Summary
The video discusses the Quinn 3 model's compatibility for fine-tuning on custom datasets, emphasizing the importance of specific data structuring. The concept of adopter weights in fine-tuning large language models and their impact on model performance is explored in relation to preserving reasoning and non-reasoning data combination. Guidance is provided on setting up the SFT trainer with the original tokenizer, defining hyperparameters, and obtaining inferences from the model for optimal results. The benefits of quantizing models using a new quantization method with dynamic 2.0 support are explained, showcasing its impact on model performance and VRAM reduction. In addition, the process of saving and running inferences with the model, including options for model usage and setting appropriate parameters, is outlined for users' understanding and implementation.
Introduction to Quinn 3 Model
Quinn 3 model is a great option for fine-tuning on custom datasets due to its different sizes and performance capabilities. Fine-tuning these models on custom datasets requires specific data structuring.
Setting Up Custom Data Set
Exploration of setting up a custom data set for fine-tuning, understanding inferences, and different aspects of the fine-tuning process.
Fine-Tuning with Adopter Weights
Discussing the concept of adopter weights in fine-tuning large language models, introducing adopter weights, and their impact on model performance.
Quantization for Model Optimization
Explaining the benefits of quantizing models using the new quantization method, conted with dynamic 2.0 support, and its impact on model performance and VRAM reduction.
Fine-Tuning Specific Data Set
Fine-tuning a specific data set with reasoning capabilities, preserving reasoning, and non-reasoning data combination for optimal results.
Configuring SFT Trainer
Setting up the SFT trainer with the original tokenizer and combined data set, defining hyperparameters, batch size, and steps for model training.
Inference and Model Usage
Guidance on obtaining inferences from the model, tokenizing user input, using the model in thinking or non-thinking mode, and setting appropriate parameters for optimal results.
Saving and Running Inference
Explanation of saving and running inference with the model, using the save pre-trained function, providing the tokenizer, and other options for model usage.
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