{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"torchtune","slug":"torchtune","name":"torchtune","type":"repo","url":"https://github.com/pytorch/torchtune","page_url":"https://unfragile.ai/torchtune","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"torchtune__cap_0","uri":"capability://automation.workflow.recipe.based.end.to.end.fine.tuning.pipeline.orchestration","name":"recipe-based end-to-end fine-tuning pipeline orchestration","description":"Torchtune provides a recipe system that encapsulates complete fine-tuning workflows as composable, reusable Python modules. Each recipe (e.g., LoRA, full fine-tuning, DPO) implements a specific training method with integrated features like FSDP distributed training, activation checkpointing, and gradient accumulation. Recipes are instantiated via YAML configuration files with CLI override support, enabling users to run complex training pipelines with a single command (tune run recipe_name) without writing boilerplate training loops.","intents":["Run a complete LoRA fine-tuning job on Llama 2 with distributed training across 8 GPUs using a single config file","Experiment with different hyperparameters by overriding YAML values from the CLI without modifying code","Implement a custom fine-tuning recipe by extending the base Recipe class with domain-specific logic","Chain multiple recipes together (e.g., quantization-aware training followed by evaluation)"],"best_for":["ML engineers building production fine-tuning pipelines","Researchers experimenting with multiple training methods on the same model","Teams needing reproducible, version-controlled training configurations"],"limitations":["Recipes are tightly coupled to specific model families (Llama, Gemma, Mistral, Phi, Qwen) — custom architectures require new recipe implementations","No built-in support for multi-stage training pipelines (e.g., pre-training → SFT → DPO) — requires manual orchestration","Recipe instantiation overhead adds ~500ms per startup due to YAML parsing and component initialization"],"requires":["Python 3.8+","PyTorch 2.0+","CUDA 11.8+ for GPU training (CPU training supported but slow)","torchtune installed via pip or from source"],"input_types":["YAML configuration files","CLI arguments (key=value overrides)","Python Recipe subclasses"],"output_types":["Trained model checkpoints (PyTorch .pt format)","Training metrics (logged to stdout, TensorBoard, or Weights & Biases)","Evaluation results (BLEU, perplexity, custom metrics)"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"torchtune__cap_1","uri":"capability://code.generation.editing.lora.and.qlora.parameter.efficient.fine.tuning.with.memory.optimization","name":"lora and qlora parameter-efficient fine-tuning with memory optimization","description":"Torchtune implements LoRA (Low-Rank Adaptation) and QLoRA (Quantized LoRA) as native PyTorch modules that inject trainable low-rank matrices into model layers while freezing base weights. QLoRA extends this by quantizing the base model to 4-bit or 8-bit precision using bitsandbytes, reducing memory footprint by 75%+ while maintaining training quality. The implementation uses a modular PEFT (Parameter-Efficient Fine-Tuning) system where LoRA adapters are applied to linear layers via a composition pattern, enabling seamless integration with distributed training and checkpointing.","intents":["Fine-tune a 70B parameter Llama model on a single 24GB GPU using QLoRA with 4-bit quantization","Apply LoRA to only attention layers while keeping MLP layers frozen to reduce trainable parameters by 99%","Merge trained LoRA weights back into the base model for inference without adapter overhead","Stack multiple LoRA adapters on the same base model for multi-task learning"],"best_for":["Resource-constrained teams training large models on consumer GPUs","Researchers comparing parameter-efficient vs full fine-tuning on the same hardware","Production systems requiring fast adapter switching without reloading base models"],"limitations":["LoRA rank and alpha hyperparameters are model-specific — no automated tuning; requires manual experimentation","QLoRA quantization introduces ~2-5% accuracy degradation on some tasks compared to full precision fine-tuning","Adapter merging is one-way — cannot unmerge LoRA weights after fusion without storing original base model","No support for quantizing to sub-4-bit precision (e.g., 2-bit or 3-bit)"],"requires":["PyTorch 2.0+","bitsandbytes 0.39+ (for QLoRA only)","CUDA 11.8+ (for quantization kernels)","Minimum 8GB VRAM for QLoRA on 7B models, 24GB for 70B models"],"input_types":["Pre-trained model weights (HuggingFace format or native PyTorch)","LoRA configuration (rank, alpha, target layers)","Training dataset (text or instruction-following format)"],"output_types":["LoRA adapter weights (.pt files, ~1-5% of base model size)","Merged model weights (full model with LoRA fused into base layers)","Training logs with memory usage metrics"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"torchtune__cap_10","uri":"capability://text.generation.language.model.inference.and.generation.with.kv.cache.optimization","name":"model inference and generation with kv-cache optimization","description":"Torchtune provides inference utilities for generating text from fine-tuned models, with built-in KV-cache optimization to reduce memory and compute during autoregressive generation. The framework implements efficient attention mechanisms (scaled dot-product attention, grouped query attention) and supports various decoding strategies (greedy, beam search, top-k sampling). Inference recipes load a trained model and generate outputs given prompts, with support for batched generation and streaming output. KV-cache is automatically managed and reused across generation steps.","intents":["Generate text from a fine-tuned Llama model with beam search decoding and temperature control","Batch-generate responses for 100 prompts in parallel to maximize GPU utilization during inference","Stream generated tokens to a client in real-time without waiting for full generation to complete","Benchmark inference latency and throughput (tokens/sec) on different hardware (GPU, CPU, mobile)"],"best_for":["Teams deploying fine-tuned models for real-time inference applications","Researchers benchmarking inference efficiency across different model architectures","Production systems requiring low-latency text generation with high throughput"],"limitations":["KV-cache memory grows linearly with sequence length — long-context generation (>4K tokens) requires significant VRAM","Beam search decoding is slower than greedy decoding due to tracking multiple hypotheses — 5-10x slower for beam_size=5","No built-in support for speculative decoding or other advanced inference optimization techniques","Batched generation requires padding to max sequence length in batch, reducing efficiency for variable-length prompts"],"requires":["PyTorch 2.0+","Fine-tuned model weights","Tokenizer for the model","Minimum 8GB VRAM for inference on 7B models, 24GB for 70B models"],"input_types":["Model weights and tokenizer","Prompts (text strings or token IDs)","Generation config (max_tokens, temperature, top_k, decoding_strategy)"],"output_types":["Generated text (string or token IDs)","Generation metadata (tokens generated, inference time, throughput)","Attention weights (for analysis/debugging)"],"categories":["text-generation-language","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"torchtune__cap_11","uri":"capability://automation.workflow.cli.based.recipe.execution.with.tune.run.and.tune.download.commands","name":"cli-based recipe execution with tune run and tune download commands","description":"Torchtune provides a command-line interface (tune run, tune download) for executing recipes and downloading models without writing Python code. The tune run command takes a recipe name and optional config overrides, automatically resolving the recipe from the registry and executing it. The tune download command fetches pre-trained models from HuggingFace Hub and caches them locally. The CLI supports shell completion, help text, and error messages to guide users. Under the hood, the CLI parses arguments, merges configs, and invokes recipe code.","intents":["Run a LoRA fine-tuning job with a single command: tune run lora_finetune_single_device --config llama2_7b_lora.yaml lr=1e-4","Download a pre-trained Llama 2 model from HuggingFace Hub: tune download meta-llama/Llama-2-7b","Get help on available recipes and their parameters: tune run --help","Execute a training job in a CI/CD pipeline without writing custom Python scripts"],"best_for":["ML engineers and researchers who prefer CLI-based workflows over Python notebooks","CI/CD pipelines triggering training jobs with environment-specific configs","Teams with non-technical users who need to run training without Python knowledge"],"limitations":["CLI is limited to predefined recipes — custom training logic requires writing Python code and registering a new recipe","Error messages may be cryptic for invalid configs — users must understand YAML and torchtune concepts to debug","No interactive mode for real-time parameter tuning — all parameters must be specified upfront","CLI does not support piping or shell integration beyond basic argument passing"],"requires":["torchtune installed and in PATH","Python 3.8+","PyTorch 2.0+ installed"],"input_types":["Recipe name (string, resolved from registry)","Config file path (YAML)","CLI overrides (key=value pairs)"],"output_types":["Trained model checkpoints","Training logs (stdout or file)","Downloaded model files (cached locally)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"torchtune__cap_12","uri":"capability://automation.workflow.activation.checkpointing.and.gradient.accumulation.for.memory.efficiency","name":"activation checkpointing and gradient accumulation for memory efficiency","description":"Torchtune integrates PyTorch's activation checkpointing (gradient checkpointing) to reduce peak memory usage during training by recomputing activations during backward pass instead of storing them. The framework also supports gradient accumulation to simulate larger batch sizes on limited VRAM by accumulating gradients over multiple forward-backward passes before updating weights. Both techniques are configured via YAML (activation_checkpointing: true, gradient_accumulation_steps: 4) and integrated transparently with distributed training and mixed-precision training.","intents":["Train a 70B model on a single 40GB GPU by enabling activation checkpointing and gradient accumulation","Simulate a batch size of 512 on a single GPU with batch_size=64 and gradient_accumulation_steps=8","Reduce peak memory by 30-40% using activation checkpointing with minimal training time overhead","Combine activation checkpointing with LoRA to fit a 70B model on a 24GB GPU"],"best_for":["Teams training large models on limited VRAM (consumer GPUs, smaller clusters)","Researchers studying memory-compute tradeoffs in LLM training","Production systems optimizing cost by using smaller GPUs with memory optimization techniques"],"limitations":["Activation checkpointing adds 10-20% training time overhead due to recomputation during backward pass","Gradient accumulation increases training time proportionally (e.g., 8 accumulation steps = 8x slower per update)","Checkpointing is not compatible with some operations (e.g., dropout with different random seeds per recomputation) — requires careful implementation","Gradient accumulation breaks synchronization in distributed training — requires careful handling of batch norm and dropout"],"requires":["PyTorch 2.0+ with activation checkpointing support","Sufficient disk space for temporary gradient storage (if using disk-based gradient checkpointing)"],"input_types":["Activation checkpointing config (enabled/disabled, checkpoint_segments)","Gradient accumulation config (accumulation_steps)","Model and training config"],"output_types":["Trained model with same convergence as non-checkpointed training","Memory usage metrics (peak memory, memory over time)","Training time metrics (time per step, total training time)"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"torchtune__cap_13","uri":"capability://automation.workflow.mixed.precision.training.with.automatic.loss.scaling","name":"mixed-precision training with automatic loss scaling","description":"Torchtune supports mixed-precision training (bfloat16, float16) to reduce memory usage and increase training speed while maintaining convergence. The framework automatically casts model parameters and activations to lower precision while keeping loss computation in float32 for numerical stability. Automatic loss scaling (AMP) prevents gradient underflow in float16 by scaling loss before backward pass. Mixed-precision is configured via YAML (dtype: bfloat16) and integrated with distributed training, gradient accumulation, and checkpointing.","intents":["Train a 70B model in bfloat16 to reduce memory by 50% and increase throughput by 1.5-2x","Use float16 with automatic loss scaling to prevent gradient underflow during training","Compare convergence between float32, float16, and bfloat16 on the same model and dataset","Enable mixed-precision training on multi-GPU setups without manual gradient scaling"],"best_for":["Teams training large models on modern GPUs (A100, H100) that have efficient bfloat16 support","Production systems optimizing training speed and memory efficiency","Researchers studying precision-accuracy tradeoffs in LLM training"],"limitations":["bfloat16 is only efficient on newer GPUs (A100+) — older GPUs (V100, T4) have minimal speedup","float16 training is less stable than bfloat16 and requires careful loss scaling tuning","Some operations (e.g., layer norm, softmax) are less stable in lower precision — may require float32 casting","Mixed-precision training may introduce subtle numerical differences that affect reproducibility"],"requires":["PyTorch 2.0+ with AMP support","GPU with efficient lower-precision support (A100, H100, or similar)","CUDA 11.0+ for float16, CUDA 11.1+ for bfloat16"],"input_types":["Mixed-precision config (dtype: bfloat16 or float16)","Loss scaling config (initial_scale, dynamic_scaling)","Model and training config"],"output_types":["Trained model in mixed-precision","Training metrics (loss, accuracy, throughput)","Memory and speed benchmarks"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"torchtune__cap_14","uri":"capability://code.generation.editing.attention.mechanism.variants.with.grouped.query.attention.gqa.and.flash.attention.support","name":"attention mechanism variants with grouped query attention (gqa) and flash attention support","description":"Implements multiple attention mechanisms including standard multi-head attention, grouped query attention (GQA) for reduced KV-cache memory, and integration with flash attention kernels for faster computation. Attention implementations are configurable per model and support both training and inference modes with proper gradient computation. Flash attention is automatically used when available, falling back to standard attention otherwise.","intents":["I want to use grouped query attention to reduce KV-cache memory during inference","I need to train with flash attention for faster training and lower memory usage","I want to compare different attention mechanisms' impact on model quality and speed"],"best_for":["teams training large models with memory constraints","researchers studying attention mechanism efficiency","practitioners optimizing inference latency and memory"],"limitations":["Flash attention requires CUDA 11.8+ and specific GPU architectures (A100, H100); not available on older GPUs","GQA reduces KV-cache size but can cause 1-2% accuracy degradation on some tasks compared to standard attention","Attention implementations are model-specific; custom architectures require custom attention implementations","Flash attention is a black-box kernel; debugging attention-related issues is harder than standard PyTorch attention"],"requires":["PyTorch 2.0+","CUDA 11.8+ for flash attention (standard attention works on CPU)","GPU with flash attention support (A100, H100, RTX 4090, etc.)"],"input_types":["Query, key, value tensors","Attention mask (optional)","Attention configuration (num heads, head dim, attention type)"],"output_types":["Attention output","Attention weights (optional, for analysis)","Gradients (for training)"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"torchtune__cap_2","uri":"capability://automation.workflow.distributed.training.with.fsdp.and.multi.gpu.synchronization","name":"distributed training with fsdp and multi-gpu synchronization","description":"Torchtune integrates PyTorch's Fully Sharded Data Parallel (FSDP) for distributed training across multiple GPUs and nodes, automatically sharding model parameters, gradients, and optimizer states. The framework handles FSDP initialization, process group setup, and synchronization barriers transparently within recipes, supporting mixed-precision training (bfloat16/float16) and gradient accumulation across shards. Users specify distributed settings via YAML (num_gpus, num_nodes, backend) and torchtune handles the rest, including automatic loss scaling and communication optimization.","intents":["Train a 70B Llama model across 8 A100 GPUs with FSDP sharding and gradient accumulation","Scale training from 1 GPU to 16 GPUs across multiple nodes without code changes, only YAML config updates","Use mixed-precision training (bfloat16) to reduce memory by 50% while maintaining convergence","Monitor per-GPU memory usage and communication overhead during distributed training"],"best_for":["Teams training models larger than single-GPU VRAM capacity","Production ML pipelines requiring reproducible multi-node training","Researchers benchmarking scaling efficiency across different hardware configurations"],"limitations":["FSDP communication overhead scales with model size and number of GPUs — 16+ GPU setups may see 15-25% throughput loss due to all-reduce operations","No automatic load balancing across heterogeneous hardware — requires manual GPU assignment if nodes have different specs","Checkpoint saving during FSDP training requires gathering sharded weights to rank 0, adding 10-30% overhead per checkpoint","Requires NCCL backend for GPU training — no support for Gloo or other collective backends"],"requires":["PyTorch 2.0+ with FSDP support","NCCL 2.14+ for multi-GPU communication","CUDA 11.8+ and cuDNN 8.6+","Passwordless SSH between nodes for multi-node training","Shared filesystem (NFS or similar) for checkpoint coordination"],"input_types":["YAML config with distributed settings (num_gpus, num_nodes, backend, sharding_strategy)","Model weights and training data","Checkpoint files for resuming training"],"output_types":["Distributed checkpoints (sharded or consolidated)","Training metrics aggregated across all ranks","Profiling data (communication time, compute time per rank)"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"torchtune__cap_3","uri":"capability://automation.workflow.flexible.configuration.system.with.yaml.and.cli.overrides","name":"flexible configuration system with yaml and cli overrides","description":"Torchtune uses a hierarchical configuration system where YAML files define all training parameters (model, optimizer, data, training hyperparameters) and CLI arguments override specific values without modifying files. The system supports nested configs (e.g., model.hidden_dim, optimizer.lr), environment variable interpolation, and dynamic component instantiation via a registry pattern. Users can compose configs by including base templates and selectively overriding values, enabling rapid experimentation without code changes.","intents":["Run the same recipe with 5 different learning rates by passing lr=1e-4 lr=1e-5 ... on the CLI","Create a base config for Llama 2 7B and inherit it in model-specific configs for 13B and 70B variants","Interpolate environment variables in YAML (e.g., data_path: ${DATA_DIR}) for CI/CD integration","Generate a config programmatically from Python and pass it to a recipe without writing YAML"],"best_for":["ML engineers running hyperparameter sweeps across multiple training jobs","Teams using CI/CD pipelines to trigger training with environment-specific configs","Researchers comparing model variants (7B vs 13B vs 70B) with minimal config duplication"],"limitations":["No built-in validation schema — invalid config keys are silently ignored, making typos hard to debug","CLI overrides only support scalar values (strings, numbers, booleans) — cannot override nested lists or dicts from CLI","Config merging is shallow — nested dicts are not recursively merged, only top-level keys","No support for conditional config logic (e.g., 'if num_gpus > 1 then use FSDP') — requires separate config files"],"requires":["PyTorch 2.0+","PyYAML 5.4+","Python 3.8+ (for f-string interpolation in configs)"],"input_types":["YAML files (.yaml or .yml)","CLI arguments (key=value format)","Python dicts (for programmatic config)"],"output_types":["Resolved configuration dict (after merging YAML and CLI overrides)","Instantiated components (model, optimizer, dataset, etc.)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"torchtune__cap_4","uri":"capability://code.generation.editing.multi.model.support.with.unified.model.builders.and.tokenizers","name":"multi-model support with unified model builders and tokenizers","description":"Torchtune provides native PyTorch implementations of popular LLM architectures (Llama, Gemma, Mistral, Phi, Qwen) with unified model builders that instantiate models from config dicts. Each model family has a corresponding tokenizer (via HuggingFace tokenizers library) and prompt template system for formatting training data. The architecture is modular — users can swap models by changing a single config line (model: llama2 vs model: mistral) without touching training code, and all models share the same training recipes.","intents":["Fine-tune Llama 2 7B using the same recipe and config structure as Mistral 7B by changing one config line","Load a pre-trained Llama 3 model from HuggingFace Hub and continue training with LoRA","Use a custom tokenizer (e.g., domain-specific vocab) by registering it in the tokenizer registry","Compare training efficiency across model families (Llama vs Gemma vs Mistral) on identical hardware"],"best_for":["Teams evaluating multiple model architectures for a specific task","Researchers implementing new model families and needing a standardized training framework","Production systems requiring model-agnostic fine-tuning pipelines"],"limitations":["Only supports models with transformer-decoder architecture — no encoder-only or encoder-decoder models","Model implementations are simplified versions of official implementations — may lack some optimizations or features (e.g., ALiBi positional embeddings in some models)","Tokenizer support is limited to models with HuggingFace tokenizers — custom tokenizers require manual integration","No automatic model conversion from other frameworks (e.g., TensorFlow, JAX) — only PyTorch weights supported"],"requires":["PyTorch 2.0+","HuggingFace transformers 4.30+","HuggingFace tokenizers 0.13+","Model weights in PyTorch format (.pt or .safetensors)"],"input_types":["Model config dict (architecture, hidden_dim, num_layers, etc.)","Pre-trained weights (HuggingFace Hub or local .pt files)","Tokenizer config (vocab size, special tokens, etc.)"],"output_types":["Instantiated PyTorch model (torch.nn.Module)","Tokenizer object (HuggingFace Tokenizer)","Model metadata (parameter count, FLOPs, memory footprint)"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"torchtune__cap_5","uri":"capability://code.generation.editing.direct.preference.optimization.dpo.and.knowledge.distillation.training","name":"direct preference optimization (dpo) and knowledge distillation training","description":"Torchtune provides recipes for DPO (Direct Preference Optimization) and knowledge distillation, enabling training on preference data without reinforcement learning. DPO recipe takes paired (chosen, rejected) responses and directly optimizes the model to prefer chosen outputs via a contrastive loss, eliminating the need for a separate reward model. Knowledge distillation recipe trains a student model to match teacher model outputs using KL divergence loss. Both recipes integrate with the standard training infrastructure (distributed training, checkpointing, metric logging) and support the same model families as SFT.","intents":["Train a Llama 2 model on preference data (chosen vs rejected responses) using DPO without building a reward model","Distill a 70B teacher model into a 7B student model to reduce inference latency by 10x","Combine DPO with LoRA to fine-tune a model on preference data with minimal memory overhead","Evaluate DPO-trained models on preference benchmarks (e.g., MT-Bench) to measure alignment improvement"],"best_for":["Teams building aligned LLMs without access to RLHF infrastructure (reward models, PPO training)","Researchers comparing DPO vs SFT vs RLHF on the same model and dataset","Production systems distilling large models for deployment on edge devices"],"limitations":["DPO assumes preference data is high-quality — noisy or mislabeled pairs degrade training significantly","DPO loss is sensitive to hyperparameters (beta, temperature) — requires careful tuning for each dataset","Knowledge distillation requires running teacher model inference during training, adding 2-3x compute overhead","No support for multi-turn preference data or ranking (only pairwise comparisons supported)"],"requires":["PyTorch 2.0+","Preference dataset in (prompt, chosen, rejected) format","For distillation: teacher model weights and inference capability","Minimum 24GB VRAM for DPO on 7B models, 80GB for 70B models"],"input_types":["Preference dataset (JSON with chosen/rejected pairs)","Model config and pre-trained weights","DPO hyperparameters (beta, temperature, loss_type)"],"output_types":["DPO-trained model weights","Training metrics (DPO loss, accuracy, reward margin)","Evaluation results on preference benchmarks"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"torchtune__cap_6","uri":"capability://code.generation.editing.quantization.aware.training.qat.with.post.training.quantization","name":"quantization-aware training (qat) with post-training quantization","description":"Torchtune provides recipes for quantization-aware training (QAT) that simulate quantization during training, enabling models to adapt to lower precision (int8, int4) before deployment. The framework also supports post-training quantization (PTQ) via integration with PyTorch's quantization APIs and bitsandbytes. QAT recipes apply fake quantization to weights and activations during forward passes, accumulating statistics for calibration, while PTQ quantizes pre-trained models without retraining. Both approaches are integrated with standard recipes and distributed training.","intents":["Train a Llama model with int8 quantization awareness to maintain accuracy after deployment on quantized hardware","Quantize a pre-trained 70B model to int4 for inference on consumer GPUs without fine-tuning","Compare QAT vs PTQ accuracy on the same model to determine if retraining is necessary","Generate quantization statistics (scale, zero-point) for custom quantization backends"],"best_for":["Teams deploying models on edge devices or mobile with strict memory/latency constraints","Researchers studying quantization impact on LLM accuracy and inference speed","Production systems requiring sub-second inference latency on consumer hardware"],"limitations":["QAT adds 15-30% training time overhead due to fake quantization operations","Quantization accuracy degrades with lower bit-widths (int4 may lose 2-5% accuracy vs fp32)","No support for mixed-precision quantization (e.g., int8 weights + int4 activations) in current recipes","Post-training quantization requires calibration data — performance degrades if calibration set is not representative"],"requires":["PyTorch 2.0+ with quantization support","bitsandbytes 0.39+ (for int4 quantization)","Calibration dataset (representative of inference data)","Minimum 24GB VRAM for QAT on 7B models"],"input_types":["Pre-trained model weights","QAT config (bit-width, quantization scheme, calibration method)","Calibration dataset (for PTQ)"],"output_types":["Quantized model weights (int8 or int4 format)","Quantization statistics (scale, zero-point per layer)","Accuracy metrics (perplexity, task-specific metrics on quantized model)"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"torchtune__cap_7","uri":"capability://automation.workflow.checkpointing.and.resumable.training.with.state.management","name":"checkpointing and resumable training with state management","description":"Torchtune provides a checkpointing system that saves model weights, optimizer state, training step count, and random seeds to enable resumable training from any checkpoint. The system handles distributed training checkpoints (sharded or consolidated), automatic checkpoint cleanup (keeping only N best checkpoints), and checkpoint validation. Users specify checkpoint frequency and retention policy via config, and torchtune automatically saves/loads state without manual intervention. Checkpoints are saved in PyTorch native format (.pt) or SafeTensors format for compatibility.","intents":["Resume a distributed training job that crashed after 10 hours by loading the last checkpoint and continuing from the same step","Keep only the 3 best checkpoints (by validation loss) to save storage, automatically deleting older ones","Convert a distributed FSDP checkpoint to a consolidated single-file checkpoint for inference","Load a checkpoint trained on 8 GPUs and continue training on 16 GPUs without manual state reshaping"],"best_for":["Teams running long-running training jobs on shared clusters with preemption risk","Production pipelines requiring deterministic training resumption and reproducibility","Researchers comparing checkpoint-based training vs from-scratch training"],"limitations":["Checkpoint consolidation (gathering sharded FSDP weights) adds 10-30% overhead and requires temporary disk space for full model","Resuming training with different distributed settings (e.g., 8 GPUs → 16 GPUs) requires manual state reshaping for FSDP","No built-in checkpoint versioning or git-like history — only keeps N most recent checkpoints","Checkpoint size equals model size + optimizer state (typically 2-3x model size) — requires significant storage"],"requires":["PyTorch 2.0+","Sufficient disk space (3-4x model size for checkpoints)","Shared filesystem for multi-node training (NFS, S3, etc.)","Write permissions to checkpoint directory"],"input_types":["Checkpoint config (save_interval, num_checkpoints_to_keep, checkpoint_dir)","Model state dict and optimizer state","Training metadata (step count, epoch, random seeds)"],"output_types":["Checkpoint files (.pt or .safetensors format)","Checkpoint metadata (step count, validation metrics, timestamp)","Consolidated checkpoints (for inference)"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"torchtune__cap_8","uri":"capability://data.processing.analysis.data.pipeline.with.prompt.templates.and.message.formatting","name":"data pipeline with prompt templates and message formatting","description":"Torchtune provides a data pipeline system that loads datasets, applies prompt templates to format examples, and tokenizes data for training. The system supports multiple data formats (JSON, CSV, HuggingFace datasets) and includes built-in prompt templates for common use cases (instruction-following, chat, code generation). Users can define custom prompt templates via Python classes or YAML configs, and the pipeline automatically handles padding, truncation, and batching. The message system supports multi-turn conversations with role-based formatting (user, assistant, system).","intents":["Load a JSON dataset of instruction-response pairs and format them as 'Instruction: ... Response: ...' using a built-in template","Create a custom prompt template for domain-specific tasks (e.g., code generation) and apply it to a HuggingFace dataset","Format multi-turn conversations with role-based messages (user, assistant, system) for chat fine-tuning","Tokenize and batch data with dynamic padding to maximize GPU utilization during training"],"best_for":["ML engineers building custom data pipelines for domain-specific fine-tuning","Teams standardizing prompt formatting across multiple training jobs","Researchers comparing different prompt templates on the same model and dataset"],"limitations":["Prompt templates are model-specific — a template designed for Llama may not work for Mistral due to different special tokens","No built-in data validation — malformed examples (e.g., missing fields) are silently skipped, making debugging hard","Dynamic padding adds ~5-10% overhead compared to static padding, but is necessary for variable-length sequences","No support for data augmentation (e.g., paraphrasing, back-translation) — requires external preprocessing"],"requires":["PyTorch 2.0+","HuggingFace datasets library (for loading HF datasets)","Tokenizer compatible with the model (HuggingFace tokenizers)"],"input_types":["Dataset files (JSON, CSV, or HuggingFace dataset ID)","Prompt template (Python class or YAML config)","Tokenizer config (vocab size, special tokens)"],"output_types":["Tokenized batches (input_ids, attention_mask, labels)","Formatted examples (for debugging/inspection)","Dataset statistics (sequence length distribution, token count)"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"torchtune__cap_9","uri":"capability://automation.workflow.metric.logging.and.evaluation.with.tensorboard.and.weights.biases.integration","name":"metric logging and evaluation with tensorboard and weights & biases integration","description":"Torchtune integrates with multiple logging backends (TensorBoard, Weights & Biases, stdout) to track training metrics (loss, accuracy, learning rate, throughput) and evaluation results. The framework automatically logs metrics at specified intervals and supports custom metric functions for task-specific evaluation (BLEU, ROUGE, exact match). Metrics are aggregated across distributed training ranks and logged to a central location. 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