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data-props="{"activeTab":"modelCard","author":{"_id":"64c8b5837fe12ecd0a7e92eb","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6215ca5692c0ecfba9186921/hrRM50-6XcdWgg2AKpENG.jpeg","fullname":"Qwen","name":"Qwen","type":"org","isHf":false,"isHfAdmin":false,"isMod":false,"plan":"team","followerCount":103873,"isUserFollowing":false},"canReadRepoSettings":false,"canWriteRepoContent":false,"canDisable":false,"model":{"author":"Qwen","cardData":{"library_name":"transformers","license":"apache-2.0","license_link":"https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE","pipeline_tag":"text-generation","base_model":["Qwen/Qwen3-8B-Base"]},"cardExists":true,"config":{"architectures":["Qwen3ForCausalLM"],"model_type":"qwen3","tokenizer_config":{"bos_token":null,"chat_template":"{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in content %}\n {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}","eos_token":"<|im_end|>","pad_token":"<|endoftext|>","unk_token":null}},"createdAt":"2025-04-27T03:42:21.000Z","discussionsDisabled":false,"discussionsSorting":"recently-created","downloads":12880447,"downloadsAllTime":120758624,"id":"Qwen/Qwen3-8B","isLikedByUser":false,"availableInferenceProviders":[{"provider":"nscale","modelStatus":"live","providerStatus":"live","providerId":"Qwen/Qwen3-8B","task":"conversational","adapterWeightsPath":"model-00001-of-00005.safetensors","features":{"structuredOutput":false,"toolCalling":true},"isCheapestPricingOutput":true,"isFastestThroughput":true,"isModelAuthor":false,"tokensPerSecond":131.38078276863246,"pricingOutput":0.18},{"provider":"featherless-ai","modelStatus":"live","providerStatus":"live","providerId":"Qwen/Qwen3-8B","task":"conversational","adapterWeightsPath":"model-00001-of-00005.safetensors","isCheapestPricingOutput":false,"isFastestThroughput":false,"isModelAuthor":false}],"showHuggingChatEntry":true,"inference":"warm","lastModified":"2025-07-26T03:49:13.000Z","likes":1366,"pipeline_tag":"text-generation","library_name":"transformers","librariesOther":[],"trackDownloads":true,"model-index":null,"evalResults":[{"dataset":{"id":"LiquidAI/ifstruct-v1.0","isBenchmark":true,"task_id":"ifstruct_v1"},"value":79.75,"source":{"url":"https://www.liquid.ai/blog/ifstruct-v1.0","name":"Liquid AI — IFStruct v1.0 blog (Qwen3-8B)","isExternal":true},"filename":".eval_results/Qwen-Qwen3-8B.yaml","verified":false,"pullRequest":39,"label":"Ifstruct V1"}],"private":false,"repoType":"model","gated":false,"tags":["transformers","safetensors","qwen3","text-generation","conversational","arxiv:2309.00071","arxiv:2505.09388","base_model:Qwen/Qwen3-8B-Base","base_model:finetune:Qwen/Qwen3-8B-Base","license:apache-2.0","eval-results","text-generation-inference","endpoints_compatible","deploy:sagemaker","deploy:azure","region:us"],"tag_objs":[{"id":"text-generation","label":"Text Generation","type":"pipeline_tag","subType":"nlp"},{"id":"transformers","label":"Transformers","type":"library"},{"id":"safetensors","label":"Safetensors","type":"library"},{"id":"qwen3","label":"qwen3","type":"other","clickable":true},{"id":"conversational","label":"conversational","type":"other","clickable":true},{"id":"base_model:Qwen/Qwen3-8B-Base","label":"base_model:Qwen/Qwen3-8B-Base","type":"other","clickable":true},{"id":"base_model:finetune:Qwen/Qwen3-8B-Base","label":"base_model:finetune:Qwen/Qwen3-8B-Base","type":"other","clickable":true},{"id":"eval-results","label":"Eval Results","type":"other","clickable":true},{"id":"text-generation-inference","label":"text-generation-inference","type":"other","clickable":true},{"id":"endpoints_compatible","label":"Inference Endpoints","type":"other","clickable":true},{"id":"arxiv:2309.00071","label":"arxiv:2309.00071","type":"arxiv","extra":{"paperTitle":"YaRN: Efficient Context Window Extension of Large Language Models"}},{"id":"arxiv:2505.09388","label":"arxiv:2505.09388","type":"arxiv","extra":{"paperTitle":"Qwen3 Technical Report"}},{"id":"license:apache-2.0","label":"apache-2.0","type":"license"},{"id":"deploy:sagemaker","label":"sagemaker","type":"deploy"},{"id":"deploy:azure","label":"azure","type":"deploy"},{"type":"region","label":"🇺🇸 Region: US","id":"region:us"}],"transformersInfo":{"auto_model":"AutoModelForCausalLM","pipeline_tag":"text-generation","processor":"AutoTokenizer"},"widgetData":[{"text":"Hi, what can you help me with?"},{"text":"What is 84 * 3 / 2?"},{"text":"Tell me an interesting fact about the universe!"},{"text":"Explain quantum computing in simple terms."}],"safetensors":{"parameters":{"BF16":8190735360},"total":8190735360,"sharded":true,"totalFileSize":16381549654},"hasBlockedOids":false,"region":"us","isQuantized":false,"licenseFilePath":"LICENSE"},"discussionsStats":{"closed":9,"open":31,"total":40},"query":{},"inferenceContextData":{"billableEntities":[],"entityName2Providers":{},"defaultProviders":[{"isOriginalProvider":false,"name":"nscale","enabled":true,"position":3,"isReleased":true,"accuratePricing":true},{"isOriginalProvider":false,"name":"featherless-ai","enabled":true,"position":7,"isReleased":true,"accuratePricing":true}]},"hasQuantizations":true,"copyToBucketNamespaces":[]}"><!--[-1--><!--]--> <header class="bg-linear-to-t border-b border-gray-100 pt-6 sm:pt-9 from-gray-50-to-white via-white dark:via-gray-950 "><!--[--><!--]--> <div class="container relative "><h1 class="flex flex-wrap items-center max-md:leading-tight mb-3 gap-y-1.5 text-lg md:text-xl"><!--[-1--><!--]--> <!--[0--><div class="group flex flex-none 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13.1172L28.8682 6.06729L11.3042 13.1172Z" fill="currentColor"></path></svg><!--]--><!--]--><!--]--> <!--[-1--><!--]--> <!--[-1--><span>Safetensors</span><!--]--> <!--[-1--><!--]--> <!--[--><!--]--></div><!----></a><!----><!--]--><!--]--><!--]--><!--]--><!--]--><!--[-1--><!--[--><!--[-1--><!--[0--><!--[-1--><!----><a class="mb-1 mr-1 md:mb-1.5 md:mr-1.5 rounded-lg" href="/models?other=qwen3"><div class="tag tag-white "><!--[-1--><!--]--> <!--[-1--><!--]--> <!--[-1--><span>qwen3</span><!--]--> <!--[-1--><!--]--> <!--[--><!--]--></div><!----></a><!----><!--]--><!--]--><!--]--><!--[-1--><!--[0--><!--[-1--><!----><a class="mb-1 mr-1 md:mb-1.5 md:mr-1.5 rounded-lg" href="/models?other=conversational"><div class="tag tag-white "><!--[-1--><!--]--> <!--[-1--><!--]--> <!--[-1--><span>conversational</span><!--]--> <!--[-1--><!--]--> <!--[--><!--]--></div><!----></a><!----><!--]--><!--]--><!--]--><!--[-1--><!--[0--><!--[-1--><!----><a class="mb-1 mr-1 md:mb-1.5 md:mr-1.5 rounded-lg" href="/models?other=eval-results"><div class="tag tag-white "><!--[8--><!--[--><svg class="text-orange-400" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M30 30h-8V4h8z" fill="currentColor"></path><path d="M20 30h-8V12h8z" fill="currentColor"></path><path d="M10 30H2V18h8z" fill="currentColor"></path></svg><!--]--><!--]--> <!--[-1--><!--]--> <!--[3--><span>Eval Results</span><!--]--> <!--[-1--><!--]--> <!--[--><!--]--></div><!----></a><!----><!--]--><!--]--><!--]--><!--[-1--><!--[0--><!--[-1--><!----><a class="mb-1 mr-1 md:mb-1.5 md:mr-1.5 rounded-lg" href="/models?other=text-generation-inference"><div class="tag tag-white "><!--[8--><!--[--><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 12 12"><path fill="#23B0FF" d="m9.6 3.6-3.2-2a1 1 0 0 0-1.1 0L2 3.7a1 1 0 0 0-.3 1.6H10a1 1 0 0 0-.3-1.6Z"></path><path fill="#2094FF" d="m6.7 9.7 3.2-4.5-.4-.8H5.7v4.8l1 .5Z"></path><path fill="#6BCAFF" d="M4.9 9.7 1.7 5.2l.4-.8h3.8v4.8l-1 .5Z"></path><path fill="#000" fill-rule="evenodd" d="M9.9 3.2c.8.5 1 1.5.5 2.3L7 10c-.6.9-2 .9-2.6 0L1.3 5.5c-.5-.8-.3-1.8.5-2.3l3.2-2c.5-.3 1.2-.3 1.7 0l3.2 2ZM6.4 5h3l-3 4.2V5ZM5.3 5h-3l3 4.2V5Zm3.8-1L6 2a.5.5 0 0 0-.5 0L2.6 4H9Z" clip-rule="evenodd"></path></svg><!--]--><!--]--> <!--[-1--><!--]--> <!--[-1--><span>text-generation-inference</span><!--]--> <!--[-1--><!--]--> <!--[--><!--]--></div><!----></a><!----><!--]--><!--]--><!--]--><!--]--><!--]--><!--[-1--><!--[--><!--[0--><div class="relative inline-block "><button class="group mr-1 mb-1 md:mr-1.5 md:mb-1.5 rounded-full rounded-br-none " type="button"><!--[0--><div><div class="tag rounded-full tag-white relative rounded-br-none pr-2.5"><!--[8--><!--[--><svg class="-mr-1 text-gray-400" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" viewBox="0 0 12 12" preserveAspectRatio="xMidYMid meet" fill="none"><path fill="currentColor" fill-rule="evenodd" d="M8.007 1.814a1.176 1.176 0 0 0-.732-.266H3.088c-.64 0-1.153.512-1.153 1.152v6.803c0 .64.513 1.152 1.153 1.152h5.54c.632 0 1.144-.511 1.144-1.152V3.816c0-.338-.137-.658-.412-.887L8.007 1.814Zm-1.875 1.81c0 .695.55 1.253 1.244 1.253h.983a.567.567 0 0 1 .553.585v4.041c0 .165-.119.302-.283.302h-5.55c-.156 0-.275-.137-.275-.302V2.7a.284.284 0 0 1 .284-.301h2.468a.574.574 0 0 1 .434.19.567.567 0 0 1 .142.395v.64Z" clip-rule="evenodd" fill-opacity=".8"></path><path fill="currentColor" fill-opacity=".2" fill-rule="evenodd" d="M6.132 3.624c0 .695.55 1.253 1.244 1.253h.97a.567.567 0 0 1 .566.585v4.041c0 .165-.119.302-.283.302h-5.55c-.156 0-.275-.137-.275-.302V2.7a.284.284 0 0 1 .284-.301h2.468a.567.567 0 0 1 .576.585v.64Z" clip-rule="evenodd"></path></svg><!--]--><!--]--> <!--[-1--><!--]--> <!--[1--><!--[0--><span class="-mr-2 text-gray-400">arxiv:</span><!--]--> <span>2309.00071</span><!--]--> <!--[-1--><!--]--> <!--[--><!--[0--><div class="border-br-gray-200 absolute bottom-0.5 right-0.5 h-1 w-1 border-[3px] border-l-transparent border-t-transparent border-b-gray-200 border-r-gray-200 group-hover:border-b-gray-400 group-hover:border-r-gray-400 dark:border-b-gray-700 dark:border-r-gray-700 group-hover:dark:border-b-gray-400 group-hover:dark:border-r-gray-400"></div><!--]--><!--]--></div><!----></div><!----><!--]--> <!--[-1--><!--]--></button> <!--[-1--><!--]--> <!--[-1--><!--]--></div><!--]--><!--[0--><div class="relative inline-block "><button class="group mr-1 mb-1 md:mr-1.5 md:mb-1.5 rounded-full rounded-br-none " type="button"><!--[0--><div><div class="tag rounded-full tag-white relative rounded-br-none pr-2.5"><!--[8--><!--[--><svg class="-mr-1 text-gray-400" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" viewBox="0 0 12 12" preserveAspectRatio="xMidYMid meet" fill="none"><path fill="currentColor" fill-rule="evenodd" d="M8.007 1.814a1.176 1.176 0 0 0-.732-.266H3.088c-.64 0-1.153.512-1.153 1.152v6.803c0 .64.513 1.152 1.153 1.152h5.54c.632 0 1.144-.511 1.144-1.152V3.816c0-.338-.137-.658-.412-.887L8.007 1.814Zm-1.875 1.81c0 .695.55 1.253 1.244 1.253h.983a.567.567 0 0 1 .553.585v4.041c0 .165-.119.302-.283.302h-5.55c-.156 0-.275-.137-.275-.302V2.7a.284.284 0 0 1 .284-.301h2.468a.574.574 0 0 1 .434.19.567.567 0 0 1 .142.395v.64Z" clip-rule="evenodd" fill-opacity=".8"></path><path fill="currentColor" fill-opacity=".2" fill-rule="evenodd" d="M6.132 3.624c0 .695.55 1.253 1.244 1.253h.97a.567.567 0 0 1 .566.585v4.041c0 .165-.119.302-.283.302h-5.55c-.156 0-.275-.137-.275-.302V2.7a.284.284 0 0 1 .284-.301h2.468a.567.567 0 0 1 .576.585v.64Z" clip-rule="evenodd"></path></svg><!--]--><!--]--> <!--[-1--><!--]--> <!--[1--><!--[0--><span class="-mr-2 text-gray-400">arxiv:</span><!--]--> <span>2505.09388</span><!--]--> <!--[-1--><!--]--> <!--[--><!--[0--><div class="border-br-gray-200 absolute bottom-0.5 right-0.5 h-1 w-1 border-[3px] border-l-transparent border-t-transparent border-b-gray-200 border-r-gray-200 group-hover:border-b-gray-400 group-hover:border-r-gray-400 dark:border-b-gray-700 dark:border-r-gray-700 group-hover:dark:border-b-gray-400 group-hover:dark:border-r-gray-400"></div><!--]--><!--]--></div><!----></div><!----><!--]--> <!--[-1--><!--]--></button> <!--[-1--><!--]--> <!--[-1--><!--]--></div><!--]--><!--]--><!--]--><!--[-1--><!--[--><!--[1--><div class="relative inline-block "><button class="group mr-1 mb-1 md:mr-1.5 md:mb-1.5 rounded-full rounded-br-none " type="button"><!--[0--><div><div class="tag rounded-full tag-white relative rounded-br-none pr-2.5"><!--[8--><!--[--><svg class="text-xs text-gray-900" width="1em" height="1em" viewBox="0 0 10 10" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M1.46009 5.0945V6.88125C1.46009 7.25201 1.75937 7.55129 2.13012 7.55129C2.50087 7.55129 2.80016 7.25201 2.80016 6.88125V5.0945C2.80016 4.72375 2.50087 4.42446 2.13012 4.42446C1.75937 4.42446 1.46009 4.72375 1.46009 5.0945ZM4.14022 5.0945V6.88125C4.14022 7.25201 4.4395 7.55129 4.81026 7.55129C5.18101 7.55129 5.48029 7.25201 5.48029 6.88125V5.0945C5.48029 4.72375 5.18101 4.42446 4.81026 4.42446C4.4395 4.42446 4.14022 4.72375 4.14022 5.0945ZM1.23674 9.78473H8.38377C8.75452 9.78473 9.0538 9.48545 9.0538 9.1147C9.0538 8.74395 8.75452 8.44466 8.38377 8.44466H1.23674C0.865993 8.44466 0.566711 8.74395 0.566711 9.1147C0.566711 9.48545 0.865993 9.78473 1.23674 9.78473ZM6.82036 5.0945V6.88125C6.82036 7.25201 7.11964 7.55129 7.49039 7.55129C7.86114 7.55129 8.16042 7.25201 8.16042 6.88125V5.0945C8.16042 4.72375 7.86114 4.42446 7.49039 4.42446C7.11964 4.42446 6.82036 4.72375 6.82036 5.0945ZM4.39484 0.623142L0.865993 2.48137C0.682851 2.57517 0.566711 2.76725 0.566711 2.97273C0.566711 3.28094 0.816857 3.53109 1.12507 3.53109H8.49991C8.80365 3.53109 9.0538 3.28094 9.0538 2.97273C9.0538 2.76725 8.93766 2.57517 8.75452 2.48137L5.22568 0.623142C4.9666 0.484669 4.65391 0.484669 4.39484 0.623142V0.623142Z" fill="currentColor"></path></svg><!--]--><!--]--> <!--[0--><span class="-mr-1 text-gray-400">License:</span><!--]--> <!--[-1--><span>apache-2.0</span><!--]--> <!--[-1--><!--]--> <!--[--><!--[0--><div class="border-br-gray-200 absolute bottom-0.5 right-0.5 h-1 w-1 border-[3px] border-l-transparent border-t-transparent border-b-gray-200 border-r-gray-200 group-hover:border-b-gray-400 group-hover:border-r-gray-400 dark:border-b-gray-700 dark:border-r-gray-700 group-hover:dark:border-b-gray-400 group-hover:dark:border-r-gray-400"></div><!--]--><!--]--></div><!----></div><!----><!--]--> <!--[-1--><!--]--></button> <!--[-1--><!--]--> 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Transformers:</p> <!--[--><!--[-1--><!--]--> <pre># Use a pipeline as a high-level helper107from transformers import pipeline108109pipe = pipeline("text-generation", model="Qwen/Qwen3-8B")110messages = [111 {"role": "user", "content": "Who are you?"},112]113pipe(messages)</pre><!--[-1--><!--]--> <pre># Load model directly114from transformers import AutoTokenizer, AutoModelForCausalLM115116tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")117model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B", device_map="auto")118messages = [119 {"role": "user", "content": "Who are you?"},120]121inputs = tokenizer.apply_chat_template(122 messages,123 add_generation_prompt=True,124 tokenize=True,125 return_dict=True,126 return_tensors="pt",127).to(model.device)128129outputs = model.generate(**inputs, max_new_tokens=40)130print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))</pre><!--]--><!--]--><!----></li><!--]--> <!--[0--><li class="w-56 px-3 py-1.5 text-sm 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0 1.407 1.153c.689 0 1.263-.49 1.407-1.153h1.176v-.576M5.954 2.305a.847.847 0 0 1-.861-.864c0-.49.373-.865.861-.865s.861.375.861.865-.373.864-.861.864M.5 5.764h1.177a1.445 1.445 0 0 0 1.406 1.152c.69 0 1.263-.49 1.407-1.152h4.047v-.577H4.49a1.445 1.445 0 0 0-1.407-1.152c-.688 0-1.263.49-1.406 1.152H.5v.577M3.083 4.61c.488 0 .862.375.862.864 0 .49-.374.865-.862.865a.847.847 0 0 1-.86-.865c0-.49.372-.864.86-.864"></path></svg><!----><span class="text-[0.6rem] leading-none">Settings</span></a></li><!--]--> <!--[--><!--[-1--><li><!----><a class="flex w-full cursor-pointer items-center whitespace-nowrap px-3 py-1.5 text-left leading-tight hover:bg-gray-50 dark:hover:bg-gray-800 pl-3 " href="/Qwen/Qwen3-8B?local-app=vllm"><!--[0--><!--[0--><!--[--><svg class="text-black mr-1.5 flex-none " xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 460 460"><path fill="#63A1FC" d="M196.952 394.188c-.976-.241-1.952-.482-3.039-1.382-.02-1.292.071-1.925.435-2.828 2.018-6.88 3.763-13.487 5.508-20.096 5.243-19.703 10.593-39.379 15.702-59.117 6.44-24.884 12.58-49.846 19.042-74.725 6.918-26.634 14.083-53.204 21.092-79.815 2.446-9.288 5.003-18.56 7.012-27.945 1.034-4.832 3.499-7.432 7.857-9.664 20.658-10.58 41.092-21.597 61.63-32.41 17.864-9.405 35.762-18.745 54.706-28.67-1.102 3.546-1.9 6.116-2.783 9.439-1.73 6.836-3.437 12.903-5.01 19.005-4.519 17.514-8.84 35.081-13.508 52.556-5.004 18.734-10.468 37.345-15.447 56.086-4.72 17.765-8.925 35.667-13.62 53.44-8.386 31.752-16.987 63.448-25.466 95.177-4.489 16.797-8.91 33.612-13.452 50.764-2.26 0-4.23-.004-6.2 0z"></path><path fill="#EBB432" d="M194.075 390.248a139 139 0 0 1-.331 2.214c-5.776-10.717-11.576-21.707-17.187-32.793-6.359-12.562-12.54-25.213-18.813-37.818-7.388-14.847-14.768-29.696-22.19-44.525-8.9-17.78-17.885-35.518-26.756-53.313-14.122-28.33-28.171-56.694-42.28-85.03-1.384-2.78-2.964-5.462-4.752-8.736 2.163-.132 3.585-.295 5.007-.295 40.662-.014 81.325.008 121.987-.05 3.117-.005 5.813-.059 5.094 5.157.006 84.33-.002 167.77.002 251.208 0 1.327.143 2.654.219 3.981"></path><path fill="#D7D8D9" d="M197.123 394.504c31.315-.378 62.802-.44 94.288-.5 1.97-.005 3.94-.001 6.2-.001 4.543-17.152 8.963-33.967 13.452-50.764 8.479-31.729 17.08-63.425 25.467-95.178 4.694-17.772 8.899-35.674 13.62-53.44 4.978-18.74 10.442-37.351 15.446-56.085 4.668-17.475 8.99-35.042 13.507-52.556 1.574-6.102 3.28-12.17 5.01-18.646 2.473-1.275 4.861-2.156 8.033-3.328-2.087 8.294-3.922 15.954-5.95 23.563-3.664 13.747-7.485 27.451-11.163 41.194-6.513 24.331-13.055 48.655-19.422 73.024-6.365 24.36-12.432 48.796-18.823 73.147-3.322 12.66-7.213 25.171-10.574 37.822-4.892 18.406-9.477 36.893-14.313 55.314-2.477 9.434-5.3 18.778-7.79 28.209-.796 3.017-2.461 3.779-5.397 3.77-31.831-.083-63.664-.152-95.494.07-4.283.03-4.791-2.612-6.097-5.615"></path><path fill="#D8D8D8" d="M194.348 389.978c-.35-1.057-.492-2.384-.492-3.71-.004-83.44.004-166.88.071-250.782 1.527-.384 2.994-.306 5.123-.192v6.588c0 73.977-.002 147.954.012 221.932 0 1.82.177 3.64.532 5.765-1.483 6.912-3.228 13.52-5.246 20.399"></path></svg><!--]--><!--]--><!----> <span class="truncate">vLLM</span> <!--[-1--><!--]--><!----><!--]--><!----></a><!----> <!--[0--><p>How to use Qwen/Qwen3-8B with vLLM:</p> <!--[--><!--[0--><h5>Install from pip and serve model</h5><!--]--> <pre># Install vLLM from pip:131pip install vllm132# Start the vLLM server:133vllm serve "Qwen/Qwen3-8B"134# Call the server using curl (OpenAI-compatible API):135curl -X POST "http://localhost:8000/v1/chat/completions" \136 -H "Content-Type: application/json" \137 --data '{138 "model": "Qwen/Qwen3-8B",139 "messages": [140 {141 "role": "user",142 "content": "What is the capital of France?"143 }144 ]145 }'</pre><!--[0--><h5>Use Docker</h5><!--]--> <pre>docker model run hf.co/Qwen/Qwen3-8B</pre><!--]--><!--]--><!----></li><!--]--><!--[-1--><li><!----><a class="flex w-full cursor-pointer items-center whitespace-nowrap px-3 py-1.5 text-left leading-tight hover:bg-gray-50 dark:hover:bg-gray-800 pl-3 " href="/Qwen/Qwen3-8B?local-app=sglang"><!--[0--><!--[0--><!--[--><svg class="text-black mr-1.5 flex-none " width="1em" height="1em" viewBox="0 0 12 12" fill="none" xmlns="http://www.w3.org/2000/svg" focusable="false" role="img" aria-hidden="true" preserveAspectRatio="xMidYMid meet"><path d="M8.648 3.182c0 1.402-1.656 2.805-3.312 2.805" stroke="#A5300F" stroke-width=".5" stroke-miterlimit="8"></path><path d="M4.031 5.12c1.08 0 2.158 1.61 2.158 3.221" stroke="#A5300F" stroke-width=".5" stroke-miterlimit="8"></path><path d="M3.048 6.238a1.322 1.322 0 1 0 0-2.644 1.322 1.322 0 0 0 0 2.644ZM8.955 4.163a1.322 1.322 0 1 0 0-2.643 1.322 1.322 0 0 0 0 2.643ZM7.317 8.34H5.053a.35.35 0 0 0-.35.35v1.399c0 .193.157.35.35.35h2.264a.35.35 0 0 0 .35-.35v-1.4a.35.35 0 0 0-.35-.35Z" fill="#FADDCD" stroke="#A5300F" stroke-width=".5"></path><path d="M6.392 8.88c.047.02.069.073.05.12l-.375.906a.092.092 0 1 1-.17-.07l.374-.906a.092.092 0 0 1 .12-.05Zm-.776.055a.093.093 0 0 1 .131 0 .093.093 0 0 1 0 .13l-.345.343.358.359a.093.093 0 0 1 0 .13.092.092 0 0 1-.13 0l-.424-.423a.092.092 0 0 1-.026-.066c0-.024.009-.048.026-.065l.41-.408Zm1 0a.093.093 0 0 1 .131 0l.41.408a.093.093 0 0 1 .027.065c0 .025-.01.048-.027.066l-.424.423a.092.092 0 0 1-.13 0 .093.093 0 0 1 0-.13l.358-.359-.344-.343a.093.093 0 0 1 0-.13Z" fill="#A5300F"></path></svg><!--]--><!--]--><!----> <span class="truncate">SGLang</span> <!--[-1--><!--]--><!----><!--]--><!----></a><!----> <!--[0--><p>How to use Qwen/Qwen3-8B with SGLang:</p> <!--[--><!--[0--><h5>Install from pip and serve model</h5><!--]--> <pre># Install SGLang from pip:146pip install sglang147# Start the SGLang server:148python3 -m sglang.launch_server \149 --model-path "Qwen/Qwen3-8B" \150 --host 0.0.0.0 \151 --port 30000152# Call the server using curl (OpenAI-compatible API):153curl -X POST "http://localhost:30000/v1/chat/completions" \154 -H "Content-Type: application/json" \155 --data '{156 "model": "Qwen/Qwen3-8B",157 "messages": [158 {159 "role": "user",160 "content": "What is the capital of France?"161 }162 ]163 }'</pre><!--[0--><h5>Use Docker images</h5><!--]--> <pre>docker run --gpus all \164 --shm-size 32g \165 -p 30000:30000 \166 -v ~/.cache/huggingface:/root/.cache/huggingface \167 --env "HF_TOKEN=<secret>" \168 --ipc=host \169 lmsysorg/sglang:latest \170 python3 -m sglang.launch_server \171 --model-path "Qwen/Qwen3-8B" \172 --host 0.0.0.0 \173 --port 30000174# Call the server using curl (OpenAI-compatible API):175curl -X POST "http://localhost:30000/v1/chat/completions" \176 -H "Content-Type: application/json" \177 --data '{178 "model": "Qwen/Qwen3-8B",179 "messages": [180 {181 "role": "user",182 "content": "What is the capital of France?"183 }184 ]185 }'</pre><!--]--><!--]--><!----></li><!--]--><!--]--> <!--[0--><!--[0--><!--[--><!--[-1--><li><!----><a class="flex w-full cursor-pointer items-center whitespace-nowrap px-3 py-1.5 text-left leading-tight hover:bg-gray-50 dark:hover:bg-gray-800 pl-3 " href="/Qwen/Qwen3-8B?local-app=docker-model-runner"><!--[0--><!--[0--><!--[--><svg class="text-black mr-1.5 flex-none text-[#1D63ED]!" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 16 12"><path d="M2.91013 5.125H1.50107C1.43433 5.125 1.38242 5.07308 1.375 4.99892L1.375 3.73806C1.375 3.66389 1.43433 3.61198 1.50107 3.61198L2.91013 3.61198C2.98429 3.61198 3.0362 3.67131 3.0362 3.73806V4.99892C3.0362 5.07308 2.97687 5.125 2.91013 5.125ZM4.85314 5.125L3.44409 5.125C3.37734 5.125 3.32543 5.07308 3.31801 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4.29008 15.8442 4.32736L16 4.4392L15.9332 4.63306C15.8219 4.90147 15.6661 5.14006 15.4583 5.33392C15.1466 5.64707 14.5826 6.01241 13.6699 6.01241L13.5215 6.01241C13.1579 6.95186 12.6607 8.01061 11.8221 8.95752C11.325 9.52417 10.7387 10.0088 10.0857 10.3891C9.29169 10.8439 8.43089 11.157 7.53298 11.3211C6.88738 11.4404 6.23436 11.5 5.58134 11.5C4.1343 11.5 2.85052 11.2241 2.06392 10.7395C1.36638 10.307 0.824667 9.59873 0.461052 8.64437C0.141962 7.77202 -0.0138728 6.84748 0.000968518 5.92294C0.000968494 5.64707 0.22359 5.42339 0.498155 5.42339L11.0356 5.42339C11.1691 5.41593 11.8073 5.36374 12.2006 5.14006C11.8741 4.61814 11.7776 4.00676 11.9335 3.35809C12.0151 3.02257 12.1561 2.69451 12.3416 2.40372L12.49 2.1875L12.7201 2.32171C12.7225 2.32369 12.729 2.32796 12.7389 2.33455C12.8788 2.42733 13.7104 2.97895 13.8628 4.04404C14.0855 4.00676 14.3155 3.98439 14.5381 3.98439Z" fill="currentColor"></path></svg><!--]--><!--]--><!----> <span class="truncate">Docker Model Runner</span> <!--[-1--><!--]--><!----><!--]--><!----></a><!----> <!--[0--><p>How to use Qwen/Qwen3-8B with Docker Model Runner:</p> <!--[--><!--[-1--><!--]--> <pre>docker model run hf.co/Qwen/Qwen3-8B</pre><!--]--><!--]--><!----></li><!--]--><!--]--><!--]--> <!--[-1--><!--]--><!--]--> <!--[0--><li class="w-56"><div class="mx-3 border-t border-gray-200 py-2 text-xs text-gray-600 dark:border-gray-700"><a class="underline decoration-gray-400 hover:decoration-gray-600 dark:hover:decoration-gray-300" href="/models?other=base_model:quantized:Qwen/Qwen3-8B"><svg class="size-3 inline-block -ml-0.5 mr-0.5 -translate-y-px" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 12 12"><path d="M1.5 8.25h9V2.253h.75v5.999h-.75V9h-3v1.5H9v.75H3v-.75h1.5V9H1.54v-.748H.75v-6h.75V8.25Zm3.75 2.25h1.5V9h-1.5v1.5Zm2.11-5.749h.786v.786H7.36v.782h-.785v.766H5.79v-.786h.785v-.766h.785v-.777h-.785V3.97h.786v.781ZM5.786 6.318H5v-.786h.786v.786Zm.783-2.351h-.783v.775h-.013v.005h-.768v.782h-.786v-.786H5v-.782h.783v-.78h.786v.786ZM10.5 2.25H1.512V1.5H10.5v.75Z" fill="currentColor"></path></svg><!---->Browse186 Quantizations</a> to use this model in <span class="whitespace-nowrap"><svg class="size-3 inline-block align-text-top translate-y-px" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 12 12"><path fill="currentColor" d="M9.11 9.94c-.26.4-.7.64-1.18.64H3.25l1.83-3.17h5.5zM5.08 7.4H1.42l3.05-5.28c.25-.43.71-.7 1.21-.7h2.86z"></path></svg><!----> llama.cpp</span>, <span class="whitespace-nowrap"><svg class="size-3 inline-block align-text-top translate-y-px" width="1em" height="1em" viewBox="0 0 646 854" fill="none" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" focusable="false" role="img" aria-hidden="true" preserveAspectRatio="xMidYMid meet"><path d="M140.629 0.239929C132.66 1.52725 123.097 5.69568 116.354 10.845C95.941 26.3541 80.1253 59.2728 73.4435 100.283C70.9302 115.792 69.2138 137.309 69.2138 153.738C69.2138 173.109 71.4819 197.874 74.7309 214.977C75.4665 218.778 75.8343 222.15 75.5278 222.395C75.2826 222.64 72.2788 225.092 68.9072 227.789C57.3827 236.984 44.2029 251.145 35.1304 264.08C17.7209 288.784 6.44151 316.86 1.72133 347.265C-0.117698 359.28 -0.608106 383.555 0.863118 395.57C4.11207 423.278 12.449 446.695 26.7321 468.151L31.391 475.078L30.0424 477.346C20.4794 493.407 12.3264 516.64 8.52575 538.953C5.522 556.608 5.15419 561.328 5.15419 584.99C5.15419 608.837 5.4607 613.557 8.28054 630.047C11.6521 649.786 18.5178 670.689 26.1804 684.605C28.6938 689.141 34.8239 698.581 35.5595 699.072C35.8047 699.194 35.0691 701.462 33.9044 704.098C25.077 723.408 17.537 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preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path fill="url(#icon-lm-studio-a)" d="M19.337 0H4.663A4.663 4.663 0 0 0 0 4.663v14.674A4.663 4.663 0 0 0 4.663 24h14.674A4.663 4.663 0 0 0 24 19.337V4.663A4.663 4.663 0 0 0 19.337 0"></path><g fill="#fff" opacity=".266"><path d="M15.803 4.35H7.418a1 1 0 0 0 0 2h8.385a1 1 0 0 0 0-2M19.928 7.063h-8.385a1 1 0 0 0 0 2h8.385a1 1 0 0 0 0-2M17.51 9.776H9.125a1 1 0 0 0 0 2h8.385a1 1 0 0 0 0-2M14.523 12.632H6.138a1 1 0 0 0 0 2h8.385a1 1 0 0 0 0-2M17.51 15.345H9.125a1 1 0 1 0 0 2h8.385a1 1 0 1 0 0-2M20.497 18.059h-4.829a1 1 0 1 0 0 1.999h4.829a1 1 0 1 0 0-2"></path></g><g fill="#fff" opacity=".845"><path d="M12.65 4.345H4.265a1 1 0 0 0 0 2h8.385a1 1 0 0 0 0-2M16.775 7.058H8.39a1 1 0 0 0 0 2h8.385a1 1 0 0 0 0-2M14.357 9.771H5.972a1 1 0 0 0 0 2h8.385a1 1 0 0 0 0-2M11.37 12.627H2.985a1 1 0 1 0 0 2h8.385a1 1 0 0 0 0-2M14.357 15.34H5.972a1 1 0 1 0 0 2h8.385a1 1 0 0 0 0-2M17.344 18.054h-4.828a1 1 0 1 0 0 1.999h4.828a1 1 0 1 0 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data-props="{}"><div></div><!----></div> <!--[-1--><!--]--> <div class="relative md:mt-2"><div class="SVELTE_HYDRATER contents" data-target="SideNavigation" data-props="{"titleTree":[{"id":"qwen3-8b","label":"Qwen3-8B","children":[{"id":"qwen3-highlights","label":"Qwen3 Highlights","children":[],"isValid":true,"title":"Qwen3 Highlights"},{"id":"model-overview","label":"Model Overview","children":[],"isValid":true,"title":"Model Overview"},{"id":"quickstart","label":"Quickstart","children":[],"isValid":true,"title":"Quickstart"},{"id":"switching-between-thinking-and-non-thinking-mode","label":"Switching Between Thinking and Non-Thinking Mode","children":[{"id":"enable_thinkingtrue","label":"<code>enable_thinking=True</code>","children":[],"isValid":true,"title":"<code>enable_thinking=True</code>"},{"id":"enable_thinkingfalse","label":"<code>enable_thinking=False</code>","children":[],"isValid":true,"title":"<code>enable_thinking=False</code>"},{"id":"advanced-usage-switching-between-thinking-and-non-thinking-modes-via-user-input","label":"Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input","children":[],"isValid":true,"title":"Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input"}],"isValid":true,"title":"Switching Between Thinking and Non-Thinking Mode"},{"id":"agentic-use","label":"Agentic Use","children":[],"isValid":true,"title":"Agentic Use"},{"id":"processing-long-texts","label":"Processing Long Texts","children":[],"isValid":true,"title":"Processing Long Texts"},{"id":"best-practices","label":"Best Practices","children":[{"id":"citation","label":"Citation","children":[],"isValid":true,"title":"Citation"}],"isValid":true,"title":"Best Practices"}],"isValid":true,"title":"Qwen3-8B"}]}"><!--[0--><div class="absolute -left-12 bottom-0 top-0 z-10 "><div class="sticky top-4 flex"><div class="h-7 pt-[0.175rem]"><span class="peer" tabindex="0"><button class="select-none text-gray-400 hover:cursor-pointer hover:text-gray-800 dark:text-gray-500 dark:hover:text-gray-400"><svg width="1em" height="1em" viewBox="0 0 10 10" class="text-lg" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" preserveAspectRatio="xMidYMid meet" fill="currentColor"><path fill-rule="evenodd" clip-rule="evenodd" d="M1.65039 2.9999C1.65039 2.8066 1.80709 2.6499 2.00039 2.6499H8.00039C8.19369 2.6499 8.35039 2.8066 8.35039 2.9999C8.35039 3.1932 8.19369 3.3499 8.00039 3.3499H2.00039C1.80709 3.3499 1.65039 3.1932 1.65039 2.9999ZM1.65039 4.9999C1.65039 4.8066 1.80709 4.6499 2.00039 4.6499H8.00039C8.19369 4.6499 8.35039 4.8066 8.35039 4.9999C8.35039 5.1932 8.19369 5.3499 8.00039 5.3499H2.00039C1.80709 5.3499 1.65039 5.1932 1.65039 4.9999ZM2.00039 6.6499C1.80709 6.6499 1.65039 6.8066 1.65039 6.9999C1.65039 7.1932 1.80709 7.3499 2.00039 7.3499H8.00039C8.19369 7.3499 8.35039 7.1932 8.35039 6.9999C8.35039 6.8066 8.19369 6.6499 8.00039 6.6499H2.00039Z"></path></svg><!----></button></span> <div class="invisible w-0 -translate-x-8 -translate-y-6 overflow-hidden rounded-xl border bg-white transition-transform hover:visible hover:w-52 hover:translate-x-0 peer-focus-within:visible peer-focus-within:w-52 peer-focus-within:translate-x-0"><nav aria-label="Secondary" class="max-h-[550px] overflow-y-auto p-3"><ul><!--[--><li class="mb-3 text-sm last:mb-0"><a class="mb-1 block break-words font-semibold text-gray-700 *:break-words hover:underline active:text-gray-900 dark:active:text-gray-200" href="#qwen3-8b" title="Qwen3-8B"><!---->Qwen3-8B<!----></a> <ul class="pl-1"><!--[--><li><a class="mb-0.5 block break-words hover:underline active:text-gray-700 dark:active:text-gray-300 text-gray-500" href="#qwen3-highlights" title="Qwen3 Highlights"><!---->Qwen3 Highlights<!----></a> <ul class="pl-2"><!--[--><!--]--></ul></li><li><a class="mb-0.5 block break-words hover:underline active:text-gray-700 dark:active:text-gray-300 text-gray-500" href="#model-overview" title="Model Overview"><!---->Model Overview<!----></a> <ul class="pl-2"><!--[--><!--]--></ul></li><li><a class="mb-0.5 block break-words hover:underline active:text-gray-700 dark:active:text-gray-300 text-gray-500" href="#quickstart" title="Quickstart"><!---->Quickstart<!----></a> <ul class="pl-2"><!--[--><!--]--></ul></li><li><a class="mb-0.5 block break-words hover:underline active:text-gray-700 dark:active:text-gray-300 text-gray-500" href="#switching-between-thinking-and-non-thinking-mode" title="Switching Between Thinking and Non-Thinking Mode"><!---->Switching Between Thinking and Non-Thinking Mode<!----></a> <ul class="pl-2"><!--[--><li><a class="mb-0.5 block break-words hover:underline active:text-gray-700 dark:active:text-gray-300 text-gray-500" href="#enable_thinkingtrue" title="<code>enable_thinking=True</code>"><!----><code>enable_thinking=True</code><!----></a></li><li><a class="mb-0.5 block break-words hover:underline active:text-gray-700 dark:active:text-gray-300 text-gray-500" href="#enable_thinkingfalse" title="<code>enable_thinking=False</code>"><!----><code>enable_thinking=False</code><!----></a></li><li><a class="mb-0.5 block break-words hover:underline active:text-gray-700 dark:active:text-gray-300 text-gray-500" href="#advanced-usage-switching-between-thinking-and-non-thinking-modes-via-user-input" title="Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input"><!---->Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input<!----></a></li><!--]--></ul></li><li><a class="mb-0.5 block break-words hover:underline active:text-gray-700 dark:active:text-gray-300 text-gray-500" href="#agentic-use" title="Agentic Use"><!---->Agentic Use<!----></a> <ul class="pl-2"><!--[--><!--]--></ul></li><li><a class="mb-0.5 block break-words hover:underline active:text-gray-700 dark:active:text-gray-300 text-gray-500" href="#processing-long-texts" title="Processing Long Texts"><!---->Processing Long Texts<!----></a> <ul class="pl-2"><!--[--><!--]--></ul></li><li><a class="mb-0.5 block break-words hover:underline active:text-gray-700 dark:active:text-gray-300 text-gray-500" href="#best-practices" title="Best Practices"><!---->Best Practices<!----></a> <ul class="pl-2"><!--[--><li><a class="mb-0.5 block break-words hover:underline active:text-gray-700 dark:active:text-gray-300 text-gray-500" href="#citation" title="Citation"><!---->Citation<!----></a></li><!--]--></ul></li><!--]--></ul></li><!--]--></ul></nav></div></div></div></div><!--]--><!----></div> <!--[0--><div class="SVELTE_HYDRATER contents" data-target="Hydrater" data-props="{"targetSelector":".model-card-content"}"><!----></div> <div class="model-card-content prose md:px-6 md:-mx-6 lg:-mr-20 lg:pr-20 xl:-mr-24 xl:pr-24 2xl:-mr-36 2xl:pr-36 hf-sanitized hf-sanitized-67sB52y16yGTh9Hiv-iDO copiable-code-container"><!--[-1--><!--]--> <!----><h1 class="relative group flex items-baseline">187 <a id="qwen3-8b" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" href="#qwen3-8b" rel="nofollow">188 <span class="header-link"><svg class="text-gray-500 hover:text-black dark:hover:text-gray-200 w-4" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span>189 </a>190 <span>191 Qwen3-8B192 </span>193</h1>194<a href="https://chat.qwen.ai/" style="margin: 2px;" rel="nofollow">195 <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;">196</a>197198<h2 class="relative group flex items-baseline">199 <a id="qwen3-highlights" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" href="#qwen3-highlights" rel="nofollow">200 <span class="header-link"><svg class="text-gray-500 hover:text-black dark:hover:text-gray-200 w-4" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span>201 </a>202 <span>203 Qwen3 Highlights204 </span>205</h2>206<p>Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:</p>207<ul>208<li><strong>Uniquely support of seamless switching between thinking mode</strong> (for complex logical reasoning, math, and coding) and <strong>non-thinking mode</strong> (for efficient, general-purpose dialogue) <strong>within single model</strong>, ensuring optimal performance across various scenarios.</li>209<li><strong>Significantly enhancement in its reasoning capabilities</strong>, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.</li>210<li><strong>Superior human preference alignment</strong>, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.</li>211<li><strong>Expertise in agent capabilities</strong>, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.</li>212<li><strong>Support of 100+ languages and dialects</strong> with strong capabilities for <strong>multilingual instruction following</strong> and <strong>translation</strong>.</li>213</ul>214<h2 class="relative group flex items-baseline">215 <a id="model-overview" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" href="#model-overview" rel="nofollow">216 <span class="header-link"><svg class="text-gray-500 hover:text-black dark:hover:text-gray-200 w-4" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span>217 </a>218 <span>219 Model Overview220 </span>221</h2>222<p><strong>Qwen3-8B</strong> has the following features:</p>223<ul>224<li>Type: Causal Language Models</li>225<li>Training Stage: Pretraining & Post-training</li>226<li>Number of Parameters: 8.2B</li>227<li>Number of Paramaters (Non-Embedding): 6.95B</li>228<li>Number of Layers: 36</li>229<li>Number of Attention Heads (GQA): 32 for Q and 8 for KV</li>230<li>Context Length: 32,768 natively and <a href="#processing-long-texts" rel="nofollow">131,072 tokens with YaRN</a>.</li>231</ul>232<p>For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our <a href="https://qwenlm.github.io/blog/qwen3/" rel="nofollow">blog</a>, <a href="https://github.com/QwenLM/Qwen3" rel="nofollow">GitHub</a>, and <a href="https://qwen.readthedocs.io/en/latest/" rel="nofollow">Documentation</a>.</p>233<h2 class="relative group flex items-baseline">234 <a id="quickstart" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" href="#quickstart" rel="nofollow">235 <span class="header-link"><svg class="text-gray-500 hover:text-black dark:hover:text-gray-200 w-4" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span>236 </a>237 <span>238 Quickstart239 </span>240</h2>241<p>The code of Qwen3 has been in the latest Hugging Face <code>transformers</code> and we advise you to use the latest version of <code>transformers</code>.</p>242<p>With <code>transformers<4.51.0</code>, you will encounter the following error:</p>243<pre><code>KeyError: 'qwen3'244</code></pre>245<p>The following contains a code snippet illustrating how to use the model generate content based on given inputs. </p>246<pre><code class="language-python"><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, AutoTokenizer247248model_name = <span class="hljs-string">"Qwen/Qwen3-8B"</span>249250<span class="hljs-comment"># load the tokenizer and the model</span>251tokenizer = AutoTokenizer.from_pretrained(model_name)252model = AutoModelForCausalLM.from_pretrained(253 model_name,254 torch_dtype=<span class="hljs-string">"auto"</span>,255 device_map=<span class="hljs-string">"auto"</span>256)257258<span class="hljs-comment"># prepare the model input</span>259prompt = <span class="hljs-string">"Give me a short introduction to large language model."</span>260messages = [261 {<span class="hljs-string">"role"</span>: <span class="hljs-string">"user"</span>, <span class="hljs-string">"content"</span>: prompt}262]263text = tokenizer.apply_chat_template(264 messages,265 tokenize=<span class="hljs-literal">False</span>,266 add_generation_prompt=<span class="hljs-literal">True</span>,267 enable_thinking=<span class="hljs-literal">True</span> <span class="hljs-comment"># Switches between thinking and non-thinking modes. Default is True.</span>268)269model_inputs = tokenizer([text], return_tensors=<span class="hljs-string">"pt"</span>).to(model.device)270271<span class="hljs-comment"># conduct text completion</span>272generated_ids = model.generate(273 **model_inputs,274 max_new_tokens=<span class="hljs-number">32768</span>275)276output_ids = generated_ids[<span class="hljs-number">0</span>][<span class="hljs-built_in">len</span>(model_inputs.input_ids[<span class="hljs-number">0</span>]):].tolist() 277278<span class="hljs-comment"># parsing thinking content</span>279<span class="hljs-keyword">try</span>:280 <span class="hljs-comment"># rindex finding 151668 (</think>)</span>281 index = <span class="hljs-built_in">len</span>(output_ids) - output_ids[::-<span class="hljs-number">1</span>].index(<span class="hljs-number">151668</span>)282<span class="hljs-keyword">except</span> ValueError:283 index = <span class="hljs-number">0</span>284285thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=<span class="hljs-literal">True</span>).strip(<span class="hljs-string">"\n"</span>)286content = tokenizer.decode(output_ids[index:], skip_special_tokens=<span class="hljs-literal">True</span>).strip(<span class="hljs-string">"\n"</span>)287288<span class="hljs-built_in">print</span>(<span class="hljs-string">"thinking content:"</span>, thinking_content)289<span class="hljs-built_in">print</span>(<span class="hljs-string">"content:"</span>, content)290</code></pre>291<p>For deployment, you can use <code>sglang>=0.4.6.post1</code> or <code>vllm>=0.8.5</code> or to create an OpenAI-compatible API endpoint:</p>292<ul>293<li>SGLang:<pre><code class="language-shell">python -m sglang.launch_server --model-path Qwen/Qwen3-8B --reasoning-parser qwen3294</code></pre>295</li>296<li>vLLM:<pre><code class="language-shell">vllm serve Qwen/Qwen3-8B --enable-reasoning --reasoning-parser deepseek_r1297</code></pre>298</li>299</ul>300<p>For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.</p>301<h2 class="relative group flex items-baseline">302 <a id="switching-between-thinking-and-non-thinking-mode" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" href="#switching-between-thinking-and-non-thinking-mode" rel="nofollow">303 <span class="header-link"><svg class="text-gray-500 hover:text-black dark:hover:text-gray-200 w-4" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span>304 </a>305 <span>306 Switching Between Thinking and Non-Thinking Mode307 </span>308</h2>309<blockquote class="tip">310<p>The <code>enable_thinking</code> switch is also available in APIs created by SGLang and vLLM. 311Please refer to our documentation for <a href="https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes" rel="nofollow">SGLang</a> and <a href="https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes" rel="nofollow">vLLM</a> users.</p>312</blockquote>313<h3 class="relative group flex items-baseline">314 <a id="enable_thinkingtrue" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" href="#enable_thinkingtrue" rel="nofollow">315 <span class="header-link"><svg class="text-gray-500 hover:text-black dark:hover:text-gray-200 w-4" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span>316 </a>317 <span>318 <code>enable_thinking=True</code>319 </span>320</h3>321<p>By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting <code>enable_thinking=True</code> or leaving it as the default value in <code>tokenizer.apply_chat_template</code>, the model will engage its thinking mode.</p>322<pre><code class="language-python">text = tokenizer.apply_chat_template(323 messages,324 tokenize=<span class="hljs-literal">False</span>,325 add_generation_prompt=<span class="hljs-literal">True</span>,326 enable_thinking=<span class="hljs-literal">True</span> <span class="hljs-comment"># True is the default value for enable_thinking</span>327)328</code></pre>329<p>In this mode, the model will generate think content wrapped in a <code><think>...</think></code> block, followed by the final response.</p>330<blockquote class="note">331<p>For thinking mode, use <code>Temperature=0.6</code>, <code>TopP=0.95</code>, <code>TopK=20</code>, and <code>MinP=0</code> (the default setting in <code>generation_config.json</code>). <strong>DO NOT use greedy decoding</strong>, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the <a href="#best-practices" rel="nofollow">Best Practices</a> section.</p>332</blockquote>333<h3 class="relative group flex items-baseline">334 <a id="enable_thinkingfalse" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" href="#enable_thinkingfalse" rel="nofollow">335 <span class="header-link"><svg class="text-gray-500 hover:text-black dark:hover:text-gray-200 w-4" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span>336 </a>337 <span>338 <code>enable_thinking=False</code>339 </span>340</h3>341<p>We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.</p>342<pre><code class="language-python">text = tokenizer.apply_chat_template(343 messages,344 tokenize=<span class="hljs-literal">False</span>,345 add_generation_prompt=<span class="hljs-literal">True</span>,346 enable_thinking=<span class="hljs-literal">False</span> <span class="hljs-comment"># Setting enable_thinking=False disables thinking mode</span>347)348</code></pre>349<p>In this mode, the model will not generate any think content and will not include a <code><think>...</think></code> block.</p>350<blockquote class="note">351<p>For non-thinking mode, we suggest using <code>Temperature=0.7</code>, <code>TopP=0.8</code>, <code>TopK=20</code>, and <code>MinP=0</code>. For more detailed guidance, please refer to the <a href="#best-practices" rel="nofollow">Best Practices</a> section.</p>352</blockquote>353<h3 class="relative group flex items-baseline">354 <a id="advanced-usage-switching-between-thinking-and-non-thinking-modes-via-user-input" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" href="#advanced-usage-switching-between-thinking-and-non-thinking-modes-via-user-input" rel="nofollow">355 <span class="header-link"><svg class="text-gray-500 hover:text-black dark:hover:text-gray-200 w-4" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span>356 </a>357 <span>358 Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input359 </span>360</h3>361<p>We provide a soft switch mechanism that allows users to dynamically control the model's behavior when <code>enable_thinking=True</code>. Specifically, you can add <code>/think</code> and <code>/no_think</code> to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.</p>362<p>Here is an example of a multi-turn conversation:</p>363<pre><code class="language-python"><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, AutoTokenizer364365<span class="hljs-keyword">class</span> <span class="hljs-title class_">QwenChatbot</span>:366 <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, model_name=<span class="hljs-string">"Qwen/Qwen3-8B"</span></span>):367 self.tokenizer = AutoTokenizer.from_pretrained(model_name)368 self.model = AutoModelForCausalLM.from_pretrained(model_name)369 self.history = []370371 <span class="hljs-keyword">def</span> <span class="hljs-title function_">generate_response</span>(<span class="hljs-params">self, user_input</span>):372 messages = self.history + [{<span class="hljs-string">"role"</span>: <span class="hljs-string">"user"</span>, <span class="hljs-string">"content"</span>: user_input}]373374 text = self.tokenizer.apply_chat_template(375 messages,376 tokenize=<span class="hljs-literal">False</span>,377 add_generation_prompt=<span class="hljs-literal">True</span>378 )379380 inputs = self.tokenizer(text, return_tensors=<span class="hljs-string">"pt"</span>)381 response_ids = self.model.generate(**inputs, max_new_tokens=<span class="hljs-number">32768</span>)[<span class="hljs-number">0</span>][<span class="hljs-built_in">len</span>(inputs.input_ids[<span class="hljs-number">0</span>]):].tolist()382 response = self.tokenizer.decode(response_ids, skip_special_tokens=<span class="hljs-literal">True</span>)383384 <span class="hljs-comment"># Update history</span>385 self.history.append({<span class="hljs-string">"role"</span>: <span class="hljs-string">"user"</span>, <span class="hljs-string">"content"</span>: user_input})386 self.history.append({<span class="hljs-string">"role"</span>: <span class="hljs-string">"assistant"</span>, <span class="hljs-string">"content"</span>: response})387388 <span class="hljs-keyword">return</span> response389390<span class="hljs-comment"># Example Usage</span>391<span class="hljs-keyword">if</span> __name__ == <span class="hljs-string">"__main__"</span>:392 chatbot = QwenChatbot()393394 <span class="hljs-comment"># First input (without /think or /no_think tags, thinking mode is enabled by default)</span>395 user_input_1 = <span class="hljs-string">"How many r's in strawberries?"</span>396 <span class="hljs-built_in">print</span>(<span class="hljs-string">f"User: <span class="hljs-subst">{user_input_1}</span>"</span>)397 response_1 = chatbot.generate_response(user_input_1)398 <span class="hljs-built_in">print</span>(<span class="hljs-string">f"Bot: <span class="hljs-subst">{response_1}</span>"</span>)399 <span class="hljs-built_in">print</span>(<span class="hljs-string">"----------------------"</span>)400401 <span class="hljs-comment"># Second input with /no_think</span>402 user_input_2 = <span class="hljs-string">"Then, how many r's in blueberries? /no_think"</span>403 <span class="hljs-built_in">print</span>(<span class="hljs-string">f"User: <span class="hljs-subst">{user_input_2}</span>"</span>)404 response_2 = chatbot.generate_response(user_input_2)405 <span class="hljs-built_in">print</span>(<span class="hljs-string">f"Bot: <span class="hljs-subst">{response_2}</span>"</span>) 406 <span class="hljs-built_in">print</span>(<span class="hljs-string">"----------------------"</span>)407408 <span class="hljs-comment"># Third input with /think</span>409 user_input_3 = <span class="hljs-string">"Really? /think"</span>410 <span class="hljs-built_in">print</span>(<span class="hljs-string">f"User: <span class="hljs-subst">{user_input_3}</span>"</span>)411 response_3 = chatbot.generate_response(user_input_3)412 <span class="hljs-built_in">print</span>(<span class="hljs-string">f"Bot: <span class="hljs-subst">{response_3}</span>"</span>)413</code></pre>414<blockquote class="note">415<p>For API compatibility, when <code>enable_thinking=True</code>, regardless of whether the user uses <code>/think</code> or <code>/no_think</code>, the model will always output a block wrapped in <code><think>...</think></code>. However, the content inside this block may be empty if thinking is disabled.416When <code>enable_thinking=False</code>, the soft switches are not valid. Regardless of any <code>/think</code> or <code>/no_think</code> tags input by the user, the model will not generate think content and will not include a <code><think>...</think></code> block.</p>417</blockquote>418<h2 class="relative group flex items-baseline">419 <a id="agentic-use" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" href="#agentic-use" rel="nofollow">420 <span class="header-link"><svg class="text-gray-500 hover:text-black dark:hover:text-gray-200 w-4" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span>421 </a>422 <span>423 Agentic Use424 </span>425</h2>426<p>Qwen3 excels in tool calling capabilities. We recommend using <a href="https://github.com/QwenLM/Qwen-Agent" rel="nofollow">Qwen-Agent</a> to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.</p>427<p>To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.</p>428<pre><code class="language-python"><span class="hljs-keyword">from</span> qwen_agent.agents <span class="hljs-keyword">import</span> Assistant429430<span class="hljs-comment"># Define LLM</span>431llm_cfg = {432 <span class="hljs-string">'model'</span>: <span class="hljs-string">'Qwen3-8B'</span>,433434 <span class="hljs-comment"># Use the endpoint provided by Alibaba Model Studio:</span>435 <span class="hljs-comment"># 'model_type': 'qwen_dashscope',</span>436 <span class="hljs-comment"># 'api_key': os.getenv('DASHSCOPE_API_KEY'),</span>437438 <span class="hljs-comment"># Use a custom endpoint compatible with OpenAI API:</span>439 <span class="hljs-string">'model_server'</span>: <span class="hljs-string">'http://localhost:8000/v1'</span>, <span class="hljs-comment"># api_base</span>440 <span class="hljs-string">'api_key'</span>: <span class="hljs-string">'EMPTY'</span>,441442 <span class="hljs-comment"># Other parameters:</span>443 <span class="hljs-comment"># 'generate_cfg': {</span>444 <span class="hljs-comment"># # Add: When the response content is `<think>this is the thought</think>this is the answer;</span>445 <span class="hljs-comment"># # Do not add: When the response has been separated by reasoning_content and content.</span>446 <span class="hljs-comment"># 'thought_in_content': True,</span>447 <span class="hljs-comment"># },</span>448}449450<span class="hljs-comment"># Define Tools</span>451tools = [452 {<span class="hljs-string">'mcpServers'</span>: { <span class="hljs-comment"># You can specify the MCP configuration file</span>453 <span class="hljs-string">'time'</span>: {454 <span class="hljs-string">'command'</span>: <span class="hljs-string">'uvx'</span>,455 <span class="hljs-string">'args'</span>: [<span class="hljs-string">'mcp-server-time'</span>, <span class="hljs-string">'--local-timezone=Asia/Shanghai'</span>]456 },457 <span class="hljs-string">"fetch"</span>: {458 <span class="hljs-string">"command"</span>: <span class="hljs-string">"uvx"</span>,459 <span class="hljs-string">"args"</span>: [<span class="hljs-string">"mcp-server-fetch"</span>]460 }461 }462 },463 <span class="hljs-string">'code_interpreter'</span>, <span class="hljs-comment"># Built-in tools</span>464]465466<span class="hljs-comment"># Define Agent</span>467bot = Assistant(llm=llm_cfg, function_list=tools)468469<span class="hljs-comment"># Streaming generation</span>470messages = [{<span class="hljs-string">'role'</span>: <span class="hljs-string">'user'</span>, <span class="hljs-string">'content'</span>: <span class="hljs-string">'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'</span>}]471<span class="hljs-keyword">for</span> responses <span class="hljs-keyword">in</span> bot.run(messages=messages):472 <span class="hljs-keyword">pass</span>473<span class="hljs-built_in">print</span>(responses)474</code></pre>475<h2 class="relative group flex items-baseline">476 <a id="processing-long-texts" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" href="#processing-long-texts" rel="nofollow">477 <span class="header-link"><svg class="text-gray-500 hover:text-black dark:hover:text-gray-200 w-4" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span>478 </a>479 <span>480 Processing Long Texts481 </span>482</h2>483<p>Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the <a href="https://arxiv.org/abs/2309.00071" rel="nofollow">YaRN</a> method.</p>484<p>YaRN is currently supported by several inference frameworks, e.g., <code>transformers</code> and <code>llama.cpp</code> for local use, <code>vllm</code> and <code>sglang</code> for deployment. In general, there are two approaches to enabling YaRN for supported frameworks:</p>485<ul>486<li><p>Modifying the model files:487In the <code>config.json</code> file, add the <code>rope_scaling</code> fields:</p>488<pre><code class="language-json"><span class="hljs-punctuation">{</span>489 ...<span class="hljs-punctuation">,</span>490 <span class="hljs-attr">"rope_scaling"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">{</span>491 <span class="hljs-attr">"rope_type"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"yarn"</span><span class="hljs-punctuation">,</span>492 <span class="hljs-attr">"factor"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">4.0</span><span class="hljs-punctuation">,</span>493 <span class="hljs-attr">"original_max_position_embeddings"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">32768</span>494 <span class="hljs-punctuation">}</span>495<span class="hljs-punctuation">}</span>496</code></pre>497<p>For <code>llama.cpp</code>, you need to regenerate the GGUF file after the modification.</p>498</li>499<li><p>Passing command line arguments:</p>500<p>For <code>vllm</code>, you can use</p>501<pre><code class="language-shell">vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072 502</code></pre>503<p>For <code>sglang</code>, you can use</p>504<pre><code class="language-shell">python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'505</code></pre>506<p>For <code>llama-server</code> from <code>llama.cpp</code>, you can use</p>507<pre><code class="language-shell">llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768508</code></pre>509</li>510</ul>511<blockquote class="important">512<p>If you encounter the following warning</p>513<pre><code>Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'}514</code></pre>515<p>please upgrade <code>transformers>=4.51.0</code>.</p>516</blockquote>517<blockquote class="note">518<p>All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, <strong>potentially impacting performance on shorter texts.</strong>519We advise adding the <code>rope_scaling</code> configuration only when processing long contexts is required. 520It is also recommended to modify the <code>factor</code> as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set <code>factor</code> as 2.0. </p>521</blockquote>522<blockquote class="note">523<p>The default <code>max_position_embeddings</code> in <code>config.json</code> is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance.</p>524</blockquote>525<blockquote class="tip">526<p>The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed.</p>527</blockquote>528<h2 class="relative group flex items-baseline">529 <a id="best-practices" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" href="#best-practices" rel="nofollow">530 <span class="header-link"><svg class="text-gray-500 hover:text-black dark:hover:text-gray-200 w-4" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span>531 </a>532 <span>533 Best Practices534 </span>535</h2>536<p>To achieve optimal performance, we recommend the following settings:</p>537<ol>538<li><p><strong>Sampling Parameters</strong>:</p>539<ul>540<li>For thinking mode (<code>enable_thinking=True</code>), use <code>Temperature=0.6</code>, <code>TopP=0.95</code>, <code>TopK=20</code>, and <code>MinP=0</code>. <strong>DO NOT use greedy decoding</strong>, as it can lead to performance degradation and endless repetitions.</li>541<li>For non-thinking mode (<code>enable_thinking=False</code>), we suggest using <code>Temperature=0.7</code>, <code>TopP=0.8</code>, <code>TopK=20</code>, and <code>MinP=0</code>.</li>542<li>For supported frameworks, you can adjust the <code>presence_penalty</code> parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.</li>543</ul>544</li>545<li><p><strong>Adequate Output Length</strong>: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.</p>546</li>547<li><p><strong>Standardize Output Format</strong>: We recommend using prompts to standardize model outputs when benchmarking.</p>548<ul>549<li><strong>Math Problems</strong>: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.</li>550<li><strong>Multiple-Choice Questions</strong>: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the <code>answer</code> field with only the choice letter, e.g., <code>"answer": "C"</code>."</li>551</ul>552</li>553<li><p><strong>No Thinking Content in History</strong>: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.</p>554</li>555</ol>556<h3 class="relative group flex items-baseline">557 <a id="citation" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" href="#citation" rel="nofollow">558 <span class="header-link"><svg class="text-gray-500 hover:text-black dark:hover:text-gray-200 w-4" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span>559 </a>560 <span>561 Citation562 </span>563</h3>564<p>If you find our work helpful, feel free to give us a cite.</p>565<pre><code>@misc{qwen3technicalreport,566 title={Qwen3 Technical Report}, 567 author={Qwen Team},568 year={2025},569 eprint={2505.09388},570 archivePrefix={arXiv},571 primaryClass={cs.CL},572 url={https://arxiv.org/abs/2505.09388}, 573}574</code></pre>575<!----></div> <!--[-1--><!--]--><!----><!--]--></div><!--]--><!--]--><!----></section><!----> <section class="pt-8 border-gray-100 md:col-span-5 pt-6 md:pb-24 md:pl-6 md:border-l order-first md:order-none"><!--[-1--><div class="flex justify-between pb-2"><dl><dt class="-mb-1 text-sm text-gray-500">Downloads last month</dt> <dd class="font-semibold">12,880,447</dd> <!--[-1--><!--]--></dl> <div 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function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n 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class="text-smd flex flex-col space-y-0 whitespace-nowrap pl-1 text-gray-800"><!--[-1--><!--[0--><!--[--><!--[0--><!--[2--><div class="flex h-[28px] w-full items-center justify-between gap-1.5"><p class="font-semibold">Base model</p> <div class="flex-1 translate-y-[2.5px] self-center border-b border-dotted dark:border-gray-800"></div> <a href="/Qwen/Qwen3-8B-Base" class="truncate font-mono text-xs underline decoration-gray-300 hover:decoration-gray-600 dark:decoration-gray-500 dark:hover:decoration-gray-200">Qwen/Qwen3-8B-Base</a></div><!--]--><!--]--><!--[-1--><div class="flex h-[28px] items-center justify-between gap-1.5" style="padding-left: 0px;"><div class="relative h-[28px] w-[19px] flex-none"><svg class="text-gray-300 dark:text-gray-700" width="15" height="28" viewBox="0 0 15 28" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M0.967742 0C0.967742 7.67991 7.21819 13.9655 15 13.9655V15C6.74768 15 0 8.3173 0 0H0.967742Z" 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fill-rule="evenodd" clip-rule="evenodd" d="M12.2621 7.17681V5.85298C12.261 5.6502 12.2077 5.45085 12.1075 5.27441L7.39301 8.00112V13.4431C7.56715 13.4419 7.73957 13.403 7.8971 13.3285L9.44011 12.4381C9.39138 12.3915 9.34927 12.338 9.31521 12.2791C9.25238 12.1704 9.21919 12.0471 9.21895 11.9215V9.23635C9.21955 9.10957 9.25281 8.98509 9.31553 8.87491L9.31524 8.87474C9.37573 8.77002 9.462 8.68251 9.56586 8.62054L11.9037 7.27079C12.0148 7.20808 12.1385 7.17675 12.2621 7.17681Z" fill="currentColor"></path><path opacity="0.25" d="M15.2083 8.87475L12.2617 10.5789L9.31519 8.87475C9.37567 8.77002 9.46195 8.68252 9.5658 8.62055L11.9037 7.2708C12.1257 7.14549 12.3978 7.14549 12.6197 7.2708L14.9576 8.62055C15.0615 8.68249 15.1477 8.7702 15.2083 8.87475Z" fill="currentColor"></path><path opacity="0.5" d="M12.2621 10.5789V13.9801C12.1492 13.9799 12.0378 13.9542 11.9363 13.9049L9.5769 12.5409C9.46822 12.478 9.37796 12.3877 9.31513 12.279C9.2523 12.1703 9.21911 12.047 9.21887 11.9215V9.2363C9.21946 9.10946 9.25276 8.98491 9.31554 8.87469L12.2621 10.5789Z" fill="currentColor"></path><path d="M15.3052 9.2363V11.9215C15.305 12.047 15.2718 12.1703 15.209 12.279C15.1461 12.3877 15.0559 12.478 14.9472 12.5409L12.5771 13.9085C12.4786 13.9551 12.3709 13.9794 12.262 13.9801V10.5789L15.2086 8.87469C15.2712 8.98497 15.3045 9.10956 15.3052 9.2363Z" fill="currentColor"></path></svg><!----> Finetuned<!--]--></div><!--]--> <!--[0--><span class="-ml-0.5 text-gray-600 dark:text-gray-400">(<a href="/models?other=base_model:finetune:Qwen/Qwen3-8B-Base" class="underline decoration-gray-300 hover:text-gray-900 hover:decoration-gray-600 dark:text-gray-300 dark:decoration-gray-500 dark:hover:text-gray-100 dark:hover:decoration-gray-200">560</a>)</span><!--]--> <div class="flex-1 translate-y-[2.5px] self-center border-b border-dotted dark:border-gray-800"></div><!--]--> <!--[0--><div class="flex items-center justify-center rounded-full bg-blue-500/10 px-2 text-blue-600 dark:text-blue-500" title="Qwen/Qwen3-8B">this model</div><!--]--><!----></div><!--]--><!--]--><!--]--> <!--[0--><div class="flex w-full flex-col justify-center space-y-0" style="padding-left: 24px;"><!--[--><!--[0--><div class="@container flex h-[28px] w-full items-center justify-between gap-1.5"><div class="relative h-[28px] w-[19px] flex-none"><div class="left absolute inset-y-0 -top-[3px] left-0 w-px bg-gray-300 dark:bg-gray-700"></div> <svg class="text-gray-300 dark:text-gray-700" width="19" height="28" viewBox="0 0 19 28" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M1 0C1 7.42391 7.4588 13.5 15.5 13.5V14.5C6.9726 14.5 0 8.04006 0 0H1Z" fill="currentColor"></path></svg></div> <div class="font-semibold"><!--[0-->Adapters<!--]--></div> <div class="flex-1 translate-y-[2.5px] self-center border-b border-dotted dark:border-gray-800"></div> <!--[-1--><!--]--> <a class="text-gray-700 underline decoration-gray-300 hover:text-gray-900 hover:decoration-gray-600 dark:text-gray-300 dark:decoration-gray-500 dark:hover:text-gray-100 dark:hover:decoration-gray-200" href="/models?other=base_model:adapter:Qwen/Qwen3-8B">2148 models</a></div><!--]--><!--[0--><div class="@container flex h-[28px] w-full items-center justify-between gap-1.5"><div class="relative h-[28px] w-[19px] flex-none"><div class="left absolute inset-y-0 -top-[3px] left-0 w-px bg-gray-300 dark:bg-gray-700"></div> <svg class="text-gray-300 dark:text-gray-700" width="19" height="28" viewBox="0 0 19 28" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M1 0C1 7.42391 7.4588 13.5 15.5 13.5V14.5C6.9726 14.5 0 8.04006 0 0H1Z" fill="currentColor"></path></svg></div> <div class="font-semibold"><!--[3-->Finetunes<!--]--></div> <div class="flex-1 translate-y-[2.5px] self-center border-b border-dotted dark:border-gray-800"></div> <!--[-1--><!--]--> <a class="text-gray-700 underline decoration-gray-300 hover:text-gray-900 hover:decoration-gray-600 dark:text-gray-300 dark:decoration-gray-500 dark:hover:text-gray-100 dark:hover:decoration-gray-200" href="/models?other=base_model:finetune:Qwen/Qwen3-8B">2071 models</a></div><!--]--><!--[0--><div class="@container flex h-[28px] w-full items-center justify-between gap-1.5"><div class="relative h-[28px] w-[19px] flex-none"><div class="left absolute inset-y-0 -top-[3px] left-0 w-px bg-gray-300 dark:bg-gray-700"></div> <svg class="text-gray-300 dark:text-gray-700" width="19" height="28" viewBox="0 0 19 28" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M1 0C1 7.42391 7.4588 13.5 15.5 13.5V14.5C6.9726 14.5 0 8.04006 0 0H1Z" fill="currentColor"></path></svg></div> <div class="font-semibold"><!--[1-->Merges<!--]--></div> <div class="flex-1 translate-y-[2.5px] self-center border-b border-dotted dark:border-gray-800"></div> <!--[-1--><!--]--> <a class="text-gray-700 underline decoration-gray-300 hover:text-gray-900 hover:decoration-gray-600 dark:text-gray-300 dark:decoration-gray-500 dark:hover:text-gray-100 dark:hover:decoration-gray-200" href="/models?other=base_model:merge:Qwen/Qwen3-8B">28 models</a></div><!--]--><!--[0--><div class="@container flex h-[28px] w-full items-center justify-between gap-1.5"><div class="relative h-[28px] w-[19px] flex-none"><div class="left absolute inset-y-0 -top-[3px] left-0 bottom-[28px] w-px bg-gray-300 dark:bg-gray-700"></div> <svg class="text-gray-300 dark:text-gray-700" width="19" height="28" viewBox="0 0 19 28" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M1 0C1 7.42391 7.4588 13.5 15.5 13.5V14.5C6.9726 14.5 0 8.04006 0 0H1Z" fill="currentColor"></path></svg></div> <div class="font-semibold"><!--[2-->Quantizations<!--]--></div> <div class="flex-1 translate-y-[2.5px] self-center border-b border-dotted dark:border-gray-800"></div> <!--[0--><div class="gap-0.75 flex items-center"><!--[--><a href="/models?apps=llama.cpp&other=base_model:quantized:Qwen/Qwen3-8B" title="Use with llama.cpp" class="@max-[280px]:last:hidden"><!--[0--><!--[--><svg class="text-black size-3 " xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 12 12"><path fill="currentColor" d="M9.11 9.94c-.26.4-.7.64-1.18.64H3.25l1.83-3.17h5.5zM5.08 7.4H1.42l3.05-5.28c.25-.43.71-.7 1.21-.7h2.86z"></path></svg><!--]--><!--]--><!----></a><a href="/models?apps=lmstudio&other=base_model:quantized:Qwen/Qwen3-8B" title="Use with LM Studio" class="@max-[280px]:last:hidden"><!--[0--><!--[--><svg class="text-black size-3 " xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path fill="url(#icon-lm-studio-a)" d="M19.337 0H4.663A4.663 4.663 0 0 0 0 4.663v14.674A4.663 4.663 0 0 0 4.663 24h14.674A4.663 4.663 0 0 0 24 19.337V4.663A4.663 4.663 0 0 0 19.337 0"></path><g fill="#fff" opacity=".266"><path d="M15.803 4.35H7.418a1 1 0 0 0 0 2h8.385a1 1 0 0 0 0-2M19.928 7.063h-8.385a1 1 0 0 0 0 2h8.385a1 1 0 0 0 0-2M17.51 9.776H9.125a1 1 0 0 0 0 2h8.385a1 1 0 0 0 0-2M14.523 12.632H6.138a1 1 0 0 0 0 2h8.385a1 1 0 0 0 0-2M17.51 15.345H9.125a1 1 0 1 0 0 2h8.385a1 1 0 1 0 0-2M20.497 18.059h-4.829a1 1 0 1 0 0 1.999h4.829a1 1 0 1 0 0-2"></path></g><g fill="#fff" opacity=".845"><path d="M12.65 4.345H4.265a1 1 0 0 0 0 2h8.385a1 1 0 0 0 0-2M16.775 7.058H8.39a1 1 0 0 0 0 2h8.385a1 1 0 0 0 0-2M14.357 9.771H5.972a1 1 0 0 0 0 2h8.385a1 1 0 0 0 0-2M11.37 12.627H2.985a1 1 0 1 0 0 2h8.385a1 1 0 0 0 0-2M14.357 15.34H5.972a1 1 0 1 0 0 2h8.385a1 1 0 0 0 0-2M17.344 18.054h-4.828a1 1 0 1 0 0 1.999h4.828a1 1 0 1 0 0-2"></path></g><defs><linearGradient id="icon-lm-studio-a" x1="78.731" x2="2229.6" y1="0" y2="2218.01" gradientUnits="userSpaceOnUse"><stop stop-color="#6E7EF3"></stop><stop offset="1" stop-color="#4F13BE"></stop></linearGradient></defs></svg><!--]--><!--]--><!----></a><a href="/models?apps=jan&other=base_model:quantized:Qwen/Qwen3-8B" title="Use with Jan" class="@max-[280px]:last:hidden"><!--[0--><!--[--><svg class="text-black size-3 " xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path fill="#FFCA28" d="M3.197 11.75c-.53-.76-.742-1.477-.224-1.91.438-.369 1.082-.439 1.879.48 0 0 2.379 2.765 3.198 3.533.186.172.417.199.628-.02.178-.185.113-.346-.038-.568 0 0-3.354-4.986-3.794-5.595-.734-1.018-.451-1.676-.112-1.988.472-.438 1.303-.526 2.074.532l4.061 5.436a.36.36 0 0 0 .469.047l.056-.04a.357.357 0 0 0 .103-.468c-.711-1.238-3.54-6.171-3.955-7.07-.478-1.037-.273-1.564.233-1.869.601-.362 1.166-.336 1.841.667.732 1.092 3.574 5.69 4.296 6.926a.358.358 0 0 0 .464.14l.008-.004c.16-.077.303-.235.237-.458-.394-1.3-2.33-5.35-2.736-6.281-.555-1.272-.284-1.705.29-1.997.603-.305 1.23-.064 1.657.785.289.577 5.349 9.785 5.349 9.785-.074-1.373.273-2.345.568-3.322.539-1.776 1.841-2.7 2.785-2.284.491.216.58.646.527.976-.106.64-.557 2.626-.644 4.18-.205 3.644.897 7.843-3.208 10.483-2.747 1.767-5.691 1.456-7.636.096-2.359-1.647-7.944-9.57-8.376-10.192Z"></path><path fill="#EDA600" d="M22.176 9.682c-.343 1.442-.388 3.213-.377 3.968.045 2.94.03 5.484-2.918 7.809-.362.286-1.498.984-2.819 1.348-.425.115-.264.252-.002.222 1.443-.167 2.537-.79 3.145-1.181 4.103-2.64 3.094-6.81 3.3-10.454.086-1.555.56-4 .553-4.211-.01-.21-.539 1.056-.882 2.499M14.564 9.833s-.294.002-.572-.386c-.836-1.162-3.458-5.551-4.213-6.628-.86-1.229-1.663-.759-1.845-.66 0 0 .66.023 1.024.563.743 1.102 3.56 6.038 4.45 7.098.574.685 1.156.013 1.156.013M4.171 10.207c.258.277 2.522 2.963 3.356 3.712.716.643 1.251-.176 1.251-.176s-.263.036-.624-.235c-.894-.677-3.014-3.09-3.492-3.599-.709-.754-1.369-.282-1.509-.197.002 0 .408-.157 1.018.495M11.542 11.48s-.242.177-.688-.344c-.324-.381-4.046-4.922-4.046-4.922-.933-1.14-1.666-.8-1.818-.713 0 0 .468-.024 1.016.607.25.288 4.222 5.352 4.382 5.525.582.635 1.12.089 1.154-.152"></path><path fill="#EDA600" d="M19.165 10.83S14.235 2.564 13.92 2c-.7-1.25-1.513-.867-1.652-.804 0 0 .453-.129.986.777.285.482 3.908 6.996 5.064 9.078.091.388.083.894-.209 1.22-1.046 1.174-2.38 2.372-1.988 5.343.098.753.377 1.465.584 1.809.261.43.563.267.43-.055a5.513 5.513 0 0 1-.26-.952c-.133-1.024-.58-2.814 1.426-4.96.34-.358 1.202-1.44.864-2.625"></path><path fill="#B0BEC5" d="M19.486 5.736a8.172 8.172 0 0 0-1.204-2.534 7.16 7.16 0 0 0-1.716-1.708c-.083-.06-.19-.241-.098-.418.09-.176.288-.153.362-.123.825.34 1.5.925 2.057 1.73.661.955.99 1.855 1.158 2.862.02.11.015.341-.22.402-.233.06-.313-.12-.34-.21M18.197 6.74a8.17 8.17 0 0 0-1.204-2.535 7.16 7.16 0 0 0-1.716-1.709c-.083-.058-.19-.24-.098-.417.09-.178.288-.153.362-.123.824.34 1.5.925 2.057 1.73a6.76 6.76 0 0 1 1.158 2.862c.019.11.015.341-.22.402-.231.06-.313-.118-.34-.21M1.489 14.552c.326.916.79 1.742 1.368 2.45a7.222 7.222 0 0 0 1.824 1.592c.087.053.205.228.125.41-.08.184-.276.172-.354.148-.846-.285-1.559-.825-2.167-1.591a6.776 6.776 0 0 1-1.346-2.78c-.027-.11-.038-.339.193-.415.231-.075.324.097.357.186M2.708 13.466c.326.916.79 1.742 1.368 2.45A7.22 7.22 0 0 0 5.9 17.507c.087.053.205.227.125.41-.08.183-.276.172-.354.147-.846-.284-1.559-.825-2.167-1.59a6.776 6.776 0 0 1-1.346-2.78c-.027-.11-.038-.34.193-.415.231-.078.324.097.357.186"></path></svg><!--]--><!--]--><!----></a><a href="/models?apps=ollama&other=base_model:quantized:Qwen/Qwen3-8B" title="Use with Ollama" class="@max-[280px]:last:hidden"><!--[0--><!--[--><svg class="text-black size-3 " width="1em" height="1em" viewBox="0 0 646 854" fill="none" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" focusable="false" role="img" aria-hidden="true" preserveAspectRatio="xMidYMid meet"><path d="M140.629 0.239929C132.66 1.52725 123.097 5.69568 116.354 10.845C95.941 26.3541 80.1253 59.2728 73.4435 100.283C70.9302 115.792 69.2138 137.309 69.2138 153.738C69.2138 173.109 71.4819 197.874 74.7309 214.977C75.4665 218.778 75.8343 222.15 75.5278 222.395C75.2826 222.64 72.2788 225.092 68.9072 227.789C57.3827 236.984 44.2029 251.145 35.1304 264.08C17.7209 288.784 6.44151 316.86 1.72133 347.265C-0.117698 359.28 -0.608106 383.555 0.863118 395.57C4.11207 423.278 12.449 446.695 26.7321 468.151L31.391 475.078L30.0424 477.346C20.4794 493.407 12.3264 516.64 8.52575 538.953C5.522 556.608 5.15419 561.328 5.15419 584.99C5.15419 608.837 5.4607 613.557 8.28054 630.047C11.6521 649.786 18.5178 670.689 26.1804 684.605C28.6938 689.141 34.8239 698.581 35.5595 699.072C35.8047 699.194 35.0691 701.462 33.9044 704.098C25.077 723.408 17.537 749.093 14.4106 770.733C12.2038 785.567 11.8973 790.349 11.8973 805.981C11.8973 825.903 13.0007 835.589 17.1692 851.466L17.7822 853.795H44.019H70.3172L68.6007 850.546C57.9957 830.93 57.0149 794.517 66.1487 758.166C70.3172 741.369 75.0374 729.048 83.8647 712.067L89.1366 701.769V695.455C89.1366 689.57 89.014 688.896 87.1137 685.034C85.6424 682.091 83.6808 679.578 80.1866 676.145C74.2404 670.383 69.9494 664.314 66.5165 656.835C51.4365 624.1 48.494 575.489 59.0991 534.049C63.5128 516.762 70.8076 501.376 78.4702 492.978C83.6808 487.215 86.378 480.779 86.378 474.097C86.378 467.17 83.926 461.469 78.4089 455.523C62.5932 438.604 52.8464 418.006 49.3522 394.038C44.3868 359.893 53.3981 322.683 73.8726 293.198C93.9181 264.263 122.055 245.689 153.503 240.724C160.552 239.559 173.732 239.743 181.088 241.092C189.119 242.502 194.145 242.072 199.295 239.62C205.67 236.617 208.858 232.877 212.597 224.295C215.907 216.633 218.482 212.464 225.409 203.821C233.746 193.461 241.776 186.411 254.649 177.89C269.362 168.266 286.097 161.278 302.771 157.906C308.839 156.68 311.659 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bg-linear-to-t! -z-1 from-gray-50! to-gray-50! shadow-none! dark:from-gray-925! dark:to-gray-925! -mt-[1.60rem] h-8 scale-x-[98%] transition-transform peer-hover:-translate-y-[2px] peer-hover:from-gray-100/80 dark:peer-hover:from-black"></div><!--]--></div><!--]--></div><!--]--> <!--[0--><div class="divider-column-vertical"></div> <h2 class="text-smd mb-5 flex items-baseline overflow-hidden whitespace-nowrap font-semibold text-gray-800"><svg class="mr-1 inline self-center flex-none text-gray-400" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" viewBox="0 0 12 12" preserveAspectRatio="xMidYMid meet" fill="none"><path fill="currentColor" fill-rule="evenodd" d="M8.007 1.814a1.176 1.176 0 0 0-.732-.266H3.088c-.64 0-1.153.512-1.153 1.152v6.803c0 .64.513 1.152 1.153 1.152h5.54c.632 0 1.144-.511 1.144-1.152V3.816c0-.338-.137-.658-.412-.887L8.007 1.814Zm-1.875 1.81c0 .695.55 1.253 1.244 1.253h.983a.567.567 0 0 1 .553.585v4.041c0 .165-.119.302-.283.302h-5.55c-.156 0-.275-.137-.275-.302V2.7a.284.284 0 0 1 .284-.301h2.468a.574.574 0 0 1 .434.19.567.567 0 0 1 .142.395v.64Z" clip-rule="evenodd" fill-opacity=".8"></path><path fill="currentColor" fill-opacity=".2" fill-rule="evenodd" d="M6.132 3.624c0 .695.55 1.253 1.244 1.253h.97a.567.567 0 0 1 .566.585v4.041c0 .165-.119.302-.283.302h-5.55c-.156 0-.275-.137-.275-.302V2.7a.284.284 0 0 1 .284-.301h2.468a.567.567 0 0 1 .576.585v.64Z" clip-rule="evenodd"></path></svg><!----> Papers for <span class="ml-1 truncate font-mono text-[0.86rem] font-medium">Qwen/Qwen3-8B</span></h2> <div class="flex flex-col gap-3"><!--[--><article class="overview-card-wrapper group/paper rounded-sm! "><a href="/papers/2505.09388" class="block p-2"><header class="flex items-center mb-1" title="arxiv:2505.09388"><!--[--><!--]--> <h4 class="truncate font-serif text-black dark:group-hover/paper:text-orange-500 text-smd">Qwen3 Technical Report</h4></header> <div class="mr-1 flex items-center overflow-hidden whitespace-nowrap text-sm leading-tight text-gray-400"><svg class="w-3.5 mr-1 flex-none text-gray-400 -translate-y-px" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" viewBox="0 0 12 12" preserveAspectRatio="xMidYMid meet" fill="none"><path fill="currentColor" fill-rule="evenodd" d="M8.007 1.814a1.176 1.176 0 0 0-.732-.266H3.088c-.64 0-1.153.512-1.153 1.152v6.803c0 .64.513 1.152 1.153 1.152h5.54c.632 0 1.144-.511 1.144-1.152V3.816c0-.338-.137-.658-.412-.887L8.007 1.814Zm-1.875 1.81c0 .695.55 1.253 1.244 1.253h.983a.567.567 0 0 1 .553.585v4.041c0 .165-.119.302-.283.302h-5.55c-.156 0-.275-.137-.275-.302V2.7a.284.284 0 0 1 .284-.301h2.468a.574.574 0 0 1 .434.19.567.567 0 0 1 .142.395v.64Z" clip-rule="evenodd" fill-opacity=".8"></path><path fill="currentColor" fill-opacity=".2" fill-rule="evenodd" d="M6.132 3.624c0 .695.55 1.253 1.244 1.253h.97a.567.567 0 0 1 .566.585v4.041c0 .165-.119.302-.283.302h-5.55c-.156 0-.275-.137-.275-.302V2.7a.284.284 0 0 1 .284-.301h2.468a.567.567 0 0 1 .576.585v.64Z" clip-rule="evenodd"></path></svg><!----> Paper <span class="px-1.5 text-gray-300">•</span> 2505.09388 <span class="px-1.5 text-gray-300">•</span> <span class="truncate">Published <time datetime="2025-05-14T13:41:34" title="Wed, 14 May 2025 13:41:34 GMT">May 14, 2025</time></span> <!--[0--><span class="px-1.5 text-gray-300">•</span> <!--[-1--><svg class="flex-none w-3 mr-1 text-gray-400" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 12 12" fill="transparent"><path d="M9.30013 9.29152H9.3H2.7H2.69987C2.62308 9.29154 2.54762 9.27146 2.481 9.23328C2.41437 9.1951 2.3589 9.14015 2.32009 9.07389C2.28128 9.00763 2.26048 8.93237 2.25977 8.85558C2.25907 8.7798 2.27796 8.70513 2.31458 8.63882L5.62238 2.9426L5.67518 2.85168C5.7059 2.81806 5.74178 2.78928 5.78164 2.76649C5.84813 2.72848 5.9234 2.70848 6 2.70848C6.0766 2.70848 6.15187 2.72848 6.21836 2.76649C6.28441 2.80425 6.33953 2.85848 6.37836 2.92389L9.68527 8.63855C9.72199 8.70493 9.74093 8.7797 9.74023 8.85558C9.73952 8.93237 9.71872 9.00763 9.67991 9.07389C9.6411 9.14015 9.58563 9.1951 9.519 9.23328C9.45238 9.27146 9.37692 9.29154 9.30013 9.29152Z" stroke="currentColor"></path></svg><!--]--> 346<!--]--> <!--[--><!--]--></div></a></article><article class="overview-card-wrapper group/paper rounded-sm! "><a href="/papers/2309.00071" class="block p-2"><header class="flex items-center mb-1" title="arxiv:2309.00071"><!--[--><!--]--> <h4 class="truncate font-serif text-black dark:group-hover/paper:text-orange-500 text-smd">YaRN: Efficient Context Window Extension of Large Language Models</h4></header> <div class="mr-1 flex items-center overflow-hidden whitespace-nowrap text-sm leading-tight text-gray-400"><svg class="w-3.5 mr-1 flex-none text-gray-400 -translate-y-px" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" viewBox="0 0 12 12" preserveAspectRatio="xMidYMid meet" fill="none"><path fill="currentColor" fill-rule="evenodd" d="M8.007 1.814a1.176 1.176 0 0 0-.732-.266H3.088c-.64 0-1.153.512-1.153 1.152v6.803c0 .64.513 1.152 1.153 1.152h5.54c.632 0 1.144-.511 1.144-1.152V3.816c0-.338-.137-.658-.412-.887L8.007 1.814Zm-1.875 1.81c0 .695.55 1.253 1.244 1.253h.983a.567.567 0 0 1 .553.585v4.041c0 .165-.119.302-.283.302h-5.55c-.156 0-.275-.137-.275-.302V2.7a.284.284 0 0 1 .284-.301h2.468a.574.574 0 0 1 .434.19.567.567 0 0 1 .142.395v.64Z" clip-rule="evenodd" fill-opacity=".8"></path><path fill="currentColor" fill-opacity=".2" fill-rule="evenodd" d="M6.132 3.624c0 .695.55 1.253 1.244 1.253h.97a.567.567 0 0 1 .566.585v4.041c0 .165-.119.302-.283.302h-5.55c-.156 0-.275-.137-.275-.302V2.7a.284.284 0 0 1 .284-.301h2.468a.567.567 0 0 1 .576.585v.64Z" clip-rule="evenodd"></path></svg><!----> Paper <span class="px-1.5 text-gray-300">•</span> 2309.00071 <span class="px-1.5 text-gray-300">•</span> <span class="truncate">Published <time datetime="2023-08-31T18:18:07" title="Thu, 31 Aug 2023 18:18:07 GMT">Aug 31, 2023</time></span> <!--[0--><span class="px-1.5 text-gray-300">•</span> <!--[-1--><svg class="flex-none w-3 mr-1 text-gray-400" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 12 12" fill="transparent"><path d="M9.30013 9.29152H9.3H2.7H2.69987C2.62308 9.29154 2.54762 9.27146 2.481 9.23328C2.41437 9.1951 2.3589 9.14015 2.32009 9.07389C2.28128 9.00763 2.26048 8.93237 2.25977 8.85558C2.25907 8.7798 2.27796 8.70513 2.31458 8.63882L5.62238 2.9426L5.67518 2.85168C5.7059 2.81806 5.74178 2.78928 5.78164 2.76649C5.84813 2.72848 5.9234 2.70848 6 2.70848C6.0766 2.70848 6.15187 2.72848 6.21836 2.76649C6.28441 2.80425 6.33953 2.85848 6.37836 2.92389L9.68527 8.63855C9.72199 8.70493 9.74093 8.7797 9.74023 8.85558C9.73952 8.93237 9.71872 9.00763 9.67991 9.07389C9.6411 9.14015 9.58563 9.1951 9.519 9.23328C9.45238 9.27146 9.37692 9.29154 9.30013 9.29152Z" stroke="currentColor"></path></svg><!--]--> 86<!--]--> <!--[--><!--]--></div></a></article><!--]--></div><!--]--> <!--[-1--><!--]--> <div class="SVELTE_HYDRATER contents" data-target="ModelEvalResults" data-props="{"model":{"author":"Qwen","cardData":{"library_name":"transformers","license":"apache-2.0","license_link":"https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE","pipeline_tag":"text-generation","base_model":["Qwen/Qwen3-8B-Base"]},"cardExists":true,"config":{"architectures":["Qwen3ForCausalLM"],"model_type":"qwen3","tokenizer_config":{"bos_token":null,"chat_template":"{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, 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{{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}","eos_token":"<|im_end|>","pad_token":"<|endoftext|>","unk_token":null}},"createdAt":"2025-04-27T03:42:21.000Z","discussionsDisabled":false,"discussionsSorting":"recently-created","downloads":12880447,"downloadsAllTime":120758624,"id":"Qwen/Qwen3-8B","isLikedByUser":false,"availableInferenceProviders":[{"provider":"nscale","modelStatus":"live","providerStatus":"live","providerId":"Qwen/Qwen3-8B","task":"conversational","adapterWeightsPath":"model-00001-of-00005.safetensors","features":{"structuredOutput":false,"toolCalling":true},"isCheapestPricingOutput":true,"isFastestThroughput":true,"isModelAuthor":false,"tokensPerSecond":131.38078276863246,"pricingOutput":0.18},{"provider":"featherless-ai","modelStatus":"live","providerStatus":"live","providerId":"Qwen/Qwen3-8B","task":"conversational","adapterWeightsPath":"model-00001-of-00005.safetensors","isCheapestPricingOutput":false,"isFastestThroughput":false,"isModelAuthor":false}],"showHuggingChatEntry":true,"inference":"warm","lastModified":"2025-07-26T03:49:13.000Z","likes":1366,"pipeline_tag":"text-generation","library_name":"transformers","librariesOther":[],"trackDownloads":true,"model-index":null,"evalResults":[{"dataset":{"id":"LiquidAI/ifstruct-v1.0","isBenchmark":true,"task_id":"ifstruct_v1"},"value":79.75,"source":{"url":"https://www.liquid.ai/blog/ifstruct-v1.0","name":"Liquid AI — IFStruct v1.0 blog (Qwen3-8B)","isExternal":true},"filename":".eval_results/Qwen-Qwen3-8B.yaml","verified":false,"pullRequest":39,"label":"Ifstruct V1"}],"private":false,"repoType":"model","gated":false,"tags":["transformers","safetensors","qwen3","text-generation","conversational","arxiv:2309.00071","arxiv:2505.09388","base_model:Qwen/Qwen3-8B-Base","base_model:finetune:Qwen/Qwen3-8B-Base","license:apache-2.0","eval-results","text-generation-inference","endpoints_compatible","deploy:sagemaker","deploy:azure","region:us"],"tag_objs":[{"id":"text-generation","label":"Text 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Models"}},{"id":"arxiv:2505.09388","label":"arxiv:2505.09388","type":"arxiv","extra":{"paperTitle":"Qwen3 Technical Report"}},{"id":"license:apache-2.0","label":"apache-2.0","type":"license"},{"id":"deploy:sagemaker","label":"sagemaker","type":"deploy"},{"id":"deploy:azure","label":"azure","type":"deploy"},{"type":"region","label":"🇺🇸 Region: US","id":"region:us"}],"transformersInfo":{"auto_model":"AutoModelForCausalLM","pipeline_tag":"text-generation","processor":"AutoTokenizer"},"widgetData":[{"text":"Hi, what can you help me with?"},{"text":"What is 84 * 3 / 2?"},{"text":"Tell me an interesting fact about the universe!"},{"text":"Explain quantum computing in simple terms."}],"safetensors":{"parameters":{"BF16":8190735360},"total":8190735360,"sharded":true,"totalFileSize":16381549654},"hasBlockedOids":false,"region":"us","isQuantized":false,"licenseFilePath":"LICENSE"}}"><!--[0--><div class="divider-column-vertical"></div> <h2 class="text-smd mb-5 flex items-center overflow-hidden whitespace-nowrap font-semibold text-gray-800"><svg class="mr-2 w-3 flex-none text-gray-300" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M30 30h-8V4h8z" fill="currentColor"></path><path d="M20 30h-8V12h8z" fill="currentColor"></path><path d="M10 30H2V18h8z" fill="currentColor"></path></svg><!----> Evaluation results <a target="_blank" href="https://huggingface.co/docs/hub/eval-results"><svg class="ml-1.5 text-sm text-gray-400 hover:text-black dark:hover:text-gray-200" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M17 22v-8h-4v2h2v6h-3v2h8v-2h-3z" fill="currentColor"></path><path d="M16 8a1.5 1.5 0 1 0 1.5 1.5A1.5 1.5 0 0 0 16 8z" fill="currentColor"></path><path d="M16 30a14 14 0 1 1 14-14a14 14 0 0 1-14 14zm0-26a12 12 0 1 0 12 12A12 12 0 0 0 16 4z" fill="currentColor"></path></svg><!----></a> <!--[-1--><!--]--></h2> <!--[0--><ul class="space-y-1.5 text-sm"><!--[--><!--[0--><li class="@container flex items-center gap-1.5 text-gray-800 hover:bg-gray-50/50 has-[a:hover]:bg-transparent dark:text-gray-200 dark:hover:bg-gray-900/50 dark:has-[a:hover]:bg-transparent"><span class="truncate text-gray-500 dark:text-gray-400"><!--[0--><a target="_blank" href="/datasets/LiquidAI/ifstruct-v1.0" class="whitespace-nowrap text-gray-800 hover:underline dark:text-gray-200">LiquidAI/ifstruct-v1.0</a> <!--[0--><span>·</span> <span class="capitalize">Ifstruct V1</span><!--]--><!--]--></span><!----> <a target="_blank" class="h-4.75 flex flex-none items-center justify-center rounded-sm bg-gray-100 px-0.5 text-xs text-gray-600 hover:bg-gray-200 dark:bg-gray-850 dark:text-gray-400 dark:hover:bg-gray-800" href="/Qwen/Qwen3-8B/discussions/39"><span class="sr-only">View evaluation results</span> <!--[0--><svg class="size-3" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path fill-rule="evenodd" clip-rule="evenodd" d="M5.99994 5.00003C5.44765 5.00003 4.99994 5.44775 4.99994 6.00003C4.99994 6.54969 5.44341 6.99577 5.99207 7C5.99471 6.99998 5.99736 6.99997 6 6.99997C6.00262 6.99997 6.00525 6.99998 6.00786 7C6.5565 6.99574 6.99994 6.54967 6.99994 6.00003C6.99994 5.44775 6.55222 5.00003 5.99994 5.00003ZM7 8.8293C8.16516 8.41745 8.99994 7.30623 8.99994 6.00003C8.99994 4.34318 7.65679 3.00003 5.99994 3.00003C4.34308 3.00003 2.99994 4.34318 2.99994 6.00003C2.99994 7.30627 3.83478 8.41753 5 8.82935V15.1707C3.83478 15.5825 2.99994 16.6938 2.99994 18C2.99994 19.6569 4.34308 21 5.99994 21C7.65679 21 8.99994 19.6569 8.99994 18C8.99994 16.6938 8.16516 15.5826 7 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