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Introduction to Animal Behaviour
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Learn to build applications with LLMs using LangChain, covering prompt templates, LLM setup, and chain creation. Gain practical insights through code examples and explore advanced techniques like few-shot prompting.
Dive into DeepSeek R1's technical architecture, benchmarks, and implementation, with hands-on demonstrations using Colab and practical insights for running distilled versions.
Explore Program-aided Language Models implementation in LangChain, featuring code walkthrough, examples, and practical applications for enhanced language model performance.
Explore Flan-UL2 20B model in Google Colab using 8-bit inference. Learn about zero-shot capabilities, chain-of-thought prompting, and practical applications in various NLP tasks.
Explore LangChain's Tools and Chains: utility functions, basic chains, chaining, PAL Math, and API tools. Learn to develop applications using these powerful components.
Explore LaMini-LM: mini language models trained on diverse, large-scale instruction data. Learn about dataset creation, model training, and practical applications through paper analysis and code demonstrations.
Explore Camel project for synthetic data creation and market research using AI agents. Learn implementation techniques and practical applications through demonstrations and code examples.
Explore efficient fine-tuning of large language models using PEFT and LoRA techniques, with a focus on practical implementation and uploading to HuggingFace Hub.
Explore StarCoder's reasoning capabilities and compatibility with PAL Chain in LangChain through demonstrations, code examples, and practical tests.
Explore StarCoder's advanced code generation capabilities, including continuous coding and gap-filling. Learn about its applications, versions, and impressive 8,000-token sequence length for enhanced coding assistance.
Explore LangChain for retrieval QA using local language models like Flan T5-XL, FastChat-T5, StableVicuna, and WizardLM, without relying on OpenAI services.
Enhance your AI development skills by learning to create and optimize BabyAGI using LangChain, with step-by-step code explanations and practical demonstrations.
Explore LangChain Output Parsers to enhance LLM results. Learn structured, comma-separated, Pydantic, fixing, and retry parsers for improved model outputs.
Explore Code Llama models and fine-tuned alternatives, comparing their capabilities, benchmarks, and recent developments in AI-powered coding assistance.
Explore the advancements in LLaMA2, including new base models, performance comparisons, and insights into its development and deployment on various platforms.
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