Results for “inference”

Search the web and extract content from URLs using Tavily and Exa APIs via the inference.sh CLI.

Upscale and enhance images using Real-ESRGAN, Thera, FLUX Upscaler, and Topaz via the inference.sh CLI.

Translate and dub audio/video into 29 languages while preserving speaker voice using the inference.sh CLI.

Start, query, and stop a TAO inference microservice for a specific network architecture by delegating container execution to the appropriate platform skill.

Access 100+ LLMs including Claude, Gemini, Kimi, and GLM via the inference.sh CLI with automatic fallback and cost optimization.

Remove background noise and isolate vocals from audio files using the inference.sh CLI and ElevenLabs voice isolator.

Generate AI avatar and talking head videos using inference.sh CLI with models like P-Video-Avatar, OmniHuman, Fabric, and PixVerse.

Generate music and songs using ElevenLabs, Diffrythm, and Tencent Song Generation models via the inference.sh CLI.

Generate videos from text, images, or references using 40+ AI models via the inference.sh CLI.

Generate AI sound effects from text descriptions using the inference.sh CLI, with control over duration and prompt influence.

Generate images using FLUX models via the inference.sh CLI, supporting text-to-image, image-to-image, and LoRA fine-tuning.

Generate images with 50+ AI models including GPT-Image-2, FLUX, Gemini, Grok, Seedream, and Reve via the inference.sh CLI.

Generate videos using Google Veo models via the inference.sh CLI, with support for multiple Veo versions and cinematic prompts.

Slow down interpretation from observation to action. Use when students or adults need to examine assumptions in conflict, dialogue, or inquiry.

Build multi-step AI content creation pipelines combining image, video, audio, and text using the inference.sh CLI.

Detect model stealing, model inversion, and membership inference performed through inference-API abuse by monitoring query patterns, applying output perturbation, and red-teaming your own model's extractability.

Causal inference framework for answering "does X cause Y?" beyond correlation. DoWhy (Microsoft Research) provides the identify-estimate-refute loop: define a causal graph (DAG), identify the causal effect using backdoor/frontdoor/instrumental variable criteria, estimate treatment effects with multiple estimators, and validate results with automated refutation tests. Use when: distinguishing causation from correlation, estimating treatment effects (ATE, ATT, CATE), designing and analyzing A/B tests with confounders, using instrumental variables, performing counterfactual reasoning ("what would have happened if..."), validating causal claims with sensitivity analysis, working with observational data where randomization is impossible, or any analysis where the question is "what is the CAUSAL effect of X on Y" rather than just "how do X and Y relate?"

Authenticates and connects to Google Cloud Agent Platform for inference with Gemini and third-party OpenMaaS models (Llama, DeepSeek, Qwen). Generates code for multiple SDKs, configures endpoints, and troubleshoots common errors.

Trains, evaluates, exports, and runs inference on FoundationStereo models for stereo depth estimation and 3D reconstruction from stereo image pairs.

Build AI applications with the inference.sh Python SDK: run apps, build agents, and integrate with 250+ models using sync/async, streaming, file uploads, and a tool builder API.

Transform any voice into a different voice while preserving speech content and emotion using the inference.sh CLI and ElevenLabs models.

Python SDK for inference.sh - run AI apps, build agents, and integrate with 150+ models. Package: inferencesh (pip install inferencesh). Supports sync/async, streaming, file uploads. Build agents with template or ad-hoc patterns, tool builder API, skills, and human approval. Use for: Python integration, AI apps, agent development, RAG pipelines, automation. Triggers: python sdk, inferencesh, pip install, python api, python client, async inference, python agent, tool builder python, programmatic ai, python integration, sdk python

Decision-theoretic framework for agents embedded within the environments they model and act upon

Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques for 1.5-3.6× speedup without quality loss.

Transcribe audio to text using ElevenLabs Scribe and Whisper models via the inference.sh CLI, supporting timestamps, speaker diarization, translation, and multi-language transcription.

After any AI-generated explanation, require the learner to identify one place it could be wrong, one thing to check, and one source to consult. Builds epistemic vigilance — treats AI output as a claim to evaluate, not truth to absorb.

Mathematical AI interpretability with sheaf cohomology, spectral analysis, causal inference, and hallucination prevention

Evaluates how well internal model representations (hidden states) predict token-level information importance in summarization tasks, using NDCG@10 and Spearman's rank correlation.

Use for writing, reviewing, debugging, or testing Python analysis of Bayesian inference results with ArviZ, including 1.x DataTree groups, legacy InferenceData inputs, xarray dimensions and coordinates, conversion, summaries, R-hat/ESS/MCSE diagnostics, posterior predictive checks, PSIS-LOO, Pareto-k, and model comparison. Trigger on chain/draw shape errors, mislabeled groups, flattened samples, missing log likelihood, or misleading diagnostic claims. Do not use to construct or sample PyMC, NumPyro, or Bambi models, for generic plotting, or for deterministic statistics without Bayesian draws.

Frame causal questions and estimate treatment effects with assumption checks. Use when: (1) policy impact analysis, (2) A/B interpretation beyond correlation, (3) confounding diagnostics. NOT for: medical/legal conclusions without experts.