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Explore combined preference and supervised fine-tuning with ORPO. Learn about fine-tuning methods, loss functions, and performance improvements. Includes practical demos and evaluations.
Deploy serverless inference endpoints efficiently. Learn setup, use cases, and cost comparisons for API-based machine learning model deployment.
Explore advanced retrieval augmented generation techniques using ALL-SORT for improved LLM-assisted information retrieval, with practical demonstrations and performance evaluations.
Explore LLM fine-tuning techniques, from dataset generation to hyperparameter optimization. Learn to overcome challenges like the reversal curse and optimize model performance for specific tasks.
Explore practical benchmarking of Mistral Large vs GPT4. Compare performance, run custom evaluations, and gain insights into LLM capabilities for informed decision-making.
Explore advanced fine-tuning techniques like DoRA, NEFT, LoRA+, and Unsloth to optimize language models. Learn implementation, comparisons, and practical applications through code examples and demonstrations.
Explore advanced techniques for fine-tuning multi-modal vision and language models like LLaVA and IDEFICS. Learn about architectures, data creation, training, and evaluation methods.
Explore Mamba and state space models, comparing them to transformers. Learn basic equations, run inference in Colab, and understand training processes and future directions for this compression-focused technology.
Master data extraction techniques using large language models for efficient and accurate information retrieval from various sources. Enhance your skills in JSON and YAML extraction, and compare model performances.
Explore advanced Whisper fine-tuning techniques for improved speech transcription. Learn to transcribe YouTube videos, understand model mechanics, and evaluate performance using LoRA.
Learn to push, merge, and manage various LLM model formats on HuggingFace. Covers setup, fine-tuning, and best practices for training and inference. Includes practical demonstrations and resource recommendations.
Explore the benefits, performance, and fine-tuning of small language models. Learn about function-calling with quantized models and discover the best tiny LLMs for various applications.
Explore advanced speculative decoding techniques for faster LLM inference, including naive, n-gram, lookahead, and assisted methods. Compare performance and learn optimization tips.
Explore Mixtral's advanced capabilities, from fine-tuning to inference. Master function calling, 4-bit quantization, and efficient model deployment for enhanced AI performance.
Learn to deploy and serve custom LLMs efficiently for multiple customers. Master server selection, API software, GPU optimization, and quantization techniques for cost-effective, high-performance model serving.
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