Source: https://ailoot.ai/models/en/qwen2-7b-instruct/ Canonical: https://ailoot.ai/models/en/qwen2-7b-instruct/ Language: en # Qwen2 7B Instruct [Markdown](https://ailoot.ai/models/en/qwen2-7b-instruct/index.md) · [JSON](https://ailoot.ai/models/en/qwen2-7b-instruct/facts.json) ## [Qwen2 7B Instruct](https://ailoot.ai/models/en/qwen2-7b-instruct/) Qwen2-7B-Instruct is an instruction-tuned text model for multilingual dialogue, coding, mathematics and reasoning. Its maker documents context up to 131,072 tokens when the long-context configuration is enabled. - [**Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `qwen2-7b-instruct`](https://ailoot.ai/models/en/qwen2-7b-instruct/#method-0) - [**Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `qwen2-7b-instruct`](https://ailoot.ai/models/en/qwen2-7b-instruct/#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. Qwen2-7B-Instruct is the instruction-tuned, 7B-size edition of Qwen2 for conversation and tasks expressed in natural language. It is a text-input, text-output model: the answer can be an explanation, a translation, a document draft or program code. The maker identifies language understanding, generation, multilingual work, coding, mathematics and reasoning as its main areas of use. Model details Qwen2 includes separate base and instruction-tuned releases. This card concerns the Instruct edition, rather than the Qwen2-7B base model or the family's mixture-of-experts release. The maker's model-information table reports 7.07 billion parameters for the 7B-size model. Its decoder Transformer uses SwiGLU, attention QKV bias and grouped-query attention, with a tokenizer designed for natural languages and code. These are architecture and representation choices, not additional input modalities. The release announcement describes training in English and Chinese plus 27 additional languages. Those include Russian, Czech and Polish; German, French and Spanish; Arabic, Persian and Turkish; Japanese and Korean; and further languages across South and Southeast Asia. A multilingual assistant can therefore receive an instruction and source material in different languages, but should still specify the language, terminology and audience of the final artifact. Training and the Instruct distinction After pretraining, the maker uses supervised fine-tuning and direct preference optimization. The instruction-tuned edition is intended to respond to a request in a conversation, while the base edition has a different role as a language-model foundation. Keep the edition name explicit when selecting a model or comparing prompt behavior. Training a model to follow instructions and supplying a precise instruction at use time are complementary parts of that workflow. Preparing a conversational input The model card's quick start separates a system instruction from the user's question and applies the matching chat template before generation. For an application-level task, the same practical distinction is useful: define the assistant's role and response rules separately from the source passage or code it must work on. A textual label written inside a document is not a substitute for correctly formatted conversation roles in the surrounding interface. A technical explanation task can provide a function and one input example, then ask what each branch does and which conditions affect the result. A coding task can specify the accepted input, expected return value and constraints on changing existing behavior. Request the implementation followed by examples for a normal and an edge case. These are applications of the documented language and coding capabilities; generating code is a text result, while running it belongs to the application environment. For multilingual editing, supply the original passage, the target audience and a small terminology list. Ask for a revision that retains the original names and conditions, then a short explanation of the changes. This is more specific than a request to improve the text: it defines which meaning must survive and what usable output should be returned. Processing long texts The maker documents context support up to 131,072 tokens, but the long-context instructions matter. Inputs beyond 32,768 tokens require the documented YaRN extension configuration. The published example uses a scaling factor of 4.0 with an original position setting of 32,768. The maker notes that the static extension setting can affect shorter-text behavior and recommends enabling it when long-context processing is actually needed. Prepare a long document task with a focused question, named source sections and an explicit output structure. For example, provide several parts of a technical specification and ask for the requirements relevant to a particular change, with the source section kept beside each finding. A large input window is useful for retaining those parts together; the answer can still be a compact table rather than a retelling of the entire document. Important boundaries The 131,072-token figure is extended-context support, not permission to ignore the long-context configuration. The quick-start example's 512 generated tokens are an example setting, not a published maximum output capacity. This Instruct release is a text model and should not inherit image or video capabilities from Qwen-VL, or the specialized tool-integrated mathematical training of Qwen2.5-Math. Choose the named edition and prepare the text for the particular explanation, code or document you need. **Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `qwen2-7b-instruct` [`qwen2-7b-instruct`](https://ailoot.ai/models/en/#m=qwen2-7b-instruct&route=Ultra&caller=qwen2-7b-instruct&fmt=chat) Alibaba /`qwen2-7b-instruct` ### Overview Qwen2-7B-Instruct is an instruction-tuned text model for multilingual dialogue, coding, mathematics and reasoning. Its maker documents context up to 131,072 tokens when the long-context configuration is enabled. 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 qwen2-7b-instruct | Default— | | Field`messages` | Type`message[]` | RequirementRequired | Values & limits Minimum items: 1 | Default— | | Field`messages[].role` | Type`string` | RequirementRequired | Values & limits Model-specific | Default— | | Field`messages[].content` | Type`string` | RequirementOptional | Values & limits Applies when: Text conversation | Default— | | Field`stream` | Type`boolean` | RequirementOptional | Values & limits | Defaultfalse | 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 | | Parameter`control_scope` | ContractText conversation; stream is a boolean and defaults to false. | | Parameter`output_modalities` | Contracttext | | Parameter`hosted_operations` | Contractfalse | | Parameter`multimodal_input_binding` | Contractnull | Method aliases: `/chat/completions` 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 Not specified When fast mode is selected Flex mode Not specified When flex mode is selected Maximum reasoning effort Not specified When maximum reasoning effort uses the default adjustment #### Ailoot rates #### Prices by configuration | Configuration | Rate schedule | Charge | | --- | --- | --- | | ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens **$0.000286** / 1K tokens Output · Output tokens **$0.00058** / 1K tokens Input · Cache read **$0** / 1K tokens Input · Cache write **$0** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens | Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/) ### Example requests #### Ailoot Ultra · OpenAI Chat curlPython SDK ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" python3 - <<'PY_REQUEST' import json,os from pathlib import Path p=json.loads('{"model":"qwen2-7b-instruct","messages":[{"role":"user","content":"hello"}],"stream":false}') 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="qwen2-7b-instruct", messages=[{"role":"user","content":"hello"}], stream=False) 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")) ``` ### 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** [`qwen2-7b-instruct`](https://ailoot.ai/models/en/#m=qwen2-7b-instruct&route=Ultra&caller=qwen2-7b-instruct&fmt=chat) #### Method path aliases `/chat/completions` #### Related caller IDs [`qwen2-7b-instruct`](https://ailoot.ai/models/en/#m=qwen2-7b-instruct&route=Ultra&caller=qwen2-7b-instruct&fmt=chat) **Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `qwen2-7b-instruct` [`qwen2-7b-instruct`](https://ailoot.ai/models/en/#m=qwen2-7b-instruct&route=Ultra&caller=qwen2-7b-instruct&fmt=messages) Alibaba /`qwen2-7b-instruct` ### Overview Qwen2-7B-Instruct is an instruction-tuned text model for multilingual dialogue, coding, mathematics and reasoning. Its maker documents context up to 131,072 tokens when the long-context configuration is enabled. 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 qwen2-7b-instruct | 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 Not specified When fast mode is selected Flex mode Not specified When flex mode is selected Maximum reasoning effort Not specified When maximum reasoning effort uses the default adjustment #### Ailoot rates #### Prices by configuration | Configuration | Rate schedule | Charge | | --- | --- | --- | | ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens **$0.000286** / 1K tokens Output · Output tokens **$0.00058** / 1K tokens Input · Cache read **$0** / 1K tokens Input · Cache write **$0** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens | Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/) ### Example requests #### Ailoot Ultra · Anthropic Messages Complete minimal JSON bodycurl · use your UTF-8 documentPython · save the complete result ```json { "model": "qwen2-7b-instruct", "max_tokens": 128, "messages": [ { "role": "user", "content": "Summarize the incident report." } ], "stream": false } ``` ```bash set -eu : "${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': 'qwen2-7b-instruct', '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': 'qwen2-7b-instruct', '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** [`qwen2-7b-instruct`](https://ailoot.ai/models/en/#m=qwen2-7b-instruct&route=Ultra&caller=qwen2-7b-instruct&fmt=messages) #### Method path aliases `/chat/completions` #### Related caller IDs [`qwen2-7b-instruct`](https://ailoot.ai/models/en/#m=qwen2-7b-instruct&route=Ultra&caller=qwen2-7b-instruct&fmt=messages) FAQ · Pro/Qwen/Qwen2-7B-Instruct What inputs and results are available? `POST /v1/chat/completions` · `qwen2-7b-instruct`: `text` → `text` `POST /v1/messages` · `qwen2-7b-instruct`: `text` → `json`, `sse` Which fields does the request use? `POST /v1/chat/completions` · `qwen2-7b-instruct`: `model`, `messages`, `stream` `POST /v1/messages` · `qwen2-7b-instruct`: `model`, `messages`, `max_tokens`, `stream` What limits apply? `POST /v1/chat/completions` · `qwen2-7b-instruct`: `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. 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