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README.md
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## 🚀 Resources
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- **Source Code**: [GitHub Repository](https://github.com/while-basic/mindcraft) #todo: add mindcraft repo
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- **Colab Notebook**: [
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## Overview
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This
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1. Install and set up the **Unsloth framework**.
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2. Initialize the **Qwen 7B** model with **4-bit quantization**.
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3. Implement **LoRA Adapters** for memory-efficient fine-tuning.
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name="unsloth/Qwen2.5-7B-bnb-4bit",
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max_seq_length=2048,
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dtype=torch.bfloat16,
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load_in_4bit=True,
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trust_remote_code=True,
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)
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## 🚀 Resources
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- **Source Code**: [GitHub Repository](https://github.com/while-basic/mindcraft) #todo: add mindcraft repo
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- **Colab Notebook**: [Colab Notebook](https://colab.research.google.com/drive/1Eq5dOjc6sePEt7ltt8zV_oBRqstednUT?usp=sharing)
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- **Blog Article**: [Walkthrough](https://chris-celaya-blog.vercel.app/articles/unsloth-training)
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- **Teaser**: [Video](https://www.youtube.com/watch?v=KUXY5OtaPZc)
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## Overview
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This **readme.md** provides step-by-step instructions to:
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1. Install and set up the **Unsloth framework**.
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2. Initialize the **Qwen 7B** model with **4-bit quantization**.
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3. Implement **LoRA Adapters** for memory-efficient fine-tuning.
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name="unsloth/Qwen2.5-7B-bnb-4bit",
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max_seq_length=2048,
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dtype=torch.bfloat16,
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load_in_4bit=True,
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trust_remote_code=True,
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)
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