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Build a domain‐aware data preprocessing pipeline: A multi‐agent collaboration approach

Flipboard

To address these challenges, this post introduces a multiagent collaboration pipeline: a set of specialized agents for classification, conversion, metadata extraction, and domainspecific tasks. Output a unified classification result for each standardized documentspecifying the category, confidence, extracted metadata, and next steps.

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FastAPI Meets OpenAI CLIP: Build and Deploy with Docker

Flipboard

Jump Right To The Downloads Section Building on FastAPI Foundations In the previous lesson , we laid the groundwork for understanding and working with FastAPI. Interactive Documentation: We showcased the power of FastAPIs auto-generated Swagger UI and ReDoc for exploring and testing APIs. Looking for the source code to this post?

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Top 6 Annotation Tools for HITL LLMs Evaluation and Domain-Specific AI Model Training

John Snow Labs

Auditing and correcting auto-generated annotations , identifying hallucinations, omissions, or formatting inconsistencies. Ideal for creating domain-specific models for NER, relation extraction, de-identification, and classification. It also supports text classification, NER, audio transcription, and PDF annotation.

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Host concurrent LLMs with LoRAX

AWS Machine Learning Blog

Walkthrough This post walks you through creating an EC2 instance, downloading and deploying the container image, and hosting a pre-trained language model and custom adapters from Amazon S3. Leave default settings for VPC , Subnet , and Auto-assign public IP. medium instance to run the optional notebook code snippets.

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Become an LLM Engineer with 20+ ODSC East Sessions

ODSC - Open Data Science

Hybrid Text Classification: Labeling with LLMs and Dense NeuralNetworks Mohammad Soltanieh-ha, PhD, Clinical Assistant Professor at Boston University Cut the cost of text labeling without sacrificing quality by combining premium LLMs with open-source tools.

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A Comprehensive Tutorial on the Five Levels of Agentic AI Architectures: From Basic Prompt Responses to Fully Autonomous Code Generation and Execution

Marktechpost

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) model = AutoModelForCausalLM.from_pretrained( MODEL_NAME, torch_dtype=torch.float16, device_map="auto", low_cpu_mem_usage=True ) get_model_and_tokenizer.model = model get_model_and_tokenizer.tokenizer = tokenizer print("Model loaded successfully!") Chat-v1.0"

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Fine-tune a BGE embedding model using synthetic data from Amazon Bedrock

AWS Machine Learning Blog

The process involves the following steps: Download the training and validation data, which consists of PDFs from Uber and Lyft 10K documents. TEI is a high-performance toolkit for deploying and serving popular text embeddings and sequence classification models, including support for FlagEmbedding models. Deploy the model to SageMaker.