Browsing: Pipeline

In this tutorial, we demonstrate how to efficiently fine-tune a large language model using Unsloth and QLoRA. We focus on building a stable, end-to-end supervised fine-tuning…

In this tutorial, we design an end-to-end, production-style analytics and modeling pipeline using Vaex to operate efficiently on millions of rows without materializing data in memory.…

INSTANCE_I = int(np.clip(INSTANCE_I, 0, len(X_test)-1)) x = X_test.iloc[INSTANCE_I].values y_true = float(y_test.iloc[INSTANCE_I]) pred = float(model.predict([x])[0]) iv = explainer.explain(x, budget=int(BUDGET_LOCAL), random_state=0) baseline = float(getattr(iv, “baseline_value”, 0.0)) main_effects =…

In this tutorial, we build an advanced Griptape-based customer support automation system that combines deterministic tooling with agentic reasoning to process real-world support tickets end-to-end. We…

import subprocess, sys, os, json, hashlib def pip(cmd): subprocess.check_call([sys.executable, “-m”, “pip”] + cmd) pip([“uninstall”, “-y”, “pillow”, “PIL”, “torchaudio”, “colpali-engine”]) pip([“install”, “-q”, “–upgrade”, “pip”]) pip([“install”, “-q”, “pillow<12”,…

metadata_dict = metadata.to_dict() diagnostic = DiagnosticReport() diagnostic.generate(real_data=real, synthetic_data=synthetic_sdv, metadata=metadata_dict, verbose=True) print(“Diagnostic score:”, diagnostic.get_score()) quality = QualityReport() quality.generate(real_data=real, synthetic_data=synthetic_sdv, metadata=metadata_dict, verbose=True) print(“Quality score:”, quality.get_score()) def show_report_details(report, title):…

In this tutorial, we build an advanced, end-to-end learning pipeline around Atomic-Agents by wiring together typed agent interfaces, structured prompting, and a compact retrieval layer that…

Serving Large Language Models (LLMs) at scale is a massive engineering challenge because of Key-Value (KV) cache management. As models grow in size and reasoning capability,…

!pip -q install -U “protobuf<5” “flwr[simulation]” transformers peft accelerate datasets sentencepiece import torch if torch.cuda.is_available(): !pip -q install -U bitsandbytes import os os.environ[“RAY_DISABLE_USAGE_STATS”] = “1” os.environ[“TOKENIZERS_PARALLELISM”]…

about examining crime trends in your local area. You know that relevant data exists, and you have some basic analytical skills that you can use to…