Hy-MT2-7B is Tencent Hy’s dense 7B translation model in the fast-thinking Hy-MT2 family. It takes written source material and translation instructions and produces translated text. The family is designed for real-world tasks in which terminology, audience, background and formatting requirements matter alongside the language pair. 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.
The exact 7B configuration has 32 layers, 32 query attention heads and eight key/value heads, with 262,144 maximum positions. It is distinct from the smaller 1.8B configuration and the 30B-A3B mixture-of-experts edition. Separate compressed releases have their own artifact identity and deployment conditions.
The producer’s default translation pattern names the target language in full and requests only the translated result. Use full language names consistent with the language of the prompt. State the source and the instructions separately so that a quoted instruction inside the material is treated as text to translate rather than a new task for the model.
Supply preferred terminology as explicit source-to-target pairs. A style instruction can define the voice and audience, while personalization can list the requirements that the passage must satisfy. Background context is useful when a word has several meanings or a sentence depends on information elsewhere in the document. Provide that context before requesting the translation.
For structured data, preserve the original nesting, hierarchy and indentation and translate only visible user-facing content. The maker’s examples retain keys, code tags, properties and placeholders. Delimiters also need their original count and placement. These instructions help keep the translated material usable in the workflow that consumes it.
Tencent recommends temperature 0.7, top-p 0.6, top-k 20 and repetition penalty 1.05 for the 7B and 1.8B editions, with no default system prompt. The example allocates 4096 generated tokens; it demonstrates a request budget rather than an intrinsic model maximum. Those settings are producer guidance, separate from a guarantee about controls exposed by a particular application.
For a useful result, ask for the translated passage or the intact structured object, then assess meaning, terminology and preserved syntax against the original. A multilingual document can use the same glossary across sections; an application-language file can retain every identifier and variable while changing the visible strings. The final output should satisfy both the linguistic and structural parts of the assignment.
", "aboutText": "Hy-MT2-7B is Tencent Hy’s dense 7B translation model in the fast-thinking Hy-MT2 family. It takes written source material and translation instructions and produces translated text. The family is designed for real-world tasks in which terminology, audience, background and formatting requirements matter alongside the language pair. 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\nThe exact 7B configuration has 32 layers, 32 query attention heads and eight key/value heads, with 262,144 maximum positions. It is distinct from the smaller 1.8B configuration and the 30B-A3B mixture-of-experts edition. Separate compressed releases have their own artifact identity and deployment conditions.\n\nThe producer’s default translation pattern names the target language in full and requests only the translated result. Use full language names consistent with the language of the prompt. State the source and the instructions separately so that a quoted instruction inside the material is treated as text to translate rather than a new task for the model.\n\nSupply preferred terminology as explicit source-to-target pairs. A style instruction can define the voice and audience, while personalization can list the requirements that the passage must satisfy. Background context is useful when a word has several meanings or a sentence depends on information elsewhere in the document. Provide that context before requesting the translation.\n\nFor structured data, preserve the original nesting, hierarchy and indentation and translate only visible user-facing content. The maker’s examples retain keys, code tags, properties and placeholders. Delimiters also need their original count and placement. These instructions help keep the translated material usable in the workflow that consumes it.\n\nTencent recommends temperature 0.7, top-p 0.6, top-k 20 and repetition penalty 1.05 for the 7B and 1.8B editions, with no default system prompt. The example allocates 4096 generated tokens; it demonstrates a request budget rather than an intrinsic model maximum. Those settings are producer guidance, separate from a guarantee about controls exposed by a particular application.\n\nFor a useful result, ask for the translated passage or the intact structured object, then assess meaning, terminology and preserved syntax against the original. A multilingual document can use the same glossary across sections; an application-language file can retain every identifier and variable while changing the visible strings. The final output should satisfy both the linguistic and structural parts of the assignment.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Translate UI strings with placeholders\n\nTranslate only the user-facing values in this YAML into Russian. Preserve keys, indentation and placeholders exactly. Source:\ncheckout:\n title: "Confirm order"\n greeting: "Hello, {name}"\n total: "Total: {amount} RUB"\nUse a neutral interface tone. Return only the translated YAML; do not translate RUB or add fields.A YAML block with the same hierarchy and keys, translated visible strings and intact placeholders and RUB.Compare keys, indentation, {name}, {amount} and RUB byte-for-byte with the source.tencent/hy-mt2-7bTencent’s Hy-MT2-7B is the 7B dense model in its instruction-guided translation family. It supports glossary, style, background and structure-preservation prompts, with a 262,144-position configuration and a distinct identity from its 1.8B and MoE siblings.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Hy-MT2-7B is Tencent Hy’s dense 7B translation model in the fast-thinking Hy-MT2 family. It takes written source material and translation instructions and produces translated text. The family is designed for real-world tasks in which terminology, audience, background and formatting requirements matter alongside the language pair. 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.
The exact 7B configuration has 32 layers, 32 query attention heads and eight key/value heads, with 262,144 maximum positions. It is distinct from the smaller 1.8B configuration and the 30B-A3B mixture-of-experts edition. Separate compressed releases have their own artifact identity and deployment conditions.
The producer’s default translation pattern names the target language in full and requests only the translated result. Use full language names consistent with the language of the prompt. State the source and the instructions separately so that a quoted instruction inside the material is treated as text to translate rather than a new task for the model.
Supply preferred terminology as explicit source-to-target pairs. A style instruction can define the voice and audience, while personalization can list the requirements that the passage must satisfy. Background context is useful when a word has several meanings or a sentence depends on information elsewhere in the document. Provide that context before requesting the translation.
For structured data, preserve the original nesting, hierarchy and indentation and translate only visible user-facing content. The maker’s examples retain keys, code tags, properties and placeholders. Delimiters also need their original count and placement. These instructions help keep the translated material usable in the workflow that consumes it.
Tencent recommends temperature 0.7, top-p 0.6, top-k 20 and repetition penalty 1.05 for the 7B and 1.8B editions, with no default system prompt. The example allocates 4096 generated tokens; it demonstrates a request budget rather than an intrinsic model maximum. Those settings are producer guidance, separate from a guarantee about controls exposed by a particular application.
For a useful result, ask for the translated passage or the intact structured object, then assess meaning, terminology and preserved syntax against the original. A multilingual document can use the same glossary across sections; an application-language file can retain every identifier and variable while changing the visible strings. The final output should satisfy both the linguistic and structural parts of the assignment.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits tencent/hy-mt2-7b | 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-mt2-7b", "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-mt2-7b',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-mt2-7b',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-mt2-7b
Use the method paths shown above.
tencent/hy-mt2-7bTencent’s Hy-MT2-7B is the 7B dense model in its instruction-guided translation family. It supports glossary, style, background and structure-preservation prompts, with a 262,144-position configuration and a distinct identity from its 1.8B and MoE siblings.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Hy-MT2-7B is Tencent Hy’s dense 7B translation model in the fast-thinking Hy-MT2 family. It takes written source material and translation instructions and produces translated text. The family is designed for real-world tasks in which terminology, audience, background and formatting requirements matter alongside the language pair. 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.
The exact 7B configuration has 32 layers, 32 query attention heads and eight key/value heads, with 262,144 maximum positions. It is distinct from the smaller 1.8B configuration and the 30B-A3B mixture-of-experts edition. Separate compressed releases have their own artifact identity and deployment conditions.
The producer’s default translation pattern names the target language in full and requests only the translated result. Use full language names consistent with the language of the prompt. State the source and the instructions separately so that a quoted instruction inside the material is treated as text to translate rather than a new task for the model.
Supply preferred terminology as explicit source-to-target pairs. A style instruction can define the voice and audience, while personalization can list the requirements that the passage must satisfy. Background context is useful when a word has several meanings or a sentence depends on information elsewhere in the document. Provide that context before requesting the translation.
For structured data, preserve the original nesting, hierarchy and indentation and translate only visible user-facing content. The maker’s examples retain keys, code tags, properties and placeholders. Delimiters also need their original count and placement. These instructions help keep the translated material usable in the workflow that consumes it.
Tencent recommends temperature 0.7, top-p 0.6, top-k 20 and repetition penalty 1.05 for the 7B and 1.8B editions, with no default system prompt. The example allocates 4096 generated tokens; it demonstrates a request budget rather than an intrinsic model maximum. Those settings are producer guidance, separate from a guarantee about controls exposed by a particular application.
For a useful result, ask for the translated passage or the intact structured object, then assess meaning, terminology and preserved syntax against the original. A multilingual document can use the same glossary across sections; an application-language file can retain every identifier and variable while changing the visible strings. The final output should satisfy both the linguistic and structural parts of the assignment.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits tencent/hy-mt2-7b | 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 $0.00059 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "tencent/hy-mt2-7b",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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-mt2-7b', '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-mt2-7b', '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-mt2-7b
Use the method paths shown above.
Technical example in English
Translate UI strings with placeholders\n\nTranslate only the user-facing values in this YAML into Russian. Preserve keys, indentation and placeholders exactly. Source:\ncheckout:\n title: "Confirm order"\n greeting: "Hello, {name}"\n total: "Total: {amount} RUB"\nUse a neutral interface tone. Return only the translated YAML; do not translate RUB or add fields.A YAML block with the same hierarchy and keys, translated visible strings and intact placeholders and RUB.Compare keys, indentation, {name}, {amount} and RUB byte-for-byte with the source.