXT_FILE"]) if not f.is_file():raise SystemExit("Choose your actual UTF-8 source material") text=f.read_text(encoding="utf-8") if not text.strip():raise SystemExit("Source material must contain text") p["messages"]=[{"role":"user","content":"Summarize this material and quote supporting source passages.\n\n"+text}] Path("request.json").write_text(json.dumps(p,ensure_ascii=False)) PY_REQUEST curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \ -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \ --data-binary @request.json -D response-headers.txt -o result.json python3 - <<'PY_RESULT' import json from pathlib import Path print(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2)) PY_RESULT ``` ```python from openai import OpenAI import os if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request") client = OpenAI(base_url="https://api.ailoot.ai/v1", api_key=os.environ["AILOOT_API_KEY"], max_retries=0) resp = client.chat.completions.create( model="nemotron-lightning", messages=[{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}], ) import base64, json from pathlib import Path def save_result(data, stem="result"): # Keep every field, including usage, IDs, reasoning and tool requests. Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8") print(json.dumps(data, ensure_ascii=False, indent=2)) def save_blob(encoded, suffix, label): raw = base64.b64decode(encoded, altchars=b"-_", validate=True) ext = "png" if raw.startswith(b"\x89PNG\r\n\x1a\n") else "jpg" if raw.startswith(b"\xff\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix file = Path(stem + "-" + label + "." + ext) file.write_bytes(raw) print("Saved", file) for index, block in enumerate(data.get("content") or []): print("Content", index, json.dumps(block, ensure_ascii=False)) for choice in data.get("choices") or []: print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False)) for index, item in enumerate(data.get("output") or []): print("Output", index, json.dumps(item, ensure_ascii=False)) if item.get("type") == "image_generation_call" and item.get("result"): save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index)) for ci, candidate in enumerate(data.get("candidates") or []): for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []): print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False)) blob = part.get("inlineData") or part.get("inline_data") if blob and blob.get("data"): mime = blob.get("mimeType") or blob.get("mime_type") suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin") save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi)) save_result(resp.to_dict(mode="json")) ``` ```js import OpenAI from "openai"; if (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request"); const client = new OpenAI({ baseURL: "https://api.ailoot.ai/v1", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 }); const resp = await client.chat.completions.create({ model: "nemotron-lightning", messages: [{ role: "user", content: "Explain the difference between an observation and a hypothesis in three sentences." }], }); import { writeFileSync } from "node:fs"; function saveResult(data, stem = "result") { writeFileSync(stem + ".json", JSON.stringify(data, null, 2)); console.log(JSON.stringify(data, null, 2)); const saveBlob = (encoded, suffix, label) => { const raw = Buffer.from(encoded, "base64"); const ext = raw.subarray(0,8).equals(Buffer.from([137,80,78,71,13,10,26,10])) ? "png" : raw[0]===255 && raw[1]===216 ? "jpg" : raw.subarray(0,4).toString()==="RIFF" && raw.subarray(8,12).toString()==="WEBP" ? "webp" : suffix; const file = `${stem}-${label}.${ext}`; writeFileSync(file, raw); console.log("Saved", file); }; for (const [i, block] of (data.content || []).entries()) console.log("Content", i, JSON.stringify(block)); for (const choice of data.choices || []) console.log("Choice", choice.index, JSON.stringify(choice.message || choice.delta || {})); for (const [i, item] of (data.output || []).entries()) { console.log("Output", i, JSON.stringify(item)); if (item.type === "image_generation_call" && item.result) saveBlob(item.result, item.output_format || "bin", `output-${i}`); } for (const [ci, candidate] of (data.candidates || []).entries()) { for (const [pi, part] of (candidate.content?.parts || []).entries()) { console.log("Candidate/part", ci, pi, JSON.stringify(part)); const blob = part.inlineData || part.inline_data; if (blob?.data) { const mime = blob.mimeType || blob.mime_type; const suffix = ({"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"})[mime] || "bin"; saveBlob(blob.data, suffix, `candidate-${ci}-part-${pi}`); } } } } saveResult(resp); ``` ```python from openai import OpenAI import os from pathlib import Path if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request") client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0) import base64, json from pathlib import Path def save_result(data, stem="result"): # Keep every field, including usage, IDs, reasoning and tool requests. Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8") print(json.dumps(data, ensure_ascii=False, indent=2)) def save_blob(encoded, suffix, label): raw = base64.b64decode(encoded, altchars=b"-_", validate=True) ext = "png" if raw.startswith(b"\x89PNG\r\n\x1a\n") else "jpg" if raw.startswith(b"\xff\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix file = Path(stem + "-" + label + "." + ext) file.write_bytes(raw) print("Saved", file) for index, block in enumerate(data.get("content") or []): print("Content", index, json.dumps(block, ensure_ascii=False)) for choice in data.get("choices") or []: print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False)) for index, item in enumerate(data.get("output") or []): print("Output", index, json.dumps(item, ensure_ascii=False)) if item.get("type") == "image_generation_call" and item.get("result"): save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index)) for ci, candidate in enumerate(data.get("candidates") or []): for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []): print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False)) blob = part.get("inlineData") or part.get("inline_data") if blob and blob.get("data"): mime = blob.get("mimeType") or blob.get("mime_type") suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin") save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi)) with client.chat.completions.stream(model='nemotron-lightning',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream: with Path("stream-events.jsonl").open("w",encoding="utf-8") as log: for event in stream: payload=event.to_dict(mode="json") log.write(json.dumps(payload,ensure_ascii=False)+"\n"); log.flush() print(json.dumps(payload,ensure_ascii=False)) final=stream.get_final_completion() save_result(final.to_dict(mode="json"),"stream-result") ``` ### Terima dan simpan hasil #### Ailoot · OpenAI Chat json atau SSE ##### Simpan semua hasil yang selesai Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan. Kekalkan setiap pilihan jawapan bersama kandungan, panggilan alat dan penaakulannya. Dalam SSE, gabungkan delta mengikut indeks pilihan jawapan/alat dan kekalkan sebab penamatan akhir serta data penggunaan. ##### Jika respons asal perlu disemak Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan. Medan respons | Medan | Jenis | | --- | --- | | Medan`choices[].index` | Jenisinteger | | Medan`choices[].message.content` | Jenisstring\|null | | Medan`choices[].message.tool_calls` | Jenistool\_call\[\] | | Medan`choices[].message.reasoning_content` | Jenisstring | | Medan`choices[].message.reasoning_details` | Jenisobject\|array | | Medan`choices[].finish_reason` | Jenisstring\|null | | Medan`usage` | Jenisobject | Ralat dan langkah seterusnya | Respons | Syarat | Langkah seterusnya | | --- | --- | --- | | Respons400 · `invalid_request_error` | SyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sah | Langkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu. | ### Alias #### Alias model **Ailoot** [`nvidia/nemotron-3.5-lightning-30b-a3b`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nvidia%2Fnemotron-3.5-lightning-30b-a3b&fmt=chat), [`nemotron-lightning`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nemotron-lightning&fmt=chat), [`nemotron-lightning-latest`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nemotron-lightning-latest&fmt=chat) #### Alias laluan kaedah Gunakan laluan kaedah yang dipaparkan di atas. #### ID pemanggil berkaitan [`nvidia/nemotron-3.5-lightning-30b-a3b`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nvidia%2Fnemotron-3.5-lightning-30b-a3b&fmt=chat), [`nemotron-lightning`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nemotron-lightning&fmt=chat), [`nemotron-lightning-latest`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nemotron-lightning-latest&fmt=chat) **Ailoot · OpenAI Chat** `POST /v1/chat/completions` `nemotron-lightning-latest` [`nemotron-lightning-latest`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nemotron-lightning-latest&fmt=chat) NVIDIA /`nemotron-lightning-latest` ID model - `nvidia/nemotron-3.5-lightning-30b-a3b` - `nemotron-lightning` - `nemotron-lightning-latest` ### Pengenalan Nemotron 3.5 Lightning ialah model teks NVIDIA untuk tugasan ejen panjang, pengaturcaraan, carian dokumen dan dialog. Input yang diterima Teks Hasil Teks ### Tersedia di Ailoot · input & kaedah **Ailoot · OpenAI Chat** `POST /v1/chat/completions` Input `text` Output `text` Konteks 4,096 tokens Output maksimum 1,024 tokens Format output json atau SSE | Medan | Jenis | Keperluan | Nilai & had | Lalai | | --- | --- | --- | --- | --- | | Medan`model` | Jenis`string` | KeperluanDiperlukan | Nilai & had nemotron-lightning-latest | Lalai— | | Medan`messages` | Jenis`message[]` | KeperluanDiperlukan | Nilai & had Item minimum: 1 | Lalai— | | Medan`messages[].role` | Jenis`string` | KeperluanDiperlukan | Nilai & had Contoh: system, user, assistant, tool, function Syarat: Peranan ini diwakili secara eksplisit oleh jenis keserasian dalam kod sumber; set peranan model yang tepat kekal khusus untuk setiap model. Bergantung pada model | Lalai— | | Medan`messages[].content` | Jenis`string\|content_part[]\|null` | KeperluanPilihan | Nilai & had | Lalai— | | Medan`messages[].tool_calls` | Jenis`tool_call[]` | KeperluanPilihan | Nilai & had | Lalai— | | Medan`messages[].tool_call_id` | Jenis`string` | KeperluanPilihan | Nilai & had Terpakai apabila: role=tool; gunakan ID sebenar yang dipulangkan | Lalai— | | Medan`messages[].name` | Jenis`string` | KeperluanPilihan | Nilai & had | Lalai— | | Medan`messages[].reasoning_content` | Jenis`string` | KeperluanPilihan | Nilai & had Terpakai apabila: Hantar semula penaakulan sebenar yang dipulangkan apabila model ini menggunakannya | Lalai— | | Medan`messages[].reasoning_details` | Jenis`object\|array` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`max_tokens` | Jenis`integer` | KeperluanPilihan | Nilai & had Minimum: 1 Bergantung pada model | Lalai— | | Medan`max_completion_tokens` | Jenis`integer` | KeperluanPilihan | Nilai & had Minimum: 1 Bergantung pada model | Lalai— | | Medan`stream` | Jenis`boolean` | KeperluanPilihan | Nilai & had | Lalaifalse | | Medan`stream_options.include_usage` | Jenis`boolean` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`temperature` | Jenis`number` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`top_p` | Jenis`number` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`stop` | Jenis`string\|string[]` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`tools` | Jenis`function_tool[]` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`tools[].type` | Jenis`string` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`tools[].function.name` | Jenis`string` | KeperluanDiperlukan | Nilai & had Terpakai apabila: alat fungsi dibekalkan | Lalai— | | Medan`tools[].function.description` | Jenis`string` | KeperluanPilihan | Nilai & had | Lalai— | | Medan`tools[].function.parameters` | Jenis`JSON_schema` | KeperluanPilihan | Nilai & had | Lalai— | | Medan`tool_choice` | Jenis`string\|object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`parallel_tool_calls` | Jenis`boolean` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`response_format` | Jenis`object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`reasoning_effort` | Jenis`string` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`reasoning` | Jenis`object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`service_tier` | Jenis`string` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`instructions` | Jenis`string` | KeperluanPilihan | Nilai & had Medan keserasian | Lalai— | | Medan`functions` | Jenis`function[]` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`function_call` | Jenis`string\|object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | | Medan`prompt_cache_options` | Jenis`object` | KeperluanPilihan | Nilai & had Bergantung pada model | Lalai— | Gabungan bahan input **Teks** · Teks UTF8 `messages[].content` Parameter kaedah dan kitar hayat | Parameter | Kontrak | | --- | --- | | Parameter`material_types` | Kontraktext | | Parameter`required` | Kontrakmodel, messages | | Parameter`optional_controls` | Kontraknull | | Parameter`content_type` | Kontrakapplication/json | | Parameter`text_file_material` | Kontrakkandungan UTF-8 sebenar dimasukkan sebagai teks; ID fail atau alamat URL (URLs) memerlukan sokongan tepat yang berasingan | Kitar hayat: `exact retained published method` Struktur kaedah dan ID adalah tepat; keupayaan pembekal berasingan daripada penerimaan permintaan. ### Pelan harga #### Kadar kaedah Ailoot Input USD 0.00008 */ 1K* Output USD 0.0002 */ 1K* Bacaan cache USD 0.00008 */ 1K* Penulisan cache USD 0.00008 */ 1K* Penulisan cache, 5 minit USD 0.00008 */ 1K tokens* 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 →](https://ailoot.ai/docs/ms/billing/reserve/) ### Contoh permintaan #### Ailoot · OpenAI Chat Contoh teksPython SDKNode.js SDKPenstriman Python ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" : "${AILOOT_TEXT_FILE:?Set your actual UTF-8 source-material file}" export AILOOT_TEXT_FILE python3 - <<'PY_REQUEST' import json,os from pathlib import Path p=json.loads('{"model": "nemotron-lightning-latest", "messages": []}') f=Path(os.environ["AILOOT_TEXT_FILE"]) if not f.is_file():raise SystemExit("Choose your actual UTF-8 source material") text=f.read_text(encoding="utf-8") if not text.strip():raise SystemExit("Source material must contain text") p["messages"]=[{"role":"user","content":"Summarize this material and quote supporting source passages.\n\n"+text}] Path("request.json").write_text(json.dumps(p,ensure_ascii=False)) PY_REQUEST curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \ -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \ --data-binary @request.json -D response-headers.txt -o result.json python3 - <<'PY_RESULT' import json from pathlib import Path print(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2)) PY_RESULT ``` ```python from openai import OpenAI import os if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request") client = OpenAI(base_url="https://api.ailoot.ai/v1", api_key=os.environ["AILOOT_API_KEY"], max_retries=0) resp = client.chat.completions.create( model="nemotron-lightning-latest", messages=[{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}], ) import base64, json from pathlib import Path def save_result(data, stem="result"): # Keep every field, including usage, IDs, reasoning and tool requests. Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8") print(json.dumps(data, ensure_ascii=False, indent=2)) def save_blob(encoded, suffix, label): raw = base64.b64decode(encoded, altchars=b"-_", validate=True) ext = "png" if raw.startswith(b"\x89PNG\r\n\x1a\n") else "jpg" if raw.startswith(b"\xff\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix file = Path(stem + "-" + label + "." + ext) file.write_bytes(raw) print("Saved", file) for index, block in enumerate(data.get("content") or []): print("Content", index, json.dumps(block, ensure_ascii=False)) for choice in data.get("choices") or []: print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False)) for index, item in enumerate(data.get("output") or []): print("Output", index, json.dumps(item, ensure_ascii=False)) if item.get("type") == "image_generation_call" and item.get("result"): save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index)) for ci, candidate in enumerate(data.get("candidates") or []): for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []): print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False)) blob = part.get("inlineData") or part.get("inline_data") if blob and blob.get("data"): mime = blob.get("mimeType") or blob.get("mime_type") suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin") save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi)) save_result(resp.to_dict(mode="json")) ``` ```js import OpenAI from "openai"; if (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request"); const client = new OpenAI({ baseURL: "https://api.ailoot.ai/v1", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 }); const resp = await client.chat.completions.create({ model: "nemotron-lightning-latest", messages: [{ role: "user", content: "Explain the difference between an observation and a hypothesis in three sentences." }], }); import { writeFileSync } from "node:fs"; function saveResult(data, stem = "result") { writeFileSync(stem + ".json", JSON.stringify(data, null, 2)); console.log(JSON.stringify(data, null, 2)); const saveBlob = (encoded, suffix, label) => { const raw = Buffer.from(encoded, "base64"); const ext = raw.subarray(0,8).equals(Buffer.from([137,80,78,71,13,10,26,10])) ? "png" : raw[0]===255 && raw[1]===216 ? "jpg" : raw.subarray(0,4).toString()==="RIFF" && raw.subarray(8,12).toString()==="WEBP" ? "webp" : suffix; const file = `${stem}-${label}.${ext}`; writeFileSync(file, raw); console.log("Saved", file); }; for (const [i, block] of (data.content || []).entries()) console.log("Content", i, JSON.stringify(block)); for (const choice of data.choices || []) console.log("Choice", choice.index, JSON.stringify(choice.message || choice.delta || {})); for (const [i, item] of (data.output || []).entries()) { console.log("Output", i, JSON.stringify(item)); if (item.type === "image_generation_call" && item.result) saveBlob(item.result, item.output_format || "bin", `output-${i}`); } for (const [ci, candidate] of (data.candidates || []).entries()) { for (const [pi, part] of (candidate.content?.parts || []).entries()) { console.log("Candidate/part", ci, pi, JSON.stringify(part)); const blob = part.inlineData || part.inline_data; if (blob?.data) { const mime = blob.mimeType || blob.mime_type; const suffix = ({"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"})[mime] || "bin"; saveBlob(blob.data, suffix, `candidate-${ci}-part-${pi}`); } } } } saveResult(resp); ``` ```python from openai import OpenAI import os from pathlib import Path if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request") client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0) import base64, json from pathlib import Path def save_result(data, stem="result"): # Keep every field, including usage, IDs, reasoning and tool requests. Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8") print(json.dumps(data, ensure_ascii=False, indent=2)) def save_blob(encoded, suffix, label): raw = base64.b64decode(encoded, altchars=b"-_", validate=True) ext = "png" if raw.startswith(b"\x89PNG\r\n\x1a\n") else "jpg" if raw.startswith(b"\xff\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix file = Path(stem + "-" + label + "." + ext) file.write_bytes(raw) print("Saved", file) for index, block in enumerate(data.get("content") or []): print("Content", index, json.dumps(block, ensure_ascii=False)) for choice in data.get("choices") or []: print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False)) for index, item in enumerate(data.get("output") or []): print("Output", index, json.dumps(item, ensure_ascii=False)) if item.get("type") == "image_generation_call" and item.get("result"): save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index)) for ci, candidate in enumerate(data.get("candidates") or []): for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []): print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False)) blob = part.get("inlineData") or part.get("inline_data") if blob and blob.get("data"): mime = blob.get("mimeType") or blob.get("mime_type") suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin") save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi)) with client.chat.completions.stream(model='nemotron-lightning-latest',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream: with Path("stream-events.jsonl").open("w",encoding="utf-8") as log: for event in stream: payload=event.to_dict(mode="json") log.write(json.dumps(payload,ensure_ascii=False)+"\n"); log.flush() print(json.dumps(payload,ensure_ascii=False)) final=stream.get_final_completion() save_result(final.to_dict(mode="json"),"stream-result") ``` ### Terima dan simpan hasil #### Ailoot · OpenAI Chat json atau SSE ##### Simpan semua hasil yang selesai Simpan setiap respons lengkap, fail dan bahagian kandungan yang dikembalikan. Kekalkan setiap pilihan jawapan bersama kandungan, panggilan alat dan penaakulannya. Dalam SSE, gabungkan delta mengikut indeks pilihan jawapan/alat dan kekalkan sebab penamatan akhir serta data penggunaan. ##### Jika respons asal perlu disemak Kekalkan ID permintaan/respons dan pengepala. Tiada URL umum bagi pengambilan tugas teks dibekalkan. Medan respons | Medan | Jenis | | --- | --- | | Medan`choices[].index` | Jenisinteger | | Medan`choices[].message.content` | Jenisstring\|null | | Medan`choices[].message.tool_calls` | Jenistool\_call\[\] | | Medan`choices[].message.reasoning_content` | Jenisstring | | Medan`choices[].message.reasoning_details` | Jenisobject\|array | | Medan`choices[].finish_reason` | Jenisstring\|null | | Medan`usage` | Jenisobject | Ralat dan langkah seterusnya | Respons | Syarat | Langkah seterusnya | | --- | --- | --- | | Respons400 · `invalid_request_error` | SyaratMedan wajib, perwakilan bahan atau nilai parameter tidak sah | Langkah seterusnyaBetulkan medan yang ditunjukkan sebelum permintaan penciptaan baharu. | ### Alias #### Alias model **Ailoot** [`nvidia/nemotron-3.5-lightning-30b-a3b`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nvidia%2Fnemotron-3.5-lightning-30b-a3b&fmt=chat), [`nemotron-lightning`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nemotron-lightning&fmt=chat), [`nemotron-lightning-latest`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nemotron-lightning-latest&fmt=chat) #### Alias laluan kaedah Gunakan laluan kaedah yang dipaparkan di atas. #### ID pemanggil berkaitan [`nvidia/nemotron-3.5-lightning-30b-a3b`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nvidia%2Fnemotron-3.5-lightning-30b-a3b&fmt=chat), [`nemotron-lightning`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nemotron-lightning&fmt=chat), [`nemotron-lightning-latest`](https://ailoot.ai/models/ms/#m=nvidia%2Fnemotron-3.5-lightning-30b-a3b&route=Core&caller=nemotron-lightning-latest&fmt=chat) FAQ · nvidia/nemotron-3.5-lightning-30b-a3b Apakah input dan hasil yang tersedia? `POST /v1/messages` · `nvidia/nemotron-3.5-lightning-30b-a3b`: `text` → `text` `POST /v1/chat/completions` · `nvidia/nemotron-3.5-lightning-30b-a3b`: `text` → `text` `POST /v1/chat/completions` · `nemotron-lightning`: `text` → `text` `POST /v1/chat/completions` · `nemotron-lightning-latest`: `text` → `text` Apakah medan yang digunakan oleh permintaan? `POST /v1/messages` · `nvidia/nemotron-3.5-lightning-30b-a3b`: `model`, `messages`, `system`, `max_tokens`, `stream`, `metadata` `POST /v1/chat/completions` · `nvidia/nemotron-3.5-lightning-30b-a3b`: `model`, `messages`, `stream` `POST /v1/chat/completions` · `nemotron-lightning`: `model`, `messages`, `stream` `POST /v1/chat/completions` · `nemotron-lightning-latest`: `model`, `messages`, `stream` Apakah had yang terpakai? `POST /v1/messages` · `nvidia/nemotron-3.5-lightning-30b-a3b`: `ctx`: `262144`; `max_out`: `32768`; `messages.min_items`: `1`; `max_tokens.minimum`: `1` `POST /v1/chat/completions` · `nvidia/nemotron-3.5-lightning-30b-a3b`: `ctx`: `4096`; `max_out`: `1024`; `messages.min_items`: `1` `POST /v1/chat/completions` · `nemotron-lightning`: `ctx`: `4096`; `max_out`: `1024`; `messages.min_items`: `1` `POST /v1/chat/completions` · `nemotron-lightning-latest`: `ctx`: `4096`; `max_out`: `1024`; `messages.min_items`: `1` Sambungkan Ailoot kepada Claude Code, OpenCode, Hermes atau aplikasi anda. ## Syarikat dan portfolio projeknya Syarikat dan portfolio projeknya: jutaan pelanggan; jualan bernilai berbilion setiap bulan. ## Beli token dengan diskaun. Diskaun langganan untuk penggunaan berkala. Kod promosi — sehingga 50% bagi pembelian token. Langganan Pilih pelan berdiskaun untuk permintaan berkala anda. Kod promosi sehingga 50% Masukkan kod semasa pembelian. Harga dikira semula mengikut promosi semasa. Perbelanjaan dalam sejarah Model, token dan jumlah disimpan bagi setiap permintaan. [Pilih langganan](https://ailoot.ai/docs/ms/billing/subscriptions/) · [Lihat harga model](https://ailoot.ai/models/ms/) ## Sambungkan API kepada produk yang anda jual. SaaS, aplikasi dan bot — di bawah tawaran rasmi serta perjanjian lesen Ailoot. Untuk jualan semula, mohon lesen penjual semula. Aplikasi komersial Tambahkan penjanaan teks, imej, video dan audio pada ciri berbayar perkhidmatan anda. Lesen penjual semula Hantar keterangan projek dan susunan akses. Minta terma penggunaan serta jualan semula secara bertulis. Dokumen pada laman Tawaran, perjanjian lesen dan dasar pemprosesan data tersedia sebelum sambungan. [Mohon lesen penjual semula](mailto:support@ailoot.ai) · [Tawaran dan perjanjian lesen](https://ailoot.ai/legal/offer?lang=ms) ## Ailoot · Syarikat dan portfolio projeknya Ailoot berteraskan pengalaman operasi yang mantap, pangkalan pelanggan yang besar, perkhidmatan yang stabil dan pemantauan KPI berterusan. Syarikat dan portfolio projeknya: jutaan pelanggan; jualan bernilai berbilion setiap bulan. Ailoot menggunakan pengalaman operasi ini untuk akses API kepada ciri AI dalam produk pelanggan, ejen, aplikasi dan automasi.