Source: https://ailoot.ai/models/en/google-gemma-2-27b-it/ Canonical: https://ailoot.ai/models/en/google-gemma-2-27b-it/ Language: en # Google: Gemma 2 27B [Markdown](https://ailoot.ai/models/en/google-gemma-2-27b-it/index.md) · [JSON](https://ailoot.ai/models/en/google-gemma-2-27b-it/facts.json) ## [Google: Gemma 2 27B](https://ailoot.ai/models/en/google-gemma-2-27b-it/) Gemma 2 27B IT is Google’s instruction-tuned, text-only model for English question answering, summaries, writing and reasoning. Its 8,192-token context and conversational format suit focused source-based assignments and code explanations. - [**Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `google/gemma-2-27b-it`](https://ailoot.ai/models/en/google-gemma-2-27b-it/#method-0) - [**Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `google/gemma-2-27b-it`](https://ailoot.ai/models/en/google-gemma-2-27b-it/#method-1) About the model This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below. Gemma 2 27B IT is Google’s 27-billion-parameter instruction-tuned edition of Gemma 2. It is an open-weight, decoder-only language model that takes text and generates text, with an English-language focus. Its intended tasks include question answering, summarisation, reasoning, creative writing and code-related work. IT identifies the instruction-tuned variant, distinct from the pretrained model and from separately quantized derivatives. The technical report documents an 8,192-token context. Gemma 2 alternates local attention with a 4,096-token sliding window and global attention spanning the full context; the 27B architecture has 46 layers and grouped-query attention. These are characteristics of the producer model rather than settings for an application’s response length. Prepare a focused source excerpt and leave space for the requested answer and conversation, instead of assuming the much larger contexts of later Gemma generations. The 27B base was trained from scratch on 13 trillion tokens of primarily English text. The documented sources include web documents, code and mathematical or scientific material. The report distinguishes this training from the distillation used for the smaller 2B and 9B base models. Instruction tuning adds supervised prompt-response training and preference-based refinement for a conversational response. That gives the IT edition a different use profile from continuing arbitrary text with the pretrained model. For a document summary, provide the passage and say what the reader needs from it: decisions, requirements, an explanation or a short set of key points. An illustrative brief is: “Summarise this specification for a support engineer. Preserve all field names, distinguish explicit requirements from examples and list the details the passage does not define.” If a fact must come from the source, ask for the relevant wording or a section reference alongside the explanation. Conversational use follows Gemma 2’s user/model turn template. Google’s exact model card demonstrates keeping the dialogue in that template, with delimiters identifying the start and end of each turn, so a follow-up can build on the previous response. The documented format differs from Gemma 4’s new native system-role and thinking controls. A practical conversation can first extract the facts, then request a shorter explanation or a draft message while preserving the extracted names and conditions. For code, include the relevant fragment, the programming language, the observed behaviour and the result you expect. Ask for an explanation, a proposed correction or a worked example, with a stated scope. For mathematical reasoning, write the full problem, variable definitions and assumptions as text. This edition has no native visual or audio input: a photographed problem or spoken explanation needs to be transcribed into text first. The output can contain code or a formatted answer, but those text forms do not establish a dedicated tool-calling or constrained-output mode. The model card identifies ambiguity, sarcasm, complex open-ended instructions and factual accuracy as areas where responses can be limited. For a source-based answer, supply the authoritative passage and ask the model to state where information is absent. For an editing task, specify the audience, tone, required facts and the parts that may change. The useful artifact is a focused English answer, summary, draft or code explanation that can be compared with the material and conditions supplied in the prompt. Practical task ### Explain a specification for support Technical example in English ``` Explain a specification for support Summarize this specification for a support engineer: “retry_after is optional and measured in seconds. If absent, the client must not infer a retry time. Example: retry_after = 30. A failed request may include error_id.” Separate requirements from examples and list what the passage does not define. Keep field names unchanged. ``` #### Expected result ``` A short support note with requirements, examples and unspecified details in separate sections. ``` #### Check the result ``` Check that 30 seconds remains an example and that error_id is not described as mandatory. ``` **Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `google/gemma-2-27b-it` [`google/gemma-2-27b-it`](https://ailoot.ai/models/en/#m=google%2Fgemma-2-27b-it&route=Ultra&caller=google%2Fgemma-2-27b-it&fmt=chat) Google /`google/gemma-2-27b-it` ### Overview Gemma 2 27B IT is Google’s instruction-tuned, text-only model for English question answering, summaries, writing and reasoning. Its 8,192-token context and conversational format suit focused source-based assignments and code explanations. Accepted inputs Text Results Text ### Available in Ailoot · inputs & methods **Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` Input types `text` Output types `text` Output format json or SSE | Field | Type | Requirement | Values & limits | Default | | --- | --- | --- | --- | --- | | Field`model` | Type`string` | RequirementRequired | Values & limits google/gemma-2-27b-it | Default— | | Field`messages` | Type`message[]` | RequirementRequired | Values & limits Minimum items: 1 | Default— | | Field`messages[].role` | Type`string` | 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— | | Field`messages[].content` | Type`string\|content_part[]\|null` | RequirementOptional | Values & limits | Default— | | Field`messages[].tool_calls` | Type`tool_call[]` | RequirementOptional | Values & limits | Default— | | Field`messages[].tool_call_id` | Type`string` | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— | | Field`messages[].name` | Type`string` | RequirementOptional | Values & limits | Default— | | Field`messages[].reasoning_content` | Type`string` | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— | | Field`messages[].reasoning_details` | Type`object\|array` | RequirementOptional | Values & limits Model-specific | Default— | | Field`max_tokens` | Type`integer` | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— | | Field`max_completion_tokens` | Type`integer` | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— | | Field`stream` | Type`boolean` | RequirementOptional | Values & limits | Defaultfalse | | Field`stream_options.include_usage` | Type`boolean` | RequirementOptional | Values & limits Model-specific | Default— | | Field`temperature` | Type`number` | RequirementOptional | Values & limits Model-specific | Default— | | Field`top_p` | Type`number` | RequirementOptional | Values & limits Model-specific | Default— | | Field`stop` | Type`string\|string[]` | RequirementOptional | Values & limits Model-specific | Default— | | Field`tools` | Type`function_tool[]` | RequirementOptional | Values & limits Model-specific | Default— | | Field`tools[].type` | Type`string` | RequirementOptional | Values & limits function Model-specific | Default— | | Field`tools[].function.name` | Type`string` | RequirementRequired | Values & limits Applies when: function tool supplied | Default— | | Field`tools[].function.description` | Type`string` | RequirementOptional | Values & limits | Default— | | Field`tools[].function.parameters` | Type`JSON_schema` | RequirementOptional | Values & limits | Default— | | Field`tool_choice` | Type`string\|object` | RequirementOptional | Values & limits Model-specific | Default— | | Field`parallel_tool_calls` | Type`boolean` | RequirementOptional | Values & limits Model-specific | Default— | | Field`response_format` | Type`object` | RequirementOptional | Values & limits Model-specific | Default— | | Field`reasoning_effort` | Type`string` | RequirementOptional | Values & limits Model-specific | Default— | | Field`reasoning` | Type`object` | RequirementOptional | Values & limits Model-specific | Default— | | Field`service_tier` | Type`string` | RequirementOptional | Values & limits Model-specific | Default— | | Field`instructions` | Type`string` | RequirementOptional | Values & limits Compatibility field | Default— | | Field`functions` | Type`function[]` | RequirementOptional | Values & limits Model-specific | Default— | | Field`function_call` | Type`string\|object` | RequirementOptional | Values & limits Model-specific | Default— | | Field`prompt_cache_options` | Type`object` | RequirementOptional | Values & limits Model-specific | Default— | | Field`modalities` | Type`string[]` | RequirementOptional | Values & limits text | Default— | | Field`provider` | Type`object` | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— | | Field`cache_control.ttl` | Type`string` | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— | Input material combinations **Text** · UTF8 text `messages[].content` Method parameters and lifecycle | Parameter | Contract | | --- | --- | | Parameter`content_type` | Contractapplication/json | | Parameter`required` | Contractmodel, messages | | Parameter`material_types` | Contracttext | | Parameter`messages` | Contractmessage array with role/content shape | | Parameter`file_material` | Contractactual UTF-8 document inserted as literal text; no file ID inferred | | Parameter`optional_controls` | Contractstream, temperature, top\_p, stop, max\_tokens, max\_completion\_tokens, reasoning, response\_format, tools, tool\_choice | | Parameter`control_scope` | Contractmodel-specific; user function tools differ from hosted tools | | Parameter`output_modalities` | Contracttext | | Parameter`hosted_operations` | Contractfalse | | Parameter`multimodal_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. ### Pricing Tariff references do not add request options. Supported parameters are listed for the selected method. Fast mode Standard rate When fast mode is selected Flex mode Standard rate When flex mode is selected Maximum reasoning effort Standard rate When maximum reasoning effort uses the default adjustment Explicit cache pricing Not enabled When explicit cache is requested #### Ailoot rates #### Prices by configuration 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 **$0.0013** / 1K tokens Output · Output tokens **$0.0013** / 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 **$0.0013** / 1K tokens Output · Output tokens **$0.0013** / 1K tokens Input · Cache read **—** / 1K tokens Input · Cache write **—** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens | Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/) ### Example requests #### Ailoot Ultra · OpenAI Chat curlPython SDKPython stream ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" python3 - <<'PY_REQUEST' import json,os from pathlib import Path p=json.loads('{"model": "google/gemma-2-27b-it", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}') 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 r=json.loads(Path("result.json").read_text()) for x in r.get("choices",[]): if x.get("message",{}).get("content"): print(x["message"]["content"]) 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) result=client.chat.completions.create(model='google/gemma-2-27b-it',messages=[{"role":"user","content":"Explain observations and hypotheses 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(result.to_dict(mode="json")) ``` ```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='google/gemma-2-27b-it',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") ``` ### Receive and save results #### Ailoot Ultra · OpenAI Chat json or SSE ##### Keep all completed results 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. ##### If the original response needs checking Keep request/response IDs and headers. No generic text job retrieval URL is supplied. Response fields | Field | Type | | --- | --- | | Field`choices[].index` | Typeinteger | | Field`choices[].message.content` | Typestring\|null | | Field`choices[].message.tool_calls` | Typetool\_call\[\] | | Field`choices[].message.reasoning_content` | Typestring | | Field`choices[].message.reasoning_details` | Typeobject\|array | | Field`choices[].finish_reason` | Typestring\|null | | Field`usage` | Typeobject | Errors and next steps | 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. | ### Aliases #### Model aliases **Ailoot Ultra** [`google/gemma-2-27b-it`](https://ailoot.ai/models/en/#m=google%2Fgemma-2-27b-it&route=Ultra&caller=google%2Fgemma-2-27b-it&fmt=chat) #### Method path aliases Use the method paths shown above. #### Related caller IDs [`google/gemma-2-27b-it`](https://ailoot.ai/models/en/#m=google%2Fgemma-2-27b-it&route=Ultra&caller=google%2Fgemma-2-27b-it&fmt=chat) **Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `google/gemma-2-27b-it` [`google/gemma-2-27b-it`](https://ailoot.ai/models/en/#m=google%2Fgemma-2-27b-it&route=Ultra&caller=google%2Fgemma-2-27b-it&fmt=messages) Google /`google/gemma-2-27b-it` ### Overview Gemma 2 27B IT is Google’s instruction-tuned, text-only model for English question answering, summaries, writing and reasoning. Its 8,192-token context and conversational format suit focused source-based assignments and code explanations. Accepted inputs Text Results json · sse ### Available in Ailoot · inputs & methods **Ailoot Ultra · Anthropic Messages** `POST /v1/messages` Input types `text` Output types `json`, `sse` Output format JSON or SSE | Field | Type | Requirement | Values & limits | Default | | --- | --- | --- | --- | --- | | Field`model` | Type`string` | RequirementRequired | Values & limits google/gemma-2-27b-it | Default— | | Field`messages` | Type`message[]` | RequirementRequired | Values & limits | Default— | | Field`messages[].role` | Type`string` | RequirementRequired | Values & limits user, assistant | Default— | | Field`messages[].content` | Type`string` | RequirementRequired | Values & limits | Default— | | Field`max_tokens` | Type`integer` | RequirementRequired | Values & limits | Default— | | Field`stream` | Type`boolean` | RequirementOptional | Values & limits | Defaultfalse | Input material combinations **Text** · UTF-8 text `messages[].content string` Method parameters and lifecycle | Parameter | Contract | | --- | --- | | Parameter`content_type` | Contractapplication/json | | Parameter`required` | Contractmodel, messages, max\_tokens | | Parameter`optional_controls` | Contractstream | | Parameter`material_types` | Contracttext | | Parameter`text_file_material` | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. | Lifecycle: `synchronous_or_streamed` ### Pricing Tariff references do not add request options. Supported parameters are listed for the selected method. Fast mode Standard rate When fast mode is selected Flex mode Standard rate When flex mode is selected Maximum reasoning effort Standard rate When maximum reasoning effort uses the default adjustment Explicit cache pricing Not enabled When explicit cache is requested #### Ailoot rates #### Prices by configuration 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 **$0.0013** / 1K tokens Output · Output tokens **$0.0013** / 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 **$0.0013** / 1K tokens Output · Output tokens **$0.0013** / 1K tokens Input · Cache read **—** / 1K tokens Input · Cache write **—** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens | Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/) ### Example requests #### Ailoot Ultra · Anthropic Messages Complete minimal JSON bodycurl · use your UTF-8 documentPython · save the complete result ```json { "model": "google/gemma-2-27b-it", "max_tokens": 128, "messages": [ { "role": "user", "content": "Summarize the incident report." } ], "stream": false } ``` ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" : "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}" export AILOOT_TEXT_FILE python3 - <<'PY_REQUEST' import json, os from pathlib import Path source_file = Path(os.environ['AILOOT_TEXT_FILE']) if not source_file.is_file(): raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document') text = source_file.read_text(encoding='utf-8') if not text.strip(): raise SystemExit('Choose a document containing text') payload = {'model': 'google/gemma-2-27b-it', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]} payload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text Path('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8') 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 result = json.loads(Path('result.json').read_text(encoding='utf-8')) print(json.dumps(result, ensure_ascii=False, indent=2)) PY_RESULT ``` ```python import json, os from pathlib import Path source_file = Path(os.environ['AILOOT_TEXT_FILE']) if not source_file.is_file(): raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document') text = source_file.read_text(encoding='utf-8') if not text.strip(): raise SystemExit('Choose a document containing text') payload = {'model': 'google/gemma-2-27b-it', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]} payload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text from urllib.request import Request, urlopen from urllib.error import HTTPError api_key = os.environ['AILOOT_API_KEY'] if not api_key.strip(): raise SystemExit('Set AILOOT_API_KEY before making a request') headers = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'} headers['x-api-key'] = api_key request = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST') try: with urlopen(request, timeout=120) as response: raw = response.read() Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8') except HTTPError as error: Path('result.json').write_bytes(error.read()) Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8') raise Path('result.json').write_bytes(raw) import json from pathlib import Path result = json.loads(Path('result.json').read_text(encoding='utf-8')) print(json.dumps(result, ensure_ascii=False, indent=2)) ``` ### Receive and save results #### Ailoot Ultra · Anthropic Messages JSON or SSE ##### Keep all completed results 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. ##### If the original response needs checking Keep request/response IDs and headers. No generic text job retrieval URL is supplied. Response fields | Field | Type | | --- | --- | | Field`id` | Typestring | | Field`model` | Typestring | | Field`content[]` | Typetext\|thinking\|tool\_use\|other content block | | Field`stop_reason` | Typestring\|null | | Field`usage` | Typeobject | Errors and next steps | 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. | ### Aliases #### Model aliases **Ailoot Ultra** [`google/gemma-2-27b-it`](https://ailoot.ai/models/en/#m=google%2Fgemma-2-27b-it&route=Ultra&caller=google%2Fgemma-2-27b-it&fmt=messages) #### Method path aliases Use the method paths shown above. #### Related caller IDs [`google/gemma-2-27b-it`](https://ailoot.ai/models/en/#m=google%2Fgemma-2-27b-it&route=Ultra&caller=google%2Fgemma-2-27b-it&fmt=messages) FAQ · Google: Gemma 2 27B What inputs and results are available? `POST /v1/chat/completions` · `google/gemma-2-27b-it`: `text` → `text` `POST /v1/messages` · `google/gemma-2-27b-it`: `text` → `json`, `sse` Which fields does the request use? `POST /v1/chat/completions` · `google/gemma-2-27b-it`: `model`, `messages`, `stream`, `modalities`, `provider` `POST /v1/messages` · `google/gemma-2-27b-it`: `model`, `messages`, `max_tokens`, `stream` What limits apply? `POST /v1/chat/completions` · `google/gemma-2-27b-it`: `messages.min_items`: `1` Connect Ailoot to Claude Code, OpenCode, Hermes or your own app. ## The company and its project portfolio The company and its project portfolio: millions of customers; monthly sales in the billions. ## Buy tokens at a discount. Subscription discounts for regular use. Promo codes offer up to 50% off token purchases. Subscription Choose a discounted plan for your regular requests. Promo code: up to 50% off Enter your code at checkout. The price updates to reflect the current promotion. Spending in your history The model, tokens and amount are recorded for each request. [Choose a subscription](https://ailoot.ai/docs/en/billing/subscriptions/) · [View model prices](https://ailoot.ai/models/en/) ## Connect the API to a product you sell. Use Ailoot in SaaS, apps and bots under its official service offer and licence agreement. Request a reseller licence for resale. Commercial application Add text, image, video and audio generation to paid features in your service. Reseller licence Send a description of your project and how you provide access. Request written terms for use and resale. Documents on the website The service offer, licence agreement and data processing policy are available before you connect. [Request a reseller licence](mailto:support@ailoot.ai) · [Service offer and licence agreement](https://ailoot.ai/legal/offer?lang=en) ## Ailoot · The company and its project portfolio Ailoot combines established operating experience, a large client base, stable service and ongoing KPI control. The company and its project portfolio: millions of customers; monthly sales in the billions. Ailoot brings that operating experience to API access for AI features in customer products, agents, applications and automations.