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@@ -14,7 +14,7 @@ tags:
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  <strong>This is Qwen-QwQ-32B with our bug fixes. <br> See <a href="https://huggingface.co/collections/unsloth/qwen-qwq-32b-collection-676b3b29c20c09a8c71a6235">our collection</a> for versions of QwQ-32B with our bug fixes including GGUF & 4-bit formats.</strong>
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  </p>
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  <p style="margin-bottom: 0;">
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- <em>Unsloth's QwQ-32B<a href="https://unsloth.ai/blog/dynamic-4bit">Dynamic Quants</a> is selectively quantized, greatly improving accuracy over standard 4-bit.</em>
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  </p>
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  <div style="display: flex; gap: 5px; align-items: center; ">
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  <a href="https://github.com/unslothai/unsloth/">
@@ -49,8 +49,6 @@ All notebooks are **beginner friendly**! Add your dataset, click "Run All", and
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  | **Gemma 2 (9B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_(9B)-Alpaca.ipynb) | 2.4x faster | 58% less |
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  | **Mistral (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-Conversational.ipynb) | 2.2x faster | 62% less |
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- [<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="200"/>](https://docs.unsloth.ai)
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-
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  - This [Llama 3.2 conversational notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) is useful for ShareGPT ChatML / Vicuna templates.
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  - This [text completion notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_(7B)-Text_Completion.ipynb) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
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  - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
 
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  <strong>This is Qwen-QwQ-32B with our bug fixes. <br> See <a href="https://huggingface.co/collections/unsloth/qwen-qwq-32b-collection-676b3b29c20c09a8c71a6235">our collection</a> for versions of QwQ-32B with our bug fixes including GGUF & 4-bit formats.</strong>
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  </p>
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  <p style="margin-bottom: 0;">
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+ <em>Unsloth's QwQ-32B <a href="https://unsloth.ai/blog/dynamic-4bit">Dynamic Quants</a> is selectively quantized, greatly improving accuracy over standard 4-bit.</em>
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  </p>
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  <div style="display: flex; gap: 5px; align-items: center; ">
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  <a href="https://github.com/unslothai/unsloth/">
 
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  | **Gemma 2 (9B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_(9B)-Alpaca.ipynb) | 2.4x faster | 58% less |
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  | **Mistral (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-Conversational.ipynb) | 2.2x faster | 62% less |
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  - This [Llama 3.2 conversational notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) is useful for ShareGPT ChatML / Vicuna templates.
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  - This [text completion notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_(7B)-Text_Completion.ipynb) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
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  - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.