Qwen3-14B-GGUF

cmp-nct/Qwen3-14B-GGUF

Qwen3-14B-GGUF is a text generation model from cmp-nct. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.

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.gguf245 GB · 100%

At a glance

Task: Text Generation
Library: transformers
License: apache-2.0
Model type: qwen3
Access: Public
Created: Feb 16, 2026
Updated: May 30, 2026
SHA: 8476cd3b

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Qwen/Qwen3-14B

Model card

From the Hugging Face model README

<div style="background:#f3efe4;border:1px solid #161614;border-radius:18px;margin:0 0 28px 0;font-family:Inter,Segoe UI,Arial,sans-serif;color:#161614;overflow:hidden"> <div style="background:#161614;color:#f3efe4;padding:14px 22px;display:flex;flex-wrap:wrap;gap:10px;align-items:center;justify-content:space-between"> <div style="display:flex;align-items:center;gap:10px"> <div style="width:12px;height:12px;background:#9cff3b;border-radius:50%;box-shadow:0 0 0 4px rgba(156,255,59,.16)"></div> <div style="font-size:13px;font-weight:900;letter-spacing:1.4px;text-transform:uppercase">Demodokos Foundry</div> </div> <div style="display:flex;flex-wrap:wrap;gap:8px"> <a href="https://demodokos.com/#listen" style="color:#f3efe4;text-decoration:none;border:1px solid rgba(243,239,228,.28);border-radius:999px;padding:5px 10px;font-size:11px;font-weight:800;letter-spacing:.6px;text-transform:uppercase">Demos</a> <a href="https://demodokos.com/#create" style="color:#f3efe4;text-decoration:none;border:1px solid rgba(243,239,228,.28);border-radius:999px;padding:5px 10px;font-size:11px;font-weight:800;letter-spacing:.6px;text-transform:uppercase">Features</a> <a href="https://demodokos.com/#pricing" style="color:#f3efe4;text-decoration:none;border:1px solid rgba(243,239,228,.28);border-radius:999px;padding:5px 10px;font-size:11px;font-weight:800;letter-spacing:.6px;text-transform:uppercase">Pricing</a> <a href="https://demodokos.com/#begin" style="color:#f3efe4;text-decoration:none;border:1px solid rgba(243,239,228,.28);border-radius:999px;padding:5px 10px;font-size:11px;font-weight:800;letter-spacing:.6px;text-transform:uppercase">Download</a> </div> </div> <div style="padding:36px 30px 28px 30px"> <div style="font-size:12px;font-weight:950;letter-spacing:2px;text-transform:uppercase;color:#59614d;margin-bottom:13px">Local AI music, speech, editing, mixing, and automation</div> <h1 style="margin:0 0 16px 0;font-size:40px;line-height:1.04;font-weight:950;letter-spacing:-1.7px;color:#11110f">Your GPU is the studio.</h1> <p style="margin:0 0 22px 0;max-width:900px;font-size:16.5px;line-height:1.65;color:#3f4039">Generate music, create lifelike speech, clone voices, separate stems, repair sections, record, mix, master, and automate full audio workflows. Foundry runs locally on Windows with an NVIDIA GPU, so your scripts, voices, songs, and client audio stay on your machine.</p> <div style="display:flex;flex-wrap:wrap;gap:10px;margin:0 0 22px 0"> <a href="https://demodokos.com/#begin" style="background:#161614;color:#f3efe4;text-decoration:none;border-radius:10px;padding:11px 18px;font-size:13px;font-weight:950;letter-spacing:.2px;display:inline-block">Download for Windows</a> <a href="https://demodokos.com/" style="background:#9cff3b;color:#11110f;text-decoration:none;border-radius:10px;padding:11px 18px;font-size:13px;font-weight:950;letter-spacing:.2px;display:inline-block">Start free trial</a> <a href="https://demodokos.com/#listen" style="background:#fffaf0;color:#161614;text-decoration:none;border:1px solid #161614;border-radius:10px;padding:10px 17px;font-size:13px;font-weight:900;letter-spacing:.2px;display:inline-block">Hear examples</a> </div> <div style="display:flex;flex-wrap:wrap;gap:8px;color:#4d4f46;font-size:12px;font-weight:900;letter-spacing:.5px;text-transform:uppercase"> <span>No cloud generation</span> <span style="color:#a1a093">/</span> <span>No credit meters</span> <span style="color:#a1a093">/</span> <span>Voice cloning included</span> <span style="color:#a1a093">/</span> <span>Built for private production</span> </div> </div> <div style="padding:0 30px 28px 30px"> <div style="display:flex;flex-wrap:wrap;border:1px solid #161614;border-radius:14px;overflow:hidden;background:#fffaf0"> <div style="flex:1 1 145px;min-width:135px;padding:18px;border-right:1px solid #161614"> <div style="font-size:29px;font-weight:950;letter-spacing:-1px;color:#11110f">50+</div> <div style="font-size:12px;font-weight:900;text-transform:uppercase;letter-spacing:.8px;color:#5b5d52">Music languages</div> </div> <div style="flex:1 1 145px;min-width:135px;padding:18px;border-right:1px solid #161614"> <div style="font-size:29px;font-weight:950;letter-spacing:-1px;color:#11110f">10</div> <div style="font-size:12px;font-weight:900;text-transform:uppercase;letter-spacing:.8px;color:#5b5d52">Speech languages</div> </div> <div style="flex:1 1 145px;min-width:135px;padding:18px;border-right:1px solid #161614"> <div style="font-size:29px;font-weight:950;letter-spacing:-1px;color:#11110f">50 x 5</div> <div style="font-size:12px;font-weight:900;text-transform:uppercase;letter-spacing:.8px;color:#5b5d52">Emotion control</div> </div> <div style="flex:1 1 145px;min-width:135px;padding:18px;border-right:1px solid #161614"> <div style="font-size:29px;font-weight:950;letter-spacing:-1px;color:#11110f">200+</div> <div style="font-size:12px;font-weight:900;text-transform:uppercase;letter-spacing:.8px;color:#5b5d52">DSP presets</div> </div> <div style="flex:1 1 145px;min-width:135px;padding:18px"> <div style="font-size:29px;font-weight:950;letter-spacing:-1px;color:#11110f">120+</div> <div style="font-size:12px;font-weight:900;text-transform:uppercase;letter-spacing:.8px;color:#5b5d52">Commands</div> </div> </div> </div> <div style="padding:0 30px 30px 30px"> <div style="display:flex;flex-wrap:wrap;gap:14px"> <div style="flex:1 1 250px;background:#161614;color:#f3efe4;border-radius:14px;padding:22px;min-height:205px"> <div style="font-size:12px;font-weight:950;letter-spacing:1.5px;text-transform:uppercase;color:#9cff3b;margin-bottom:12px">Music</div> <h3 style="margin:0 0 10px 0;font-size:22px;line-height:1.15;font-weight:950;color:#ffffff">Songs, vocals, structure, style, and language control.</h3> <p style="margin:0 0 14px 0;color:#c8c3b8;font-size:14px;line-height:1.55">Create full tracks, extend ideas, transform references, patch weak sections, and generate multilingual songs locally.</p> <a href="https://demodokos.com/blog/best-ai-music-generators-2026" style="color:#9cff3b;text-decoration:none;font-size:13px;font-weight:900">Compare music generators</a> </div> <div style="flex:1 1 250px;background:#fffaf0;border:1px solid #161614;border-radius:14px;padding:22px;min-height:205px"> <div style="font-size:12px;font-weight:950;letter-spacing:1.5px;text-transform:uppercase;color:#59614d;margin-bottom:12px">Speech</div> <h3 style="margin:0 0 10px 0;font-size:22px;line-height:1.15;font-weight:950;color:#11110f">Expressive narration that does not sound like filler.</h3> <p style="margin:0 0 14px 0;color:#4b4c45;font-size:14px;line-height:1.55">Generate speech in 10 languages, direct emotion line by line, clone voices from short samples, and build multi-speaker scenes.</p> <a href="https://demodokos.com/local_ai_voice" style="color:#161614;text-decoration:none;font-size:13px;font-weight:950">Explore local AI voice</a> </div> <div style="flex:1 1 250px;background:#fffaf0;border:1px solid #161614;border-radius:14px;padding:22px;min-height:205px"> <div style="font-size:12px;font-weight:950;letter-spacing:1.5px;text-transform:uppercase;color:#59614d;margin-bottom:12px">Studio</div> <h3 style="margin:0 0 10px 0;font-size:22px;line-height:1.15;font-weight:950;color:#11110f">A real production workspace, not just a prompt field.</h3> <p style="margin:0 0 14px 0;color:#4b4c45;font-size:14px;line-height:1.55">Record, arrange, stem-split, patch, crossfade, process with DSP, mix on a DAW-style timeline, and export final audio.</p> <a href="https://demodokos.com/#create" style="color:#161614;text-decoration:none;font-size:13px;font-weight:950">See what is inside</a> </div> </div> </div> <div style="background:#dfe8d2;border-top:1px solid #161614;border-bottom:1px solid #161614;padding:26px 30px"> <div style="display:flex;flex-wrap:wrap;gap:18px;align-items:flex-start"> <div style="flex:1 1 280px"> <div style="font-size:12px;font-weight:950;letter-spacing:1.7px;text-transform:uppercase;color:#35402e;margin-bottom:10px">Local creative agent</div> <h3 style="margin:0 0 10px 0;font-size:24px;line-height:1.15;font-weight:950;color:#11110f">Give the boring preparation work to the assistant.</h3> <p style="margin:0;color:#3f4639;font-size:14px;line-height:1.6">Use the built-in agent for lyrics, music briefs, script preparation, speaker extraction, emotion planning, literature summaries, narration segmentation, batch workflows, and repeatable production tasks.</p> </div> <div style="flex:1 1 280px;background:#f3efe4;border:1px solid #161614;border-radius:14px;padding:18px"> <div style="font-family:Consolas,Monaco,monospace;font-size:13px;line-height:1.75;color:#161614"> <div><span style="color:#59614d">foundry</span> analyze manuscript.pdf</div> <div><span style="color:#59614d">foundry</span> extract speakers and emotions</div> <div><span style="color:#59614d">foundry</span> generate narration</div> <div><span style="color:#59614d">foundry</span> compose intro music</div> <div><span style="color:#59614d">foundry</span> mix and export</div> </div> </div> </div> </div> <div style="padding:28px 30px 12px 30px"> <div style="font-size:12px;font-weight:950;letter-spacing:1.7px;text-transform:uppercase;color:#59614d;margin-bottom:12px">Pick your workflow</div> <div style="display:flex;flex-wrap:wrap;gap:12px"> <a href="https://demodokos.com/for_youtubers" style="flex:1 1 220px;background:#fffaf0;border:1px solid #161614;border-radius:14px;padding:18px;text-decoration:none;color:#161614;display:block"> <div style="font-size:18px;font-weight:950;margin-bottom:7px">YouTubers and faceless channels</div> <div style="font-size:13px;line-height:1.5;color:#4b4c45">Voiceovers, hooks, background music, repeatable production, no per-video credit anxiety.</div> </a> <a href="https://demodokos.com/for_gamedevs" style="flex:1 1 220px;background:#fffaf0;border:1px solid #161614;border-radius:14px;padding:18px;text-decoration:none;color:#161614;display:block"> <div style="font-size:18px;font-weight:950;margin-bottom:7px">Game developers</div> <div style="font-size:13px;line-height:1.5;color:#4b4c45">NPC dialogue, character voices, batch generation, ambience, score, and engine-ready export.</div> </a> <a href="https://demodokos.com/for_audiobooks" style="flex:1 1 220px;background:#fffaf0;border:1px solid #161614;border-radius:14px;padding:18px;text-decoration:none;color:#161614;display:block"> <div style="font-size:18px;font-weight:950;margin-bottom:7px">Audiobooks and podcasts</div> <div style="font-size:13px;line-height:1.5;color:#4b4c45">Long-form narration, multi-speaker scenes, private manuscripts, music beds, and final export.</div> </a> </div> </div> <div style="padding:16px 30px 12px 30px"> <div style="font-size:12px;font-weight:950;letter-spacing:1.7px;text-transform:uppercase;color:#59614d;margin-bottom:12px">Speech studios by language</div> <div style="display:flex;flex-wrap:wrap;gap:8px"> <a href="https://demodokos.com/ai_audio_studio/english" style="background:#161614;color:#f3efe4;border-radius:999px;padding:8px 12px;font-size:12px;font-weight:900;text-decoration:none">English</a> <a href="https://demodokos.com/ai_audio_studio/deutsch" style="background:#161614;color:#f3efe4;border-radius:999px;padding:8px 12px;font-size:12px;font-weight:900;text-decoration:none">Deutsch</a> <a href="https://demodokos.com/ai_audio_studio/francais" style="background:#161614;color:#f3efe4;border-radius:999px;padding:8px 12px;font-size:12px;font-weight:900;text-decoration:none">Français</a> <a href="https://demodokos.com/ai_audio_studio/espanol" style="background:#161614;color:#f3efe4;border-radius:999px;padding:8px 12px;font-size:12px;font-weight:900;text-decoration:none">Español</a> <a href="https://demodokos.com/ai_audio_studio/italiano" style="background:#161614;color:#f3efe4;border-radius:999px;padding:8px 12px;font-size:12px;font-weight:900;text-decoration:none">Italiano</a> <a href="https://demodokos.com/ai_audio_studio/zhongwen" style="background:#161614;color:#f3efe4;border-radius:999px;padding:8px 12px;font-size:12px;font-weight:900;text-decoration:none">中文</a> <a href="https://demodokos.com/ai_audio_studio/nihongo" style="background:#161614;color:#f3efe4;border-radius:999px;padding:8px 12px;font-size:12px;font-weight:900;text-decoration:none">日本語</a> <a href="https://demodokos.com/ai_audio_studio/russkiy" style="background:#161614;color:#f3efe4;border-radius:999px;padding:8px 12px;font-size:12px;font-weight:900;text-decoration:none">Русский</a> <a href="https://demodokos.com/ai_audio_studio/portugues" style="background:#161614;color:#f3efe4;border-radius:999px;padding:8px 12px;font-size:12px;font-weight:900;text-decoration:none">Português</a> <a href="https://demodokos.com/ai_audio_studio/hangugeo" style="background:#161614;color:#f3efe4;border-radius:999px;padding:8px 12px;font-size:12px;font-weight:900;text-decoration:none">한국어</a> </div> </div> <div style="padding:16px 30px 30px 30px"> <div style="border-top:1px solid #161614;padding-top:20px;display:flex;flex-wrap:wrap;gap:18px;align-items:flex-start;justify-content:space-between"> <div style="flex:1 1 260px"> <div style="font-size:12px;font-weight:950;letter-spacing:1.7px;text-transform:uppercase;color:#59614d;margin-bottom:10px">Learn fast</div> <div style="display:flex;flex-wrap:wrap;gap:8px"> <a href="https://demodokos.com/tutorials/getting-started" style="background:#fffaf0;color:#161614;border:1px solid #161614;text-decoration:none;border-radius:10px;padding:8px 12px;font-size:12px;font-weight:900">Getting started</a> <a href="https://demodokos.com/tutorials/emotions-style-manager" style="background:#fffaf0;color:#161614;border:1px solid #161614;text-decoration:none;border-radius:10px;padding:8px 12px;font-size:12px;font-weight:900">Emotion manager</a> <a href="https://demodokos.com/tutorials/patching" style="background:#fffaf0;color:#161614;border:1px solid #161614;text-decoration:none;border-radius:10px;padding:8px 12px;font-size:12px;font-weight:900">Patching</a> <a href="https://demodokos.com/tutorials/creative-ai-agent" style="background:#fffaf0;color:#161614;border:1px solid #161614;text-decoration:none;border-radius:10px;padding:8px 12px;font-size:12px;font-weight:900">Creative AI agent</a> </div> </div> <div style="flex:1 1 260px"> <div style="font-size:12px;font-weight:950;letter-spacing:1.7px;text-transform:uppercase;color:#59614d;margin-bottom:10px">Deep dives</div> <div style="display:flex;flex-wrap:wrap;gap:8px"> <a href="https://demodokos.com/voice_cloning_software" style="background:#fffaf0;color:#161614;border:1px solid #161614;text-decoration:none;border-radius:10px;padding:8px 12px;font-size:12px;font-weight:900">Voice cloning</a> <a href="https://demodokos.com/elevenlabs_alternative" style="background:#fffaf0;color:#161614;border:1px solid #161614;text-decoration:none;border-radius:10px;padding:8px 12px;font-size:12px;font-weight:900">ElevenLabs alternative</a> <a href="https://demodokos.com/blog/creative-ai-and-automation" style="background:#fffaf0;color:#161614;border:1px solid #161614;text-decoration:none;border-radius:10px;padding:8px 12px;font-size:12px;font-weight:900">Automation</a> <a href="https://demodokos.com/privacy" style="background:#fffaf0;color:#161614;border:1px solid #161614;text-decoration:none;border-radius:10px;padding:8px 12px;font-size:12px;font-weight:900">Privacy</a> </div> </div> </div> </div> </div>

Model

This model is hosted for Demodokos Foundry but it can be used for other purposes, enjoy a stable download location and custom quantizations not available elsewhere.

To Switch Between Thinking and Non-Thinking

If you are using llama.cpp, Ollama, Open WebUI etc., you can add /think and /no_think 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.

Here is an example of multi-turn conversation:

> Who are you /no_think

<think>

</think>

I am Qwen, a large-scale language model developed by Alibaba Cloud. [...]

> How many 'r's are in 'strawberries'? /think

<think>
Okay, let's see. The user is asking how many times the letter 'r' appears in the word "strawberries". [...]
</think>

The word strawberries contains 3 instances of the letter r. [...]

Qwen3-14B

Qwen3 Highlights

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:

Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios.
Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
Superior human preference alignment, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
Expertise in agent capabilities, 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.
Support of 100+ languages and dialects with strong capabilities for multilingual instruction following and translation.

Model Overview

Qwen3-14B has the following features:

Type: Causal Language Models
Training Stage: Pretraining & Post-training
Number of Parameters: 14.8B
Number of Paramaters (Non-Embedding): 13.2B
Number of Layers: 40
Number of Attention Heads (GQA): 40 for Q and 8 for KV
Context Length: 32,768 natively and 131,072 tokens with YaRN.

For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation.

Quickstart

The code of Qwen3 has been in the latest Hugging Face transformers and we advise you to use the latest version of transformers.

With transformers<4.51.0, you will encounter the following error:

KeyError: 'qwen3'

The following contains a code snippet illustrating how to use the model generate content based on given inputs.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen3-14B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

# parsing thinking content
try:
    # rindex finding 151668 (</think>)
    index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
    index = 0

thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")

print("thinking content:", thinking_content)
print("content:", content)

For deployment, you can use vllm>=0.8.5 or sglang>=0.4.5.post2 to create an OpenAI-compatible API endpoint:

vLLM:

vllm serve Qwen/Qwen3-14B --enable-reasoning --reasoning-parser deepseek_r1

SGLang:

python -m sglang.launch_server --model-path Qwen/Qwen3-14B --reasoning-parser deepseek-r1

Switching Between Thinking and Non-Thinking Mode

[!TIP] The enable_thinking switch is also available in APIs created by vLLM and SGLang. Please refer to our documentation for more details.

`enable_thinking=True`

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 enable_thinking=True or leaving it as the default value in tokenizer.apply_chat_template, the model will engage its thinking mode.

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True  # True is the default value for enable_thinking
)

In this mode, the model will generate think content wrapped in a <think>...</think> block, followed by the final response.

[!NOTE] For thinking mode, use Temperature=0.6, TopP=0.95, TopK=20, and MinP=0 (the default setting in generation_config.json). DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the Best Practices section.

`enable_thinking=False`

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.

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False  # Setting enable_thinking=False disables thinking mode
)

In this mode, the model will not generate any think content and will not include a <think>...</think> block.

[!NOTE] For non-thinking mode, we suggest using Temperature=0.7, TopP=0.8, TopK=20, and MinP=0. For more detailed guidance, please refer to the Best Practices section.

Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input

We provide a soft switch mechanism that allows users to dynamically control the model's behavior when enable_thinking=True. Specifically, you can add /think and /no_think 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.

Here is an example of a multi-turn conversation:

from transformers import AutoModelForCausalLM, AutoTokenizer

class QwenChatbot:
    def __init__(self, model_name="Qwen/Qwen3-14B"):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForCausalLM.from_pretrained(model_name)
        self.history = []

    def generate_response(self, user_input):
        messages = self.history + [{"role": "user", "content": user_input}]

        text = self.tokenizer.apply_chat_template(
            messages,
            tokenize=False,
            add_generation_prompt=True
        )

        inputs = self.tokenizer(text, return_tensors="pt")
        response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist()
        response = self.tokenizer.decode(response_ids, skip_special_tokens=True)

        # Update history
        self.history.append({"role": "user", "content": user_input})
        self.history.append({"role": "assistant", "content": response})

        return response

# Example Usage
if __name__ == "__main__":
    chatbot = QwenChatbot()

    # First input (without /think or /no_think tags, thinking mode is enabled by default)
    user_input_1 = "How many r's in strawberries?"
    print(f"User: {user_input_1}")
    response_1 = chatbot.generate_response(user_input_1)
    print(f"Bot: {response_1}")
    print("----------------------")

    # Second input with /no_think
    user_input_2 = "Then, how many r's in blueberries? /no_think"
    print(f"User: {user_input_2}")
    response_2 = chatbot.generate_response(user_input_2)
    print(f"Bot: {response_2}") 
    print("----------------------")

    # Third input with /think
    user_input_3 = "Really? /think"
    print(f"User: {user_input_3}")
    response_3 = chatbot.generate_response(user_input_3)
    print(f"Bot: {response_3}")

Note For API compatibility, when enable_thinking=True, regardless of whether the user uses /think or /no_think, the model will always output a block wrapped in <think>...</think>. However, the content inside this block may be empty if thinking is disabled. When enable_thinking=False, the soft switches are not valid. Regardless of any /think or /no_think tags input by the user, the model will not generate think content and will not include a <think>...</think> block.

Agentic Use

Qwen3 excels in tool calling capabilities. We recommend using Qwen-Agent 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.

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.

from qwen_agent.agents import Assistant

# Define LLM
llm_cfg = {
    'model': 'Qwen3-14B',

    # Use the endpoint provided by Alibaba Model Studio:
    # 'model_type': 'qwen_dashscope',
    # 'api_key': os.getenv('DASHSCOPE_API_KEY'),

    # Use a custom endpoint compatible with OpenAI API:
    'model_server': 'http://localhost:8000/v1',  # api_base
    'api_key': 'EMPTY',

    # Other parameters:
    # 'generate_cfg': {
    #         # Add: When the response content is `<think>this is the thought</think>this is the answer;
    #         # Do not add: When the response has been separated by reasoning_content and content.
    #         'thought_in_content': True,
    #     },
}

# Define Tools
tools = [
    {'mcpServers': {  # You can specify the MCP configuration file
            'time': {
                'command': 'uvx',
                'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
            },
            "fetch": {
                "command": "uvx",
                "args": ["mcp-server-fetch"]
            }
        }
    },
  'code_interpreter',  # Built-in tools
]

# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)

# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
    pass
print(responses)

Processing Long Texts

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 YaRN method.

YaRN is currently supported by several inference frameworks, e.g., transformers and llama.cpp for local use, vllm and sglang for deployment. In general, there are two approaches to enabling YaRN for supported frameworks:

Modifying the model files: In the config.json file, add the rope_scaling fields:
```
{
    ...,
    "rope_scaling": {
        "type": "yarn",
        "factor": 4.0,
        "original_max_position_embeddings": 32768
    }
}
```
For llama.cpp, you need to regenerate the GGUF file after the modification.

Passing command line arguments:

For vllm, you can use

vllm serve ... --rope-scaling '{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072

For sglang, you can use

python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'

For llama-server from llama.cpp, you can use

llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768

[!IMPORTANT] If you encounter the following warning
Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'}
please upgrade transformers>=4.51.0.

[!NOTE] All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise adding the rope_scaling configuration only when processing long contexts is required. It is also recommended to modify the factor as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set factor as 2.0.

[!NOTE] The default max_position_embeddings in config.json 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.

[!TIP] The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed.

Best Practices

To achieve optimal performance, we recommend the following settings:

Sampling Parameters:
- For thinking mode (enable_thinking=True), use Temperature=0.6, TopP=0.95, TopK=20, and MinP=0. DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions.
- For non-thinking mode (enable_thinking=False), we suggest using Temperature=0.7, TopP=0.8, TopK=20, and MinP=0.
- For supported frameworks, you can adjust the presence_penalty 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.
Adequate Output Length: 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.
Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking.
- Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the answer field with only the choice letter, e.g., "answer": "C"."
No Thinking Content in History: 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.

Citation

If you find our work helpful, feel free to give us a cite.

@misc{qwen3,
    title  = {Qwen3},
    url    = {https://qwenlm.github.io/blog/qwen3/},
    author = {Qwen Team},
    month  = {April},
    year   = {2025}
}

Task: Text Generation
Library: transformers
Type: qwen3
License: apache-2.0
Languages: en
Created: Feb 16, 2026
Updated: May 30, 2026