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Update README.md

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@@ -17,11 +17,13 @@ This repository demonstrates how to fine-tune the **Qwen 7B** model to create "A
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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**: [Run the Tutorial](https://colab.research.google.com/drive/1Eq5dOjc6sePEt7ltt8zV_oBRqstednUT?usp=sharing) #todo: add colab notebook url
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-
 
 
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  ## Overview
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- This guide 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.
@@ -67,7 +69,7 @@ import torch
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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, # Or torch.float16 for older GPUs
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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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+
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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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  )