Hy-MT2-30B-A3B is Tencent Hy’s mixture-of-experts edition of the Hy-MT2 translation family. The producer positions the family for complex written translation tasks and multilingual instructions, including requirements for terminology, style and structured data. The 30B-A3B edition stays distinct from the dense 1.8B and 7B models. The maker’s language table includes Chinese, Traditional Chinese, English, Japanese, Korean, Russian, Arabic, French and Spanish, as well as Cantonese, Tibetan, Kazakh, Mongolian and Uyghur.
Its published configuration has 48 layers, 32 query attention heads and four key/value heads. Each expert layer uses 128 experts with eight selected per token, and the maximum position count is 262,144. These are properties of this exact configuration. The larger family label does not identify one of the separately published quantized artifacts.
Begin a translation task by naming the target language in full and defining the required form of the answer. The maker’s basic template requests only the translated result. If the source is part of a larger document, add the background needed to interpret it, while keeping that context separate from the text to translate.
For terminology-sensitive work, provide a glossary of source terms and preferred translations. For style, state the audience and tone explicitly. A personalized translation can list several requirements, such as the treatment of names or the desired voice, before supplying the source passage. These producer prompting patterns make the translation constraints part of the assignment.
For structured material, lock the original format, hierarchy and indentation. Translate only the visible user-facing text and preserve keys, code labels, properties and variable placeholders. If separators divide a batch of passages, retain their count and position. A useful output remains usable by the same application that consumes the original structure.
This MoE edition’s recommended sampling differs from the dense models: temperature 0.7, top-p 1.0, no top-k filtering and repetition penalty 1.0. The producer specifies no default system prompt. Its example generation allocation of 4096 tokens is a request example, not a claim that the model’s maximum answer length is fixed at that value.
Review the result against both parts of the task: linguistic meaning and structural preservation. For a document workflow, confirm that the glossary is consistent across sections. For an application-language file, compare identifiers and placeholders with the source before passing the translated values onward. These checks assess the output rather than assuming that a requested preservation rule was satisfied.
", "aboutText": "Hy-MT2-30B-A3B is Tencent Hy’s mixture-of-experts edition of the Hy-MT2 translation family. The producer positions the family for complex written translation tasks and multilingual instructions, including requirements for terminology, style and structured data. The 30B-A3B edition stays distinct from the dense 1.8B and 7B models. The maker’s language table includes Chinese, Traditional Chinese, English, Japanese, Korean, Russian, Arabic, French and Spanish, as well as Cantonese, Tibetan, Kazakh, Mongolian and Uyghur.\n\nIts published configuration has 48 layers, 32 query attention heads and four key/value heads. Each expert layer uses 128 experts with eight selected per token, and the maximum position count is 262,144. These are properties of this exact configuration. The larger family label does not identify one of the separately published quantized artifacts.\n\nBegin a translation task by naming the target language in full and defining the required form of the answer. The maker’s basic template requests only the translated result. If the source is part of a larger document, add the background needed to interpret it, while keeping that context separate from the text to translate.\n\nFor terminology-sensitive work, provide a glossary of source terms and preferred translations. For style, state the audience and tone explicitly. A personalized translation can list several requirements, such as the treatment of names or the desired voice, before supplying the source passage. These producer prompting patterns make the translation constraints part of the assignment.\n\nFor structured material, lock the original format, hierarchy and indentation. Translate only the visible user-facing text and preserve keys, code labels, properties and variable placeholders. If separators divide a batch of passages, retain their count and position. A useful output remains usable by the same application that consumes the original structure.\n\nThis MoE edition’s recommended sampling differs from the dense models: temperature 0.7, top-p 1.0, no top-k filtering and repetition penalty 1.0. The producer specifies no default system prompt. Its example generation allocation of 4096 tokens is a request example, not a claim that the model’s maximum answer length is fixed at that value.\n\nReview the result against both parts of the task: linguistic meaning and structural preservation. For a document workflow, confirm that the glossary is consistent across sections. For an application-language file, compare identifiers and placeholders with the source before passing the translated values onward. These checks assess the output rather than assuming that a requested preservation rule was satisfied.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Translate an interface file safely\n\nTranslate only the user-visible YAML values into Russian. Preserve keys, indentation and the {count} placeholder. Use the glossary Retry = Повторить. Return only translated YAML.\nbuttons:\n retry: "Retry"\n cancel: "Cancel"\nstatus:\n pending: "{count} files pending"YAML with the same mapping hierarchy and keys, translated visible values and the unchanged {count} placeholder.Parse the YAML and compare the key tree and placeholder multiset with the source; check the Retry glossary term.tencent/hy-mt2-30b-a3bTencent’s Hy-MT2-30B-A3B is the MoE edition of its fast-thinking translation family. It handles instruction-guided translation, terminology and structured data, with a 262,144-position configuration and sampling recommendations distinct from the dense editions.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Hy-MT2-30B-A3B is Tencent Hy’s mixture-of-experts edition of the Hy-MT2 translation family. The producer positions the family for complex written translation tasks and multilingual instructions, including requirements for terminology, style and structured data. The 30B-A3B edition stays distinct from the dense 1.8B and 7B models. The maker’s language table includes Chinese, Traditional Chinese, English, Japanese, Korean, Russian, Arabic, French and Spanish, as well as Cantonese, Tibetan, Kazakh, Mongolian and Uyghur.
Its published configuration has 48 layers, 32 query attention heads and four key/value heads. Each expert layer uses 128 experts with eight selected per token, and the maximum position count is 262,144. These are properties of this exact configuration. The larger family label does not identify one of the separately published quantized artifacts.
Begin a translation task by naming the target language in full and defining the required form of the answer. The maker’s basic template requests only the translated result. If the source is part of a larger document, add the background needed to interpret it, while keeping that context separate from the text to translate.
For terminology-sensitive work, provide a glossary of source terms and preferred translations. For style, state the audience and tone explicitly. A personalized translation can list several requirements, such as the treatment of names or the desired voice, before supplying the source passage. These producer prompting patterns make the translation constraints part of the assignment.
For structured material, lock the original format, hierarchy and indentation. Translate only the visible user-facing text and preserve keys, code labels, properties and variable placeholders. If separators divide a batch of passages, retain their count and position. A useful output remains usable by the same application that consumes the original structure.
This MoE edition’s recommended sampling differs from the dense models: temperature 0.7, top-p 1.0, no top-k filtering and repetition penalty 1.0. The producer specifies no default system prompt. Its example generation allocation of 4096 tokens is a request example, not a claim that the model’s maximum answer length is fixed at that value.
Review the result against both parts of the task: linguistic meaning and structural preservation. For a document workflow, confirm that the glossary is consistent across sections. For an application-language file, compare identifiers and placeholders with the source before passing the translated values onward. These checks assess the output rather than assuming that a requested preservation rule was satisfied.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits tencent/hy-mt2-30b-a3b | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits Examples: system, user, assistant, tool, function Conditions: These roles are explicitly represented by Source compatibility types; the exact model role set remains model-specific. Model-specific | Default— |
Fieldmessages[].content | Typestring|content_part[]|null | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_calls | Typetool_call[] | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_call_id | Typestring | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— |
Fieldmessages[].name | Typestring | RequirementOptional | Values & limits | Default— |
Fieldmessages[].reasoning_content | Typestring | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— |
Fieldmessages[].reasoning_details | Typeobject|array | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmax_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldmax_completion_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldstream_options.include_usage | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstop | Typestring|string[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typefunction_tool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].type | Typestring | RequirementOptional | Values & limits function Model-specific | Default— |
Fieldtools[].function.name | Typestring | RequirementRequired | Values & limits Applies when: function tool supplied | Default— |
Fieldtools[].function.description | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtools[].function.parameters | TypeJSON_schema | RequirementOptional | Values & limits | Default— |
Fieldtool_choice | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldparallel_tool_calls | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldresponse_format | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning_effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldservice_tier | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinstructions | Typestring | RequirementOptional | Values & limits Compatibility field | Default— |
Fieldfunctions | Typefunction[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldfunction_call | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprompt_cache_options | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmodalities | Typestring[] | RequirementOptional | Values & limits text | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— |
Fieldcache_control.ttl | Typestring | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— |
Text · UTF8 text
messages[].content
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages |
Parametermaterial_types | Contracttext |
Parametermessages | Contractmessage array with role/content shape |
Parameterfile_material | Contractactual UTF-8 document inserted as literal text; no file ID inferred |
Parameteroptional_controls | Contractstream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice |
Parametercontrol_scope | Contractmodel-specific; user function tools differ from hosted tools |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_input_binding | Contractnull |
Lifecycle: synchronous_or_streamed
Send exact model ID with messages.
Context length is not an output cap.
A text file may supply literal text; image/audio/file-object inputs need exact binding.
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.000148 / 1K tokens Output · Output tokens $0.00059 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.000148 / 1K tokens Output · Output tokens $0.00059 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "tencent/hy-mt2-30b-a3b", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}')\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\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("choices",[]):\n if x.get("message",{}).get("content"): print(x["message"]["content"])\nPY_RESULT\nfrom openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.chat.completions.create(model='tencent/hy-mt2-30b-a3b',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}])\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n 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\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nsave_result(result.to_dict(mode="json"))\nfrom openai import OpenAI\nimport os\nfrom pathlib import Path\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n 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\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nwith client.chat.completions.stream(model='tencent/hy-mt2-30b-a3b',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:\n with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n for event in stream:\n payload=event.to_dict(mode="json")\n log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n print(json.dumps(payload,ensure_ascii=False))\n final=stream.get_final_completion()\n save_result(final.to_dict(mode="json"),"stream-result")\njson or SSE
Keep every complete returned response, file and content part.
Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldchoices[].index | Typeinteger |
Fieldchoices[].message.content | Typestring|null |
Fieldchoices[].message.tool_calls | Typetool_call[] |
Fieldchoices[].message.reasoning_content | Typestring |
Fieldchoices[].message.reasoning_details | Typeobject|array |
Fieldchoices[].finish_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra tencent/hy-mt2-30b-a3b
Use the method paths shown above.
tencent/hy-mt2-30b-a3bTencent’s Hy-MT2-30B-A3B is the MoE edition of its fast-thinking translation family. It handles instruction-guided translation, terminology and structured data, with a 262,144-position configuration and sampling recommendations distinct from the dense editions.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Hy-MT2-30B-A3B is Tencent Hy’s mixture-of-experts edition of the Hy-MT2 translation family. The producer positions the family for complex written translation tasks and multilingual instructions, including requirements for terminology, style and structured data. The 30B-A3B edition stays distinct from the dense 1.8B and 7B models. The maker’s language table includes Chinese, Traditional Chinese, English, Japanese, Korean, Russian, Arabic, French and Spanish, as well as Cantonese, Tibetan, Kazakh, Mongolian and Uyghur.
Its published configuration has 48 layers, 32 query attention heads and four key/value heads. Each expert layer uses 128 experts with eight selected per token, and the maximum position count is 262,144. These are properties of this exact configuration. The larger family label does not identify one of the separately published quantized artifacts.
Begin a translation task by naming the target language in full and defining the required form of the answer. The maker’s basic template requests only the translated result. If the source is part of a larger document, add the background needed to interpret it, while keeping that context separate from the text to translate.
For terminology-sensitive work, provide a glossary of source terms and preferred translations. For style, state the audience and tone explicitly. A personalized translation can list several requirements, such as the treatment of names or the desired voice, before supplying the source passage. These producer prompting patterns make the translation constraints part of the assignment.
For structured material, lock the original format, hierarchy and indentation. Translate only the visible user-facing text and preserve keys, code labels, properties and variable placeholders. If separators divide a batch of passages, retain their count and position. A useful output remains usable by the same application that consumes the original structure.
This MoE edition’s recommended sampling differs from the dense models: temperature 0.7, top-p 1.0, no top-k filtering and repetition penalty 1.0. The producer specifies no default system prompt. Its example generation allocation of 4096 tokens is a request example, not a claim that the model’s maximum answer length is fixed at that value.
Review the result against both parts of the task: linguistic meaning and structural preservation. For a document workflow, confirm that the glossary is consistent across sections. For an application-language file, compare identifiers and placeholders with the source before passing the translated values onward. These checks assess the output rather than assuming that a requested preservation rule was satisfied.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits tencent/hy-mt2-30b-a3b | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring | RequirementRequired | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF-8 text
messages[].content string
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractstream |
Parametermaterial_types | Contracttext |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: synchronous_or_streamed
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.000148 / 1K tokens Output · Output tokens $0.00059 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.000148 / 1K tokens Output · Output tokens $0.00059 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "tencent/hy-mt2-30b-a3b",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'tencent/hy-mt2-30b-a3b', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'tencent/hy-mt2-30b-a3b', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nJSON or SSE
Keep every complete returned response, file and content part.
Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldcontent[] | Typetext|thinking|tool_use|other content block |
Fieldstop_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra tencent/hy-mt2-30b-a3b
Use the method paths shown above.
Technical example in English
Translate an interface file safely\n\nTranslate only the user-visible YAML values into Russian. Preserve keys, indentation and the {count} placeholder. Use the glossary Retry = Повторить. Return only translated YAML.\nbuttons:\n retry: "Retry"\n cancel: "Cancel"\nstatus:\n pending: "{count} files pending"YAML with the same mapping hierarchy and keys, translated visible values and the unchanged {count} placeholder.Parse the YAML and compare the key tree and placeholder multiset with the source; check the Retry glossary term.