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| **Qwen2.5 (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb) | 2x faster | 60% less |
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| **Phi-4 (14B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb) | 2x faster | 50% less |
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# Qwen3-235B-A22B
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## Qwen3 Highlights
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| **Qwen2.5 (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb) | 2x faster | 60% less |
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| **Phi-4 (14B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb) | 2x faster | 50% less |
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# To Switch Between Thinking and Non-Thinking
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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.
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Here is an example of multi-turn conversation:
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```
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> Who are you /no_think
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<think>
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</think>
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I am Qwen, a large-scale language model developed by Alibaba Cloud. [...]
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> How many 'r's are in 'strawberries'? /think
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<think>
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Okay, let's see. The user is asking how many times the letter 'r' appears in the word "strawberries". [...]
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</think>
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The word strawberries contains 3 instances of the letter r. [...]
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```
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# Qwen3-235B-A22B
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## Qwen3 Highlights
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