DeepSeek R1 Distill Llama 8B transfers reasoning patterns from DeepSeek R1 into a dense model based on Llama 3.1 8B Base. DeepSeek fine-tunes this foundation with examples generated by R1. The result is a separate 8B distilled checkpoint that works with text input and returns a text answer, including a derivation, a code proposal or an explanation of a logical problem.
Distilled model identity. The manufacturer releases Llama and Qwen distillations with different foundations and sizes. The Llama 8B edition begins with a Base model; Llama 70B begins with Llama 3.3 70B Instruct. Qwen editions use their own foundations. Preserve the exact named checkpoint when choosing a model or interpreting its response. DeepSeek adjusts the distilled configurations and tokenizers, so an unchanged foundation-model setup is not the complete description of the prepared release.
For a mathematical question, state the assumptions, define symbols and include the whole problem. Ask for a stepwise derivation followed by a clearly separated final result. A useful teaching request can ask why a particular transformation is valid and which assumption makes it possible. When an argument depends on a boundary case, include that case in the prompt and ask the model to check it explicitly. This produces a solution you can inspect and discuss rather than an unexplained answer.
Usage recommendations. DeepSeek recommends a temperature between 0.5 and 0.7, with 0.6 preferred for its reasoning series. It advises placing the instructions in the user message, without a separate system prompt, and asks mathematical users to request sequential reasoning with the final answer in box notation. For cases where the intended reasoning pattern is skipped, DeepSeek recommends enforcing the response prefix <think>\\n in a compatible generation setup. These are published model-use recommendations for compatible generation environments.
For programming, supply a concise function or algorithm, representative inputs and the expected outputs. Ask for the proposed implementation and examples that exercise ordinary and exceptional cases. For a bug report, retain the error message and state the intended behaviour, then request a minimal repair with an explanation. A small, focused problem makes it easier to check whether the answer addresses the actual requirement and to locate the point that needs a follow-up.
For logical analysis, provide the premises separately from the conclusion you want tested. Request either an argument connecting them or a counterexample that shows the conclusion need not follow. If the task is underspecified, ask for the missing assumption before a definite conclusion. Independently verify important calculations and run code in its intended project. When assessing the model for a workflow, follow the producer’s advice to use multiple representative trials rather than one favourable response.
", "aboutText": "DeepSeek R1 Distill Llama 8B transfers reasoning patterns from DeepSeek R1 into a dense model based on Llama 3.1 8B Base. DeepSeek fine-tunes this foundation with examples generated by R1. The result is a separate 8B distilled checkpoint that works with text input and returns a text answer, including a derivation, a code proposal or an explanation of a logical problem.\n\nDistilled model identity. The manufacturer releases Llama and Qwen distillations with different foundations and sizes. The Llama 8B edition begins with a Base model; Llama 70B begins with Llama 3.3 70B Instruct. Qwen editions use their own foundations. Preserve the exact named checkpoint when choosing a model or interpreting its response. DeepSeek adjusts the distilled configurations and tokenizers, so an unchanged foundation-model setup is not the complete description of the prepared release.\n\nFor a mathematical question, state the assumptions, define symbols and include the whole problem. Ask for a stepwise derivation followed by a clearly separated final result. A useful teaching request can ask why a particular transformation is valid and which assumption makes it possible. When an argument depends on a boundary case, include that case in the prompt and ask the model to check it explicitly. This produces a solution you can inspect and discuss rather than an unexplained answer.\n\nUsage recommendations. DeepSeek recommends a temperature between 0.5 and 0.7, with 0.6 preferred for its reasoning series. It advises placing the instructions in the user message, without a separate system prompt, and asks mathematical users to request sequential reasoning with the final answer in box notation. For cases where the intended reasoning pattern is skipped, DeepSeek recommends enforcing the response prefix `Technical example in English
Explain a square-root boundary\n\nSolve sqrt(x + 6) = x over the real numbers. Give a concise derivation, state the domain restrictions before squaring and check every resulting candidate in the original equation. Put the final solution set in box notation and explain why squaring can introduce an invalid candidate.A domain statement, an algebraic derivation, candidate substitutions and a boxed solution set.Check that every accepted root satisfies both the sign restriction and the unsquared equation.deepseek/deepseek-r1-distill-llama-8bA compact dense reasoning model built by DeepSeek on Llama 3.1 8B Base and fine-tuned with R1-generated examples. It produces text solutions for mathematical, logical and programming tasks from a clearly stated problem.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
DeepSeek R1 Distill Llama 8B transfers reasoning patterns from DeepSeek R1 into a dense model based on Llama 3.1 8B Base. DeepSeek fine-tunes this foundation with examples generated by R1. The result is a separate 8B distilled checkpoint that works with text input and returns a text answer, including a derivation, a code proposal or an explanation of a logical problem.
Distilled model identity. The manufacturer releases Llama and Qwen distillations with different foundations and sizes. The Llama 8B edition begins with a Base model; Llama 70B begins with Llama 3.3 70B Instruct. Qwen editions use their own foundations. Preserve the exact named checkpoint when choosing a model or interpreting its response. DeepSeek adjusts the distilled configurations and tokenizers, so an unchanged foundation-model setup is not the complete description of the prepared release.
For a mathematical question, state the assumptions, define symbols and include the whole problem. Ask for a stepwise derivation followed by a clearly separated final result. A useful teaching request can ask why a particular transformation is valid and which assumption makes it possible. When an argument depends on a boundary case, include that case in the prompt and ask the model to check it explicitly. This produces a solution you can inspect and discuss rather than an unexplained answer.
Usage recommendations. DeepSeek recommends a temperature between 0.5 and 0.7, with 0.6 preferred for its reasoning series. It advises placing the instructions in the user message, without a separate system prompt, and asks mathematical users to request sequential reasoning with the final answer in box notation. For cases where the intended reasoning pattern is skipped, DeepSeek recommends enforcing the response prefix <think>\\n in a compatible generation setup. These are published model-use recommendations for compatible generation environments.
For programming, supply a concise function or algorithm, representative inputs and the expected outputs. Ask for the proposed implementation and examples that exercise ordinary and exceptional cases. For a bug report, retain the error message and state the intended behaviour, then request a minimal repair with an explanation. A small, focused problem makes it easier to check whether the answer addresses the actual requirement and to locate the point that needs a follow-up.
For logical analysis, provide the premises separately from the conclusion you want tested. Request either an argument connecting them or a counterexample that shows the conclusion need not follow. If the task is underspecified, ask for the missing assumption before a definite conclusion. Independently verify important calculations and run code in its intended project. When assessing the model for a workflow, follow the producer’s advice to use multiple representative trials rather than one favourable response.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits deepseek/deepseek-r1-distill-llama-8b | 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.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.00008 / 1K tokens Output · Output tokens $0.00008 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "deepseek/deepseek-r1-distill-llama-8b", "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='deepseek/deepseek-r1-distill-llama-8b',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='deepseek/deepseek-r1-distill-llama-8b',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 deepseek/deepseek-r1-distill-llama-8b
Use the method paths shown above.
deepseek/deepseek-r1-distill-llama-8bA compact dense reasoning model built by DeepSeek on Llama 3.1 8B Base and fine-tuned with R1-generated examples. It produces text solutions for mathematical, logical and programming tasks from a clearly stated problem.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
DeepSeek R1 Distill Llama 8B transfers reasoning patterns from DeepSeek R1 into a dense model based on Llama 3.1 8B Base. DeepSeek fine-tunes this foundation with examples generated by R1. The result is a separate 8B distilled checkpoint that works with text input and returns a text answer, including a derivation, a code proposal or an explanation of a logical problem.
Distilled model identity. The manufacturer releases Llama and Qwen distillations with different foundations and sizes. The Llama 8B edition begins with a Base model; Llama 70B begins with Llama 3.3 70B Instruct. Qwen editions use their own foundations. Preserve the exact named checkpoint when choosing a model or interpreting its response. DeepSeek adjusts the distilled configurations and tokenizers, so an unchanged foundation-model setup is not the complete description of the prepared release.
For a mathematical question, state the assumptions, define symbols and include the whole problem. Ask for a stepwise derivation followed by a clearly separated final result. A useful teaching request can ask why a particular transformation is valid and which assumption makes it possible. When an argument depends on a boundary case, include that case in the prompt and ask the model to check it explicitly. This produces a solution you can inspect and discuss rather than an unexplained answer.
Usage recommendations. DeepSeek recommends a temperature between 0.5 and 0.7, with 0.6 preferred for its reasoning series. It advises placing the instructions in the user message, without a separate system prompt, and asks mathematical users to request sequential reasoning with the final answer in box notation. For cases where the intended reasoning pattern is skipped, DeepSeek recommends enforcing the response prefix <think>\\n in a compatible generation setup. These are published model-use recommendations for compatible generation environments.
For programming, supply a concise function or algorithm, representative inputs and the expected outputs. Ask for the proposed implementation and examples that exercise ordinary and exceptional cases. For a bug report, retain the error message and state the intended behaviour, then request a minimal repair with an explanation. A small, focused problem makes it easier to check whether the answer addresses the actual requirement and to locate the point that needs a follow-up.
For logical analysis, provide the premises separately from the conclusion you want tested. Request either an argument connecting them or a counterexample that shows the conclusion need not follow. If the task is underspecified, ask for the missing assumption before a definite conclusion. Independently verify important calculations and run code in its intended project. When assessing the model for a workflow, follow the producer’s advice to use multiple representative trials rather than one favourable response.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits deepseek/deepseek-r1-distill-llama-8b | 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.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.00008 / 1K tokens Output · Output tokens $0.00008 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "deepseek/deepseek-r1-distill-llama-8b",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'deepseek/deepseek-r1-distill-llama-8b', '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': 'deepseek/deepseek-r1-distill-llama-8b', '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 deepseek/deepseek-r1-distill-llama-8b
Use the method paths shown above.
Technical example in English
Explain a square-root boundary\n\nSolve sqrt(x + 6) = x over the real numbers. Give a concise derivation, state the domain restrictions before squaring and check every resulting candidate in the original equation. Put the final solution set in box notation and explain why squaring can introduce an invalid candidate.A domain statement, an algebraic derivation, candidate substitutions and a boxed solution set.Check that every accepted root satisfies both the sign restriction and the unsquared equation.