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                <strong>This 4-bit model currently only works with Unsloth! <br> See <a href="https://huggingface.co/collections/unsloth/llama-4-67f19503d764b0f3a2a868d2">our collection</a> for versions of Llama 4 including 4-bit & 16-bit formats.</strong>
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            The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. 
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                <strong>This <a href="https://unsloth.ai/blog/dynamic-4bit">Dynamic 4-bit</a> model currently only works with Unsloth! <br> See <a href="https://huggingface.co/collections/unsloth/llama-4-67f19503d764b0f3a2a868d2">our collection</a> for versions of Llama 4 including 4-bit & 16-bit formats.</strong>
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                <em><a href="https://docs.unsloth.ai/basics/tutorials-how-to-fine-tune-and-run-llms">Read our Guide</a> to see how to Fine-tune & Run Llama 4 correctly.</em>
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                <a href="https://github.com/unslothai/unsloth/">
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                  <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
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                </a>
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                <a href="https://discord.gg/unsloth">
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                  <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
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                </a>
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                <a href="https://docs.unsloth.ai/basics/tutorials-how-to-fine-tune-and-run-llms">
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            <h1 style="margin-top: 0rem;">✨ Fine-tune Llama 4 with Unsloth!</h1>
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            - Fine-tune Llama-4-Scout on a single H100 80GB GPU using Unsloth!
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            - Read our Blog about Llama 4 support: [unsloth.ai/blog/llama4](https://unsloth.ai/blog/llama4)
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            - View the rest of our notebooks in our [docs here](https://docs.unsloth.ai/get-started/unsloth-notebooks).
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            - Export your fine-tuned model to GGUF, Ollama, llama.cpp, vLLM or 🤗HF.
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            | Unsloth supports          |    Free Notebooks                                                                                           | Performance | Memory use |
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            |-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
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            | **GRPO with Llama 3.1 (8B)**      | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)               | 2x faster | 80% less |
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            | **Llama-3.2 (3B)**      | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb)               | 2.4x faster | 58% less |
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            | **Llama-3.2 (11B vision)**      | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)               | 2x faster | 60% less |
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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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            | **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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            <br>
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            # Llama 4 model details
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            The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. 
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