China is rapidly advancing in AI, releasing models like DeepSeek and Qwen to rival global giants. DeepSeek has gained widespread recognition, comparable to ChatGPT, while Qwen is making strides with its versatile chatbot, offering vision, reasoning, and coding capabilities in one interface. QwQ 32B is Qwen\u2019s latest reasoning model. It is a medium-sized model, competes with top-tier reasoning models like DeepSeek-R1<\/a> and o1-mini<\/a>, showcasing China\u2019s impressive progress in AI innovation.<\/p>\n QwQ-32B is a 32-billion-parameter AI model from the Qwen series. It uses Reinforcement Learning (RL) to improve reasoning and problem-solving skills, performing as well as larger models like DeepSeek-R1. It can adapt its reasoning based on feedback and use tools effectively. The model is open-weight, available on Hugging Face and ModelScope under the Apache 2.0 license, and can be accessed through Qwen Chat. It highlights how RL can boost AI capabilities in meaningful ways.<\/p>\n Also Read: How to Run Qwen2.5 Models Locally in 3 Minutes?<\/a><\/p>\n QwQ-32B has been tested across various benchmarks to evaluate its mathematical reasoning, coding skills, and problem-solving abilities. The results below compare its performance with other top models, such as DeepSeek-R1-Distilled-Qwen-32B, DeepSeek-R1-Distilled-Llama-70B, o1-mini, and the original DeepSeek-R1.<\/p>\n The LiveBench scores, which evaluate reasoning models across a broad range of tasks, show QwQ-32B performing between R1 and o3-mini \u2013 but at just 1\/10th the cost. The pricing estimates are based on APIs or OpenRouter data, with QwQ-Preview priced at $0.18 per output token on DeepInfra. This makes QwQ-32B a highly efficient and cost-effective option compared to other leading models.<\/p>\n QwQ-32B by Alibaba scores 59% on GPQA Diamond for scientific reasoning and 86% on AIME 2024 for math. It excels in math but lags in scientific reasoning compared to top models.<\/p>\n It is also trending on #1 on HuggingFace. <\/p>\n Also Read: Qwen Chat: The AI Chatbot that\u2019s Better than ChatGPT<\/a><\/p>\n To access the QwQ-32B model, you have several options depending on your needs \u2013 whether you want to try it casually, run it locally, or integrate it into your projects. <\/p>\n Requirements:<\/strong><\/p>\n Install dependencies:<\/strong><\/p>\n Download the model and tokenizer from Hugging Face:<\/strong><\/p>\n Run a simple inference:<\/strong><\/p>\n If you want to run it locally, checkout my Collab notebook here<\/a>. <\/p>\n Prompt:<\/strong> Create a static webpage with illuminating candle with sparks around the flame<\/em><\/p>\n \nWhat is Qwen\u2019s QwQ 32B? <\/h2>\n
Performance <\/h2>\n
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How to Access QwQ 32B? <\/h2>\n
Via Qwen Chat (Easiest Option)<\/h3>\n
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Download and Run Locally via Hugging Face<\/h3>\n
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pip install transformers torch<\/code><\/pre>\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\nmodel_name = \"Qwen\/QwQ-32B\"\nmodel = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=\"auto\", device_map=\"auto\")\ntokenizer = AutoTokenizer.from_pretrained(model_name)<\/code><\/pre>\nprompt = \"How many r's are in the word 'strawberry'?\"\nmessages = [{\"role\": \"user\", \"content\": prompt}]\ntext = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\nmodel_inputs = tokenizer([text], return_tensors=\"pt\").to(model.device)\ngenerated_ids = model.generate(**model_inputs, max_new_tokens=512)\nresponse = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]\nprint(response)<\/code><\/pre>\nUsing Ollama for a Simpler Local Setup<\/h3>\n
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ollama pull qwq:32b<\/code><\/pre>\nollama run qwq:32b<\/code><\/pre>\n\n
Let\u2019s Try QwQ 32B<\/h2>\n