#jupyter_notebook #automated_machine_learning #automl #classification #data_science #deep_learning #finetuning #hyperparam #hyperparameter_optimization #jupyter_notebook #machine_learning #natural_language_generation #natural_language_processing #python #random_forest #regression #scikit_learn #tabular_data #timeseries_forecasting #tuning

https://github.com/microsoft/FLAML

#jupyter_notebook #ai #finetuning #langchain #llama #llama2 #llm #machine_learning #python #pytorch #vllm

The `llama-recipes` repository helps you get started with Meta's Llama models, including Llama 3.2 Text and Vision. It provides example scripts and notebooks for various use cases, such as fine-tuning the models and building applications. You can use these models locally, in the cloud, or on-premises. The repository includes guides for installing the necessary tools, converting models to Hugging Face format, and using features like multimodal inference and responsible AI practices. This makes it easier for you to quickly set up and use the Llama models for your projects, saving time and effort.

https://github.com/meta-llama/llama-recipes

#python #deepseek #deepseek_r1 #fine_tuning #finetuning #gemma #gemma2 #llama #llama3 #llm #llms #lora #mistral #phi3 #qlora #unsloth

Using Unsloth.ai, you can finetune AI models like Llama, Mistral, and others up to 2x faster and with 70% less memory. The process is beginner-friendly; you just need to add your dataset, click "Run All" in the provided notebooks, and you'll get a faster, finetuned model that can be exported or uploaded to platforms like Hugging Face. This saves time and resources, making it easier to work with large AI models without needing powerful hardware. Additionally, Unsloth supports various features like 4-bit quantization, long context windows, and integration with tools from Hugging Face, making it a powerful tool for AI model development.

https://github.com/unslothai/unsloth