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Hi, I'm a Machine Learning Scientist, Author, Blogger, and LLM Developer.
- The LLM Course: A popular curated list of resources to get into LLMs (>48k β).
- LLM Engineer's Handbook: My book about LLM engineering with fine-tuning, RAG, data, evaluation, deployment, etc.
- Hands-on GNNs: My book about designing and implementing graph neural networks in practice.
- LLM Datasets: Curated list of high-quality datasets for LLM fine-tuning.
- LLM Tools: Automate LLM pipelines with Colab notebooks like LLM AutoEval, LazyMergekit, LazyAxolotl, and AutoQuant.
- Liquid Foundation Models: My work at Liquid AI is to post-train our own pre-trained LLMs with a custom architecture. [Playground]
- Abliterated models: Collection of abliterated models to remove refusals. [Article]
- NeuralDaredevil-8B: Uncensored models with the highest MMLU score in the 8B category.
- AlphaMonarch-7B: Top performer in terms of reasoning + conversational abilities on a variety of benchmarks. [Demo]
- NeuralBeagle14-7B: The most powerful 7B model (rank 10 on the entire Open LLM Leaderboard). [Demo]
- Phixtral: Novel Mixture of Experts architecture with phi-2 models. [Demo]
- Beyonder-4x7B-v3: Mixture of Experts with four excellent fine-tuned Mistral-7b models. [Demo]
- NeuralHermes: A DPO fine-tuned version of OpenHermes (extremely cost-efficient). [Demo]