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Meta recently released Llama 3.2, and this video demonstrates how to fine-tune the 3 billion parameter instruct model using Unsloth and run it locally with Olama. By preparing the FindTom100K dataset, adjusting prompt templates, and adding LoRA adapters, the tutorial covers efficient fine-tuning and conversion of the model into GGUF format for local deployment. This enables users to run custom fine-tuned Llama 3.2 models on their own devices, leveraging powerful AI capabilities without relying on cloud resources.
LINKS:
Colab: https://colab.research.google.com/drive/1T5-zKWM_5OD21QHwXHiV9ixTRR7k3iB9?usp=sharing
https://www.llama.com/
Dataset: https://huggingface.co/datasets/mlabonne/FineTome-100k
Ollama madelfile: https://github.com/ollama/ollama/blob/main/docs/modelfile.md
💻 RAG Beyond Basics Course:
https://prompt-s-site.thinkific.com/courses/rag
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00:00 Introduction to Llama 3.2 Release
00:40 Overview of Llama 3.2 Models
01:42 Fine-Tuning Llama 3.2 with Unsloth
01:58 Preparing the Dataset for Fine-Tuning
02:34 Setting Up the Fine-Tuning Environment
03:32 Configuring the Fine-Tuning Parameters
07:59 Training the Model
12:31 Running the Fine-Tuned Model Locally
16:39 Conclusion and Future Videos
All Interesting Videos:
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Everything LLM: https://youtube.com/playlist?list=PLVEEucA9MYhNF5-zeb4Iw2Nl1OKTH-Txw
Everything Midjourney: https://youtube.com/playlist?list=PLVEEucA9MYhMdrdHZtFeEebl20LPkaSmw
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