Results for “urdf”

User-Mode Driver Framework v2 (UMDF). User-mode driver model that uses the same WDF object model as KMDF but runs in a host process (WUDFHost.exe) protected by the reflector. Required for some categories (Indirect Display Drivers, many sensor and camera drivers) and recommended for any driver that doesn't strictly need kernel mode. USE WHEN: user mentions "UMDF", "WUDFHost", "user-mode driver", "reflector", "IDD", "ISensor", "WDFHOST", "UMDF v2", "FX2" DO NOT USE FOR: KMDF (use `wdf-kmdf`), classic UMDF v1 (deprecated, COM-based)

Accelerate pandas workflows with GPU DataFrames using cuDF and dask-cuDF for ETL, joins, groupby, and large-scale data processing.

Fine-tune models on Azure AI Foundry using SFT, DPO, or RFT, covering dataset preparation, training job submission, deployment, and evaluation.

Build discounted cash flow models for company valuation. TRIGGERS - Use when user needs help with dcf-model related tasks.

Train large language models (7B-70B+) with RLHF using PPO, GRPO, DPO, and other algorithms, accelerated by Ray and vLLM for distributed multi-GPU setups.

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

Build discounted cash flow models for company valuation. TRIGGERS - Use when user needs help with dcf-model related tasks.

Audit-ready decision artifacts for LLM outputs — assumptions, risks, recommendation, and review gating (schema-valid JSON).

Produces a machine-validated, auditable JSON decision record with assumptions, risks, recommendation, and review gating for high-stakes decisions.

Produces auditable, schema-valid JSON decision records with assumptions, risks, recommendations, and review gating for high-stakes choices.

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

Trains language models with Direct Preference Optimization using preference pairs, covering DPOTrainer setup, dataset preparation, and beta tuning for stable preference learning without explicit reward models.