{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"nvidia-nemo","slug":"nvidia-nemo","name":"NVIDIA NeMo","type":"framework","url":"https://github.com/NVIDIA/NeMo","page_url":"https://unfragile.ai/nvidia-nemo","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"nvidia-nemo__cap_0","uri":"capability://automation.workflow.distributed.llm.training.with.megatron.tensor.pipeline.parallelism","name":"distributed llm training with megatron tensor/pipeline parallelism","description":"Orchestrates large-scale LLM training across multiple GPUs using NVIDIA Megatron-Core's tensor parallelism (TP), pipeline parallelism (PP), and sequence parallelism strategies. Integrates with PyTorch Lightning's distributed training backend to automatically partition model weights, activations, and gradients across devices while managing communication collectives (all-reduce, all-gather) for synchronization. Supports mixed-precision training (FP8, BF16, FP32) with gradient accumulation and activation checkpointing to reduce memory footprint on large models (70B+ parameters).","intents":["Train a 70B+ parameter LLM across 8+ GPUs without running out of memory","Scale training from single-node to multi-node clusters with minimal code changes","Reduce training time by 3-5x using tensor parallelism instead of data parallelism alone","Fine-tune pretrained models like Llama or Qwen with distributed gradient updates"],"best_for":["ML engineers training foundation models at scale","Teams with access to multi-GPU clusters (A100, H100, L40S)","Organizations building proprietary LLMs requiring custom architectures"],"limitations":["Requires careful tuning of TP/PP degrees to avoid communication bottlenecks; suboptimal splits can reduce throughput by 20-40%","Distributed checkpointing adds ~5-10% training overhead for state serialization across ranks","No automatic fault tolerance — requires external job scheduler (SLURM, Kubernetes) for preemption recovery","Limited to NVIDIA GPUs; no multi-vendor support (AMD, Intel)"],"requires":["NVIDIA CUDA 11.8+","PyTorch 2.0+","Megatron-Core 0.3.0+","Multi-GPU setup (minimum 2 GPUs for TP/PP, 8+ for production)","NCCL 2.14+ for collective communication"],"input_types":["model configuration (YAML/JSON with architecture, hidden_size, num_layers)","training data (HuggingFace datasets, local JSONL, Parquet)","pretrained checkpoint (safetensors, .nemo format)"],"output_types":["distributed checkpoint (sharded across ranks)","training logs (loss, throughput tokens/sec, GPU utilization)","merged model weights (consolidated to single checkpoint)"],"categories":["automation-workflow","model-training","distributed-systems"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nvidia-nemo__cap_1","uri":"capability://code.generation.editing.llm.inference.with.speculative.decoding.and.kv.cache.optimization","name":"llm inference with speculative decoding and kv-cache optimization","description":"Implements efficient LLM inference through speculative decoding (draft model generates multiple tokens, verifier accepts/rejects in parallel) and key-value cache management to reduce memory bandwidth and latency. Supports batched generation with dynamic batching, token-level scheduling, and optional quantization (INT8, FP8) for reduced model footprint. Integrates with HuggingFace AutoModel for seamless loading of Llama, Mistral, Qwen, and other open-weight models without custom conversion pipelines.","intents":["Serve LLM inference at 2-3x higher throughput using speculative decoding without accuracy loss","Reduce inference latency by 40-60% through KV-cache optimization and token scheduling","Deploy quantized models (INT8) with <1% perplexity degradation","Load and run HuggingFace models directly without manual weight conversion"],"best_for":["ML engineers optimizing inference latency for production chatbots","Teams deploying models on resource-constrained hardware (single A100, L40S)","Builders integrating LLM inference into low-latency applications (real-time chat, code completion)"],"limitations":["Speculative decoding requires a smaller draft model; overhead of running two models can negate speedup if draft model is >20% of verifier size","KV-cache optimization assumes fixed sequence length; dynamic sequence lengths require cache reallocation (~10ms overhead per sequence)","Quantization (INT8/FP8) requires calibration on representative data; poor calibration can degrade quality by 2-5 BLEU points","No built-in batching scheduler — requires external request queue (e.g., vLLM-style scheduler) for optimal throughput"],"requires":["PyTorch 2.0+","NVIDIA CUDA 11.8+ (for FP8 quantization)","HuggingFace transformers 4.30+","Model weights in HuggingFace format or .nemo checkpoint"],"input_types":["prompt text (string or token IDs)","generation config (max_tokens, temperature, top_p)","model checkpoint (HuggingFace safetensors or NeMo .nemo)"],"output_types":["generated text (string)","token IDs with log probabilities","inference metrics (latency, tokens/sec, cache hit rate)"],"categories":["code-generation-editing","text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nvidia-nemo__cap_10","uri":"capability://image.visual.multimodal.model.training.with.vision.language.alignment","name":"multimodal model training with vision-language alignment","description":"Enables training of vision-language models (e.g., CLIP-like architectures) that align image and text embeddings through contrastive learning. Supports multi-GPU training with distributed contrastive loss computation, where positive pairs (image-caption) are gathered across all GPUs to increase batch size for stable training. Integrates with pretrained vision encoders (ViT, ResNet) and text encoders (BERT, GPT-2) with optional freezing of encoder weights for efficient fine-tuning.","intents":["Train a CLIP-like model to align image and text embeddings on custom image-caption dataset","Fine-tune a pretrained vision-language model on domain-specific images (medical, product) with frozen encoders","Build a zero-shot image classification system using aligned embeddings","Evaluate vision-language model on downstream tasks (image retrieval, visual question answering)"],"best_for":["ML engineers building vision-language systems for search, retrieval, or classification","Teams fine-tuning pretrained multimodal models on domain-specific data","Researchers experimenting with contrastive learning and alignment strategies"],"limitations":["Contrastive learning requires large batch sizes (256+) for stable training; smaller batches can lead to poor alignment and 5-10% accuracy degradation","Distributed contrastive loss requires all-gather communication across GPUs; communication overhead can reduce throughput by 20-30% on 8+ GPUs","Vision and text encoder architectures must be compatible (same embedding dimension); mismatches require projection layers, adding complexity","No built-in support for hard negative mining; random negatives can be inefficient for large-scale datasets"],"requires":["PyTorch 1.13+","torchvision for vision models","HuggingFace transformers for text encoders","NeMo 1.0+","Multi-GPU setup (minimum 2 GPUs for distributed contrastive loss)"],"input_types":["image files (JPEG, PNG)","text captions (string)","image-caption pairs (JSON manifest)"],"output_types":["aligned embeddings (image and text in same space)","trained vision and text encoders","similarity scores (for retrieval/ranking)"],"categories":["image-visual","text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nvidia-nemo__cap_11","uri":"capability://automation.workflow.model.quantization.and.export.to.onnx.torchscript.for.deployment","name":"model quantization and export to onnx/torchscript for deployment","description":"Provides post-training quantization (INT8, FP8) and export to ONNX or TorchScript formats for deployment on edge devices or inference servers. Quantization includes calibration on representative data and per-channel/per-layer quantization strategies. Exported models can be optimized with graph fusion, operator fusion, and constant folding to reduce model size and latency. Supports dynamic shapes for variable-length inputs (e.g., variable sequence length in NLP).","intents":["Quantize a trained model to INT8 with <1% accuracy loss for deployment on edge devices","Export a NeMo model to ONNX for inference on non-NVIDIA hardware (CPU, mobile)","Optimize exported model with graph fusion and constant folding to reduce latency by 30-50%","Deploy quantized model on inference servers (TensorRT, ONNX Runtime) with dynamic batch sizes"],"best_for":["ML engineers deploying models on edge devices (mobile, IoT) with latency/memory constraints","Teams migrating from NVIDIA-specific inference to cross-platform inference (ONNX Runtime, TensorRT)","Researchers optimizing model size and latency for production systems"],"limitations":["Quantization requires calibration on representative data; poor calibration can degrade accuracy by 2-5%","ONNX export requires manual operator mapping for custom NeMo layers; unsupported operators cause export failures","Dynamic shapes in ONNX add complexity and can reduce optimization opportunities; static shapes are 10-20% faster","TorchScript export is limited to Python-compatible operations; custom CUDA kernels cannot be exported"],"requires":["PyTorch 1.13+","ONNX 1.12+","ONNX Runtime (for ONNX inference)","TensorRT 8.0+ (optional, for optimized NVIDIA inference)"],"input_types":["trained NeMo model","calibration data (representative samples for quantization)","export config (YAML with quantization strategy, target format)"],"output_types":["quantized model (INT8 or FP8 weights)","ONNX model (for cross-platform inference)","TorchScript model (for PyTorch-based inference)","quantization statistics (min/max per layer)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nvidia-nemo__cap_12","uri":"capability://automation.workflow.preemption.aware.training.with.automatic.resumption.from.checkpoints","name":"preemption-aware training with automatic resumption from checkpoints","description":"Implements preemption-aware training that detects GPU preemption signals (SLURM, Kubernetes) and gracefully saves state before termination. On resumption, automatically loads the latest checkpoint and continues training from the exact step, preserving optimizer state, learning rate schedule, and random number generator seeds. Integrates with job schedulers to request additional time or requeue jobs automatically.","intents":["Train a model on a preemptible GPU cluster (spot instances, SLURM preemption) without losing progress","Automatically resume training after GPU preemption without manual intervention","Preserve training reproducibility across preemption events by saving RNG seeds and data order","Optimize training cost by using cheaper preemptible GPUs with automatic resumption"],"best_for":["ML engineers training models on shared clusters with preemption (SLURM, Kubernetes)","Teams using spot instances or preemptible GPUs to reduce training costs","Researchers running long-training jobs (days/weeks) that may be interrupted"],"limitations":["Preemption detection requires integration with job scheduler (SLURM, Kubernetes); custom schedulers require custom signal handlers","Checkpoint saving on preemption adds latency (5-10 seconds); if preemption grace period is <10 seconds, checkpoint may not complete","Resumption requires the same number of GPUs and same distributed training configuration; changing GPU count or parallelism strategy requires manual checkpoint conversion","No automatic job requeuing; requires external job scheduler integration (e.g., SLURM job arrays)"],"requires":["PyTorch Lightning 2.0+","SLURM or Kubernetes for preemption signals","NeMo 1.0+","Distributed checkpoint support (requires multi-GPU setup)"],"input_types":["training config (with preemption_timeout, checkpoint_dir)","checkpoint path (for resumption)","job scheduler config (SLURM sbatch script or Kubernetes job spec)"],"output_types":["checkpoint saved on preemption","training logs (with preemption events recorded)","resumed training from exact step"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nvidia-nemo__cap_13","uri":"capability://text.generation.language.speaker.verification.and.speaker.embedding.extraction.for.voice.authentication","name":"speaker verification and speaker embedding extraction for voice authentication","description":"Provides speaker verification models (speaker recognition, speaker identification) using speaker embedding extractors (e.g., ECAPA-TDNN, Titanet) that map audio to fixed-size speaker embeddings in a learned metric space. NeMo's speaker verification pipeline includes speaker enrollment (registering known speakers), speaker verification (comparing test audio to enrolled speakers), and speaker identification (classifying test audio to one of multiple speakers). Supports both speaker-dependent and speaker-independent models, and integrates with standard speaker verification datasets (VoxCeleb, TIMIT).","intents":["Build a voice authentication system that verifies a user's identity based on their voice","Extract speaker embeddings from audio for downstream tasks (speaker clustering, diarization)","Fine-tune a pre-trained speaker verification model on custom voice data","Identify which speaker is speaking in a multi-speaker audio file"],"best_for":["Teams building voice authentication or biometric systems","Researchers working on speaker recognition and speaker diarization","Organizations requiring speaker identification in multi-speaker scenarios"],"limitations":["Speaker verification is sensitive to acoustic conditions (background noise, reverberation); performance degrades significantly in noisy environments","Requires enrollment audio (10-30 seconds per speaker) for accurate verification; limited enrollment data leads to false rejections","Speaker identification accuracy decreases with number of speakers; practical limit is 10-100 speakers before accuracy drops below 90%","No built-in support for speaker adaptation or domain adaptation; requires fine-tuning on target domain"],"requires":["Python 3.9+","PyTorch 1.13+","NeMo 1.0+","Audio files (WAV, MP3, etc.) for enrollment and verification"],"input_types":["Audio files (enrollment and test)","Speaker labels (for training)","Optional: speaker embeddings (for similarity computation)"],"output_types":["Speaker embeddings (fixed-size vectors)","Verification scores (similarity between test and enrolled speakers)","Speaker identification results (predicted speaker ID)"],"categories":["text-generation-language","data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nvidia-nemo__cap_2","uri":"capability://code.generation.editing.automatic.speech.recognition.with.streaming.and.cache.aware.inference","name":"automatic speech recognition with streaming and cache-aware inference","description":"Builds ASR models using CTC (Connectionist Temporal Classification) or RNN-T (Recurrent Neural Network Transducer) architectures with streaming-capable encoder-decoder designs. Implements cache-aware streaming inference where the encoder maintains a sliding window of audio context and the decoder processes tokens incrementally, enabling low-latency transcription on audio streams. Integrates Lhotse data loading framework for efficient audio preprocessing (MFCC, Mel-spectrogram), augmentation (SpecAugment), and batching with variable-length sequences.","intents":["Build a real-time speech-to-text system that transcribes audio with <500ms latency","Train ASR models on multilingual datasets (English, Mandarin, Spanish) with shared encoder","Deploy streaming ASR on edge devices with <100MB model footprint","Fine-tune pretrained ASR models (Conformer, Squeezeformer) on domain-specific audio (medical, legal)"],"best_for":["Speech engineers building voice assistants or real-time transcription services","Teams deploying ASR on mobile/edge devices with latency constraints","Researchers experimenting with ASR architectures (CTC vs RNN-T vs hybrid)"],"limitations":["Streaming inference requires fixed context window; larger windows (>2 seconds) increase latency by 50-100ms per window","RNN-T models are 2-3x slower than CTC at inference due to autoregressive decoding; requires beam search for competitive WER","Lhotse data loading adds ~10-15% training time overhead for on-the-fly augmentation; pre-cached augmentation is 2-3x faster but requires 2-5x storage","No built-in language model integration; WER improvements from LM rescoring require external ARPA/FST tools"],"requires":["PyTorch 1.13+","librosa or soundfile for audio I/O","Lhotse 1.0+ for data loading","NVIDIA CUDA 11.0+ (optional, for GPU acceleration)"],"input_types":["audio files (WAV, MP3, FLAC)","audio streams (microphone input, network stream)","transcription manifests (JSON with audio_filepath, text labels)"],"output_types":["transcribed text (string)","token-level confidence scores","timing information (start/end timestamps per word)","streaming partial hypotheses (for real-time display)"],"categories":["code-generation-editing","data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nvidia-nemo__cap_3","uri":"capability://image.visual.text.to.speech.synthesis.with.phoneme.to.grapheme.conversion.and.prosody.control","name":"text-to-speech synthesis with phoneme-to-grapheme conversion and prosody control","description":"Generates natural speech from text using FastPitch (duration/pitch prediction) and HiFi-GAN (vocoder) architectures with optional prosody control (speaking rate, pitch contour). Includes grapheme-to-phoneme (G2P) modules for converting text to phonetic representations, supporting multiple languages (English, Mandarin, Japanese) with language-specific phoneme inventories. Vocoder can be fine-tuned on target speaker data for voice cloning with minimal samples (10-30 utterances).","intents":["Generate natural-sounding speech from text with controllable speaking rate and pitch","Build multilingual TTS system that handles text normalization and phoneme conversion automatically","Fine-tune TTS vocoder on custom speaker voice with 20-30 audio samples","Export TTS model to ONNX or TorchScript for deployment on edge devices"],"best_for":["Audio engineers building voice applications (virtual assistants, audiobook narration)","Teams deploying TTS on mobile/embedded devices with latency <500ms","Researchers experimenting with prosody control and speaker adaptation"],"limitations":["G2P conversion is language-specific; multilingual support requires separate G2P models per language, adding complexity","Prosody control (pitch, duration) is coarse-grained; fine-grained emotional prosody requires additional emotion classification model","Vocoder quality degrades significantly for out-of-distribution speakers; fine-tuning on new speaker requires 10-30 clean utterances minimum","No real-time streaming synthesis; full text must be processed before audio generation begins (~2-5 seconds for 30-second utterance)"],"requires":["PyTorch 1.13+","librosa for audio processing","g2p_en or language-specific G2P library","NVIDIA CUDA 11.0+ (optional, for GPU acceleration)"],"input_types":["text (string with optional phoneme markup)","speaker ID (for multi-speaker models)","prosody parameters (speaking_rate, pitch_scale)"],"output_types":["audio waveform (WAV, 22kHz or 44kHz)","mel-spectrogram (for visualization)","duration and pitch predictions (for debugging)"],"categories":["image-visual","text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nvidia-nemo__cap_4","uri":"capability://automation.workflow.experiment.tracking.and.checkpoint.management.with.pytorch.lightning.integration","name":"experiment tracking and checkpoint management with pytorch lightning integration","description":"Provides experiment management via PyTorch Lightning's Trainer API, automatically logging metrics (loss, accuracy, throughput) to multiple backends (Weights & Biases, TensorBoard, Neptune). Implements distributed checkpointing that shards model weights, optimizer states, and RNG seeds across GPU ranks, enabling resumption from preemption or failure without data loss. Checkpoint format is abstracted (supports .nemo, safetensors, PyTorch) with automatic conversion between formats.","intents":["Track training metrics across multiple runs and compare hyperparameter configurations","Resume training from checkpoint after GPU preemption or crash without losing progress","Save and load model checkpoints in a format-agnostic way (convert between .nemo, safetensors, PyTorch)","Reproduce training runs by logging random seeds, data order, and hyperparameters"],"best_for":["ML engineers managing long-running training jobs (days/weeks) on shared clusters","Teams using Weights & Biases or TensorBoard for experiment tracking","Researchers requiring reproducible training with full hyperparameter logging"],"limitations":["Distributed checkpointing requires all ranks to checkpoint simultaneously; asynchronous checkpointing not supported, causing ~5-10% training slowdown during checkpoint","Checkpoint format conversion (e.g., .nemo to safetensors) requires loading full model into memory; not feasible for models >200B parameters","No built-in checkpoint versioning or garbage collection; old checkpoints must be manually deleted to avoid disk space issues","Integration with external job schedulers (SLURM preemption signals) requires custom callback code"],"requires":["PyTorch Lightning 2.0+","PyTorch 2.0+","Optional: Weights & Biases, TensorBoard, Neptune for logging"],"input_types":["model (PyTorch nn.Module or NeMo ModelPT)","training config (YAML with learning_rate, batch_size, num_epochs)","checkpoint path (for resumption)"],"output_types":["checkpoint files (distributed across ranks or merged)","training logs (JSON, CSV, or cloud logging service)","metrics (loss, accuracy, throughput, GPU memory)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nvidia-nemo__cap_5","uri":"capability://memory.knowledge.huggingface.model.import.and.automodel.integration","name":"huggingface model import and automodel integration","description":"Enables seamless loading of HuggingFace pretrained models (Llama, Mistral, Qwen, Phi) into NeMo's training and inference pipelines via AutoModel wrapper. Automatically converts HuggingFace weight formats (safetensors, PyTorch) to NeMo's internal representation and applies NVIDIA-specific optimizations (Megatron-compatible weight layouts, FP8 quantization). Supports both full model loading and selective layer loading for parameter-efficient fine-tuning (LoRA, QLoRA).","intents":["Load a Llama-2 70B model from HuggingFace and fine-tune it with NeMo's distributed training","Convert HuggingFace model weights to Megatron-compatible layout for tensor parallelism","Apply LoRA or QLoRA to a HuggingFace model without modifying the base architecture","Export fine-tuned NeMo model back to HuggingFace format for community sharing"],"best_for":["ML engineers fine-tuning open-weight models from HuggingFace Hub","Teams wanting to leverage NeMo's distributed training without rewriting model code","Researchers experimenting with parameter-efficient fine-tuning (LoRA, QLoRA)"],"limitations":["Weight conversion from HuggingFace to Megatron layout requires custom mapping logic; unsupported architectures (e.g., custom attention variants) fail silently or require manual fixes","AutoModel abstraction adds ~5-10% overhead for weight loading due to format conversion and validation","LoRA/QLoRA integration is partial; not all NeMo features (e.g., distributed checkpointing) work seamlessly with LoRA adapters","No automatic architecture detection; mismatched config (e.g., hidden_size mismatch) causes cryptic shape errors"],"requires":["HuggingFace transformers 4.30+","HuggingFace hub (for model downloading)","PyTorch 2.0+","NeMo 1.0+"],"input_types":["HuggingFace model ID (string, e.g., 'meta-llama/Llama-2-70b')","model config (YAML overrides for NeMo-specific settings)","LoRA config (if using parameter-efficient fine-tuning)"],"output_types":["NeMo model (ModelPT instance)","Megatron-compatible checkpoint","HuggingFace-compatible model (for export)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nvidia-nemo__cap_6","uri":"capability://automation.workflow.mixed.precision.training.with.fp8.quantization.and.gradient.scaling","name":"mixed-precision training with fp8 quantization and gradient scaling","description":"Implements automatic mixed-precision (AMP) training using PyTorch's native AMP with optional FP8 quantization for weights and activations. Gradient scaling prevents underflow in lower precision, with automatic loss scaling that adapts based on gradient overflow detection. Supports per-layer quantization configuration, allowing selective FP8 for compute-heavy layers (attention, MLP) while keeping critical layers (embeddings, output) in higher precision.","intents":["Train large models 2-3x faster using FP8 quantization with <1% accuracy loss","Reduce GPU memory usage by 40-50% through lower-precision activations and gradients","Automatically tune loss scaling to prevent gradient underflow without manual tuning","Apply selective quantization to specific layers (e.g., FP8 for attention, BF16 for embeddings)"],"best_for":["ML engineers training large models on limited GPU memory (A100 40GB, H100 80GB)","Teams optimizing training throughput and cost on cloud infrastructure","Researchers experimenting with quantization-aware training"],"limitations":["FP8 quantization requires NVIDIA H100 or newer GPUs with native FP8 support; A100 requires software emulation, losing 30-50% speedup","Per-layer quantization requires careful tuning; aggressive quantization of critical layers (embeddings, output) can degrade convergence by 2-5%","Automatic loss scaling can oscillate if gradient distribution changes significantly during training; manual tuning sometimes needed","FP8 quantization is incompatible with some optimizers (e.g., LAMB); requires optimizer-specific implementations"],"requires":["PyTorch 2.0+ with AMP support","NVIDIA CUDA 11.8+","H100 GPU (for native FP8) or A100 (with software emulation)","Megatron-Core 0.3.0+ (for FP8 kernels)"],"input_types":["training config (precision: 'fp8', loss_scale_window: 1000)","model (PyTorch nn.Module)","training data (batches of tokens or images)"],"output_types":["trained model (with FP8 weights)","training metrics (loss, throughput, gradient overflow rate)","quantization statistics (min/max per layer)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nvidia-nemo__cap_7","uri":"capability://text.generation.language.natural.language.processing.with.token.classification.and.machine.translation","name":"natural language processing with token classification and machine translation","description":"Provides pre-built NLP models for token-level tasks (named entity recognition, part-of-speech tagging) and sequence-to-sequence tasks (machine translation, summarization). Token classification uses BERT-like encoders with task-specific classification heads, supporting multi-label and hierarchical label schemes. Machine translation leverages Transformer encoder-decoder architecture with optional back-translation for data augmentation and knowledge distillation for model compression.","intents":["Fine-tune a BERT model for named entity recognition on domain-specific text (medical, legal)","Build a machine translation system for low-resource language pairs using back-translation","Apply knowledge distillation to compress a translation model from 300M to 50M parameters","Extract structured information (entities, relations) from unstructured text"],"best_for":["NLP engineers building information extraction pipelines","Teams deploying NLP models on edge devices with latency constraints","Researchers experimenting with low-resource NLP tasks"],"limitations":["Token classification requires aligned token-level labels; automatic label alignment from document-level labels is not supported","Machine translation quality degrades significantly for low-resource language pairs (<1M parallel sentences); back-translation helps but requires large monolingual corpora","Knowledge distillation requires careful temperature tuning; poor tuning can reduce student model quality by 5-10 BLEU points","No built-in active learning or data selection; requires external tools for efficient data annotation"],"requires":["PyTorch 1.13+","HuggingFace transformers 4.20+","NeMo 1.0+"],"input_types":["text (string or token IDs)","labels (for token classification: BIO tags; for MT: parallel sentences)","model config (YAML with encoder/decoder architecture)"],"output_types":["token-level predictions (BIO tags, confidence scores)","translated text (for MT)","attention weights (for interpretability)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nvidia-nemo__cap_8","uri":"capability://automation.workflow.model.configuration.management.with.yaml.based.recipes.and.hydra.integration","name":"model configuration management with yaml-based recipes and hydra integration","description":"Manages model and training configurations using Hydra framework, enabling declarative specification of architectures, hyperparameters, and data pipelines via YAML files. Supports configuration composition (base configs + overrides), parameter sweeps for hyperparameter tuning, and automatic config validation against schema. Recipes are versioned and shareable, allowing reproducible training across teams and clusters.","intents":["Define a complex training pipeline (model, optimizer, data loader, callbacks) in a single YAML file","Run hyperparameter sweeps (learning rate, batch size, warmup steps) without code changes","Share reproducible training recipes across teams and version them in Git","Validate configs before training to catch misconfigurations early"],"best_for":["ML engineers managing multiple training experiments with different hyperparameters","Teams sharing training recipes and best practices","Researchers requiring reproducible and shareable training configurations"],"limitations":["Hydra config composition can become complex with deep inheritance hierarchies; debugging config resolution requires understanding Hydra's precedence rules","Parameter sweeps require careful tuning of sweep ranges; poor ranges can waste compute on suboptimal configurations","Config validation is optional; missing required fields are caught at runtime, not at config load time","YAML syntax errors can be cryptic; no IDE support for config schema validation"],"requires":["Hydra 1.1+","PyYAML 5.4+","NeMo 1.0+"],"input_types":["YAML config files (model, trainer, data, optimizer)","command-line overrides (e.g., 'learning_rate=1e-4')"],"output_types":["resolved config (merged from base + overrides)","config logs (for reproducibility)","sweep results (metrics for each hyperparameter combination)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"nvidia-nemo__cap_9","uri":"capability://data.processing.analysis.data.loading.and.preprocessing.with.lhotse.integration.for.audio.speech","name":"data loading and preprocessing with lhotse integration for audio/speech","description":"Integrates Lhotse framework for declarative audio data pipeline definition, handling audio I/O, feature extraction (MFCC, Mel-spectrogram), augmentation (SpecAugment, time-stretching), and batching with variable-length sequences. Lhotse manifests (JSON) describe datasets in a format-agnostic way, enabling easy dataset composition and versioning. Supports distributed data loading across GPUs with automatic sharding and deterministic shuffling for reproducibility.","intents":["Define a complex audio preprocessing pipeline (load WAV, extract Mel-spectrogram, apply SpecAugment) in a YAML config","Compose multiple audio datasets (train, validation, test) with automatic balancing and shuffling","Distribute data loading across 8+ GPUs without data duplication or synchronization issues","Reproduce data augmentation by logging random seeds and augmentation parameters"],"best_for":["Speech engineers building ASR/TTS systems with large audio datasets","Teams requiring reproducible data preprocessing and augmentation","Researchers experimenting with audio augmentation strategies"],"limitations":["Lhotse manifest creation requires upfront effort; converting from other formats (SoundFile, librosa) requires custom scripts","On-the-fly augmentation adds 10-15% training overhead; pre-cached augmentation is faster but requires 2-5x storage","Distributed data loading requires careful sharding to avoid data leakage between train/val/test splits","Variable-length sequence batching requires padding or bucketing; 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