{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"litgpt","slug":"litgpt","name":"LitGPT","type":"framework","url":"https://github.com/Lightning-AI/litgpt","page_url":"https://unfragile.ai/litgpt","categories":["model-training","deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"litgpt__cap_0","uri":"capability://code.generation.editing.decoder.only.transformer.model.architecture.with.20.pre.configured.model.families","name":"decoder-only transformer model architecture with 20+ pre-configured model families","description":"Implements minimal-abstraction decoder-only transformer architectures (GPT, Llama, Mistral, Phi, Gemma, Qwen, etc.) using PyTorch with explicit, modifiable code rather than wrapper abstractions. The Config dataclass in litgpt/config.py defines ~100 parameters per model (layer count, embedding dimensions, attention heads, RoPE scaling, GQA variants) that map directly to model instantiation. Supports model sizes from 0.5B to 405B parameters with native support for architectural variants like grouped query attention, sliding window attention, and mixture-of-experts.","intents":["I want to train or fine-tune a specific open-source LLM without being locked into a proprietary API","I need to understand and modify the exact transformer implementation for research or custom optimization","I want to work with multiple model families (Llama, Mistral, Phi) using a unified training pipeline","I need to deploy models with specific architectural features like GQA or sliding window attention"],"best_for":["ML researchers and engineers building custom LLM training pipelines","teams requiring full control over model architecture and training dynamics","organizations migrating from closed-source LLM APIs to open-source alternatives"],"limitations":["Requires deep understanding of transformer architectures and PyTorch to modify core model code","No automatic architecture discovery — must select from pre-configured models or manually define new ones","Model configs are Python dataclasses, not serializable to standard formats like YAML without custom conversion"],"requires":["Python 3.9+","PyTorch 2.0+","PyTorch Lightning 2.0+","CUDA 11.8+ for GPU training (CPU inference supported but slow)"],"input_types":["model configuration (Python Config object)","pretrained checkpoint (HuggingFace or LitGPT format)","training data (raw text, JSON, or custom DataModule)"],"output_types":["instantiated PyTorch model (torch.nn.Module)","generated text tokens","model checkpoint (PyTorch state_dict)"],"categories":["code-generation-editing","model-training","deep-learning-frameworks"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litgpt__cap_1","uri":"capability://code.generation.editing.lora.and.qlora.parameter.efficient.fine.tuning.with.selective.layer.freezing","name":"lora and qlora parameter-efficient fine-tuning with selective layer freezing","description":"Implements Low-Rank Adaptation (LoRA) and Quantized LoRA (QLoRA) fine-tuning via the litgpt/lora.py module, which injects trainable low-rank decomposition matrices (A, B) into attention and linear layers while freezing base model weights. QLoRA variant uses BitsAndBytes 4-bit quantization to reduce base model memory footprint to ~6GB for 70B models. Supports selective layer targeting (e.g., only attention layers or specific transformer blocks) and integrates with PyTorch Lightning's distributed training for multi-GPU LoRA fine-tuning.","intents":["I want to fine-tune a 70B model on a single GPU with limited VRAM","I need to adapt a pretrained model to a specific domain while keeping 99%+ of weights frozen","I want to train multiple LoRA adapters on the same base model for different tasks","I need to reduce fine-tuning time and memory compared to full model fine-tuning"],"best_for":["teams with limited GPU memory (single 24GB GPU or smaller)","rapid prototyping and domain adaptation workflows","multi-task learning scenarios requiring task-specific adapters"],"limitations":["LoRA rank and alpha hyperparameters require tuning; no automatic selection","QLoRA introduces ~5-10% inference latency overhead due to dequantization during forward passes","Adapter composition (merging multiple LoRA modules) requires manual weight merging, not built-in","No support for LoRA on embedding layers by default (requires custom implementation)"],"requires":["Python 3.9+","PyTorch 2.0+","BitsAndBytes 0.41+ (for QLoRA)","8GB+ VRAM for QLoRA on 70B models, 24GB+ for standard LoRA"],"input_types":["pretrained model checkpoint","training dataset (text or instruction-following format)","LoRA config (rank, alpha, target layers)"],"output_types":["LoRA adapter weights (A, B matrices)","merged model checkpoint (base + LoRA weights)","training metrics (loss, validation accuracy)"],"categories":["code-generation-editing","model-training","memory-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litgpt__cap_10","uri":"capability://text.generation.language.http.server.deployment.with.litserve.and.openai.compatible.endpoints","name":"http server deployment with litserve and openai-compatible endpoints","description":"Integrates with LitServe (Lightning AI's inference server) to deploy models as HTTP APIs with OpenAI-compatible endpoints (/v1/chat/completions, /v1/completions). Handles request batching, concurrent inference, and automatic scaling across multiple GPUs. Supports streaming responses (Server-Sent Events), request validation, and error handling. Models can be served with quantization, LoRA adapters, or full precision, with automatic device placement and memory management.","intents":["I want to deploy a fine-tuned model as a REST API compatible with OpenAI client libraries","I need to serve multiple models concurrently with automatic request batching","I want to enable streaming responses for real-time text generation","I need to scale inference across multiple GPUs with automatic load balancing"],"best_for":["production deployment of LLM services","teams using OpenAI-compatible client libraries (LangChain, LlamaIndex)","inference-heavy applications requiring horizontal scaling"],"limitations":["LitServe is relatively new; less battle-tested than vLLM or TensorRT-LLM for production workloads","No built-in request queuing or priority handling; requires custom middleware","Streaming responses add ~5-10% latency overhead vs batch inference","No automatic model caching or warm-up; first request after deployment may be slow"],"requires":["Python 3.9+","PyTorch 2.0+","LitServe 0.1+","Model checkpoint","GPU for inference (CPU inference supported but slow)"],"input_types":["model checkpoint","server config (port, batch_size, max_tokens)","HTTP requests (JSON with prompt and generation params)"],"output_types":["HTTP responses (JSON with generated text)","streaming responses (Server-Sent Events)","server logs and metrics"],"categories":["text-generation-language","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litgpt__cap_11","uri":"capability://data.processing.analysis.evaluation.integration.with.lm.evaluation.harness.for.benchmarking","name":"evaluation integration with lm-evaluation-harness for benchmarking","description":"Integrates with EleutherAI's lm-evaluation-harness to run standardized benchmarks (MMLU, HellaSwag, ARC, TruthfulQA, etc.) on trained models. Provides evaluation scripts that load LitGPT checkpoints, apply prompt formatting, and compute benchmark metrics. Supports both zero-shot and few-shot evaluation, with configurable number of shots and prompt templates. Results are comparable across models and frameworks, enabling reproducible evaluation.","intents":["I want to benchmark my fine-tuned model against standard LLM evaluation suites","I need to compare my model's performance on MMLU, HellaSwag, and other standard benchmarks","I want to run zero-shot and few-shot evaluation with different prompt templates","I need reproducible evaluation results for research papers or model cards"],"best_for":["research teams publishing model results","model evaluation and comparison workflows","teams requiring standardized benchmarking"],"limitations":["lm-evaluation-harness is computationally expensive; MMLU evaluation takes 1-2 hours on a single GPU","Benchmark results are sensitive to prompt formatting; small template changes can shift scores by 1-3%","No built-in support for custom evaluation tasks; requires extending lm-evaluation-harness","Evaluation requires downloading benchmark datasets; adds ~10GB storage overhead"],"requires":["Python 3.9+","PyTorch 2.0+","lm-evaluation-harness","Model checkpoint","GPU for evaluation (CPU evaluation is very slow)","Internet connection for downloading benchmarks"],"input_types":["model checkpoint","benchmark names (MMLU, HellaSwag, etc.)","evaluation config (num_shots, batch_size)"],"output_types":["benchmark scores (accuracy, F1, etc.)","evaluation logs (per-task metrics)","results JSON (for comparison and reporting)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litgpt__cap_12","uri":"capability://data.processing.analysis.tokenizer.abstraction.with.huggingface.and.sentencepiece.backend.support","name":"tokenizer abstraction with huggingface and sentencepiece backend support","description":"Implements a unified Tokenizer class (litgpt/tokenizer.py) that wraps both HuggingFace Tokenizers and SentencePiece backends, providing a consistent encode/decode interface. Handles special tokens, padding, truncation, and batch tokenization. Supports loading tokenizers from HuggingFace hub or local paths, with automatic caching. Integrates with model-specific tokenizer configurations (e.g., Llama's special tokens, Mistral's chat tokens).","intents":["I want a unified tokenizer interface that works with both HuggingFace and SentencePiece models","I need to tokenize text consistently across different model families","I want to handle special tokens and chat formatting automatically","I need to batch tokenize large datasets efficiently"],"best_for":["teams using multiple model families with different tokenizers","data preprocessing and dataset preparation workflows","inference pipelines requiring consistent tokenization"],"limitations":["Tokenizer abstraction adds ~5-10% overhead vs direct tokenizer calls","Some tokenizer-specific features (e.g., custom token merging) are not exposed","No built-in support for custom tokenizers; requires subclassing Tokenizer class","Batch tokenization performance depends on underlying tokenizer implementation"],"requires":["Python 3.9+","transformers library (for HuggingFace tokenizers)","sentencepiece (for SentencePiece tokenizers)","Tokenizer file or HuggingFace model ID"],"input_types":["text (string or list of strings)","tokenizer path or HuggingFace model ID","tokenization config (padding, truncation, max_length)"],"output_types":["token IDs (list of integers)","token strings (for debugging)","attention masks (for padding)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litgpt__cap_13","uri":"capability://code.generation.editing.configuration.system.with.dataclass.based.model.and.training.configs","name":"configuration system with dataclass-based model and training configs","description":"Implements a Config dataclass system (litgpt/config.py) that defines model architectures via ~100 parameters (num_layers, hidden_size, num_heads, etc.) and training hyperparameters (learning_rate, batch_size, warmup_steps). Provides named configurations for 20+ model families (Llama, Mistral, Phi, etc.) that can be loaded by name or customized. Configs are Python dataclasses, enabling IDE autocomplete, type checking, and programmatic manipulation. Supports config serialization to YAML for reproducibility.","intents":["I want to define model architectures programmatically with type checking","I need to manage training hyperparameters across multiple experiments","I want to load pre-configured models (Llama, Mistral) and customize specific parameters","I need to save and reproduce training configs for reproducibility"],"best_for":["ML engineers managing multiple model configurations","research teams requiring reproducible experiment tracking","teams using Python-first configuration management"],"limitations":["Dataclass configs are Python-specific; not easily shareable with non-Python tools","No built-in config validation; requires manual checks for invalid parameter combinations","Config serialization to YAML requires custom logic; no automatic round-trip serialization","Large config files (100+ parameters) can be difficult to navigate and understand"],"requires":["Python 3.9+","PyTorch 2.0+"],"input_types":["model name (string, e.g., 'Llama-2-7b')","config parameters (dict or Config object)","YAML config file (optional)"],"output_types":["Config object (dataclass instance)","model instantiation parameters","YAML config file (for saving)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litgpt__cap_14","uri":"capability://text.generation.language.prompt.formatting.system.with.model.specific.instruction.templates","name":"prompt formatting system with model-specific instruction templates","description":"Implements a Prompt system (litgpt/prompts.py) that applies model-specific instruction templates for chat and instruction-following tasks. Supports templates for Llama Chat, Mistral Instruct, Phi, Gemma, and other models. Handles multi-turn conversations, system prompts, and automatic token counting. Templates are defined as Python classes with format() methods, enabling transparent prompt construction and debugging.","intents":["I want to apply model-specific chat formatting automatically (e.g., Llama Chat template)","I need to handle multi-turn conversations with consistent formatting","I want to count tokens in prompts before generation to avoid truncation","I need to debug prompt formatting to understand how my input is transformed"],"best_for":["instruction-following and chat applications","teams using multiple model families with different prompt formats","evaluation and benchmarking workflows requiring consistent formatting"],"limitations":["Prompt templates are model-specific; custom models require manual template definition","No automatic template detection; requires explicit template selection","Token counting is approximate; actual token count depends on tokenizer implementation","Multi-turn conversation handling requires manual state management"],"requires":["Python 3.9+","Model-specific prompt template (built-in or custom)","Tokenizer for token counting"],"input_types":["prompt text (string)","model name (for template selection)","conversation history (optional, for multi-turn)"],"output_types":["formatted prompt (string)","token count (integer)","formatted conversation (for multi-turn)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litgpt__cap_15","uri":"capability://code.generation.editing.configuration.hub.with.pre.defined.model.architectures.and.hyperparameters","name":"configuration hub with pre-defined model architectures and hyperparameters","description":"LitGPT provides a configuration hub (litgpt/config.py) with pre-defined Config dataclasses for 20+ model families (Llama, Mistral, Phi, Gemma, Qwen, Falcon, OLMo, etc.), each specifying ~100 architectural parameters (layer count, embedding dimensions, attention heads, RoPE, GQA, etc.). Named configurations enable one-line model instantiation without manual parameter specification. The hub is extensible — new models can be added by defining a Config dataclass and registering it.","intents":["I want to instantiate a specific model variant (e.g., Llama 2 7B) without manually specifying all architectural parameters","I need to compare different model architectures with consistent configuration management","I want to add a new model family to LitGPT by defining its configuration","I need to understand the architectural differences between model families"],"best_for":["developers building applications with multiple model families","researchers comparing model architectures","teams extending LitGPT with custom model variants"],"limitations":["Configuration hub is static — requires code changes to add new models","No automatic configuration discovery from Hugging Face model cards","Configuration parameters are tightly coupled to model implementation","No validation that configuration parameters are compatible with model code"],"requires":["Python 3.9+","PyTorch 2.0+","Model name matching a known configuration OR custom Config definition"],"input_types":["model name (string)","optional configuration overrides (dict)"],"output_types":["Config dataclass instance","instantiated PyTorch model"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litgpt__cap_2","uri":"capability://code.generation.editing.adapter.v1.and.v2.fine.tuning.with.bottleneck.layer.injection","name":"adapter v1 and v2 fine-tuning with bottleneck layer injection","description":"Implements Adapter modules (litgpt/adapter.py and litgpt/adapter_v2.py) that inject small bottleneck layers into transformer blocks, reducing trainable parameters to 0.5-2% of base model size. Adapter V1 uses sequential down-projection → activation → up-projection, while V2 adds parallel residual connections and layer normalization for improved gradient flow. Adapters are inserted after attention and feed-forward layers, allowing task-specific specialization while keeping base weights frozen.","intents":["I want a more parameter-efficient alternative to LoRA with better gradient flow","I need to fine-tune models with explicit bottleneck architectures for interpretability","I want to compare Adapter V1 vs V2 performance on my specific task","I need to deploy multiple task-specific adapters with minimal memory overhead"],"best_for":["multi-task learning with many small adapters","scenarios requiring architectural interpretability of fine-tuning changes","teams comparing parameter-efficient tuning methods"],"limitations":["Adapter inference adds ~3-5% latency per adapter layer due to extra forward passes","Adapter bottleneck dimension requires manual tuning; no principled selection method provided","V2 adapters have more parameters than V1 (typically 2-3x), reducing memory savings","No built-in adapter composition or routing mechanisms for dynamic task selection"],"requires":["Python 3.9+","PyTorch 2.0+","PyTorch Lightning 2.0+","12GB+ VRAM for adapter fine-tuning on 70B models"],"input_types":["pretrained model checkpoint","training dataset","adapter config (bottleneck dimension, insertion points)"],"output_types":["adapter module weights","merged checkpoint (base + adapter)","training logs and metrics"],"categories":["code-generation-editing","model-training","memory-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litgpt__cap_3","uri":"capability://code.generation.editing.full.model.fine.tuning.with.mixed.precision.and.gradient.accumulation","name":"full model fine-tuning with mixed precision and gradient accumulation","description":"Enables end-to-end fine-tuning of all model parameters using PyTorch Lightning's training loop with automatic mixed precision (AMP) in FP16 or BF16, gradient accumulation for effective larger batch sizes, and gradient checkpointing to reduce activation memory. Integrates with FSDP (Fully Sharded Data Parallel) for multi-GPU distributed training, automatically sharding model weights, gradients, and optimizer states across devices. Supports learning rate scheduling, warmup, and weight decay configuration.","intents":["I want to fine-tune a model on a large dataset with all parameters trainable","I need to use multiple GPUs to reduce fine-tuning time for a 70B model","I want to use mixed precision training to reduce memory and speed up training","I need to fine-tune with gradient checkpointing to fit larger models in VRAM"],"best_for":["teams with multi-GPU infrastructure (4+ GPUs)","large-scale domain adaptation requiring full model updates","scenarios where parameter-efficient methods underperform"],"limitations":["Requires 80GB+ VRAM for 70B model full fine-tuning even with gradient checkpointing and mixed precision","FSDP introduces ~10-15% communication overhead on multi-GPU setups due to gradient synchronization","No automatic learning rate scaling for distributed training; requires manual adjustment","Gradient checkpointing reduces memory by ~50% but adds ~20-30% training time overhead"],"requires":["Python 3.9+","PyTorch 2.0+","PyTorch Lightning 2.0+","CUDA 11.8+ with multi-GPU setup (2+ GPUs recommended)","80GB+ total VRAM for 70B model fine-tuning"],"input_types":["pretrained model checkpoint","training dataset (text or instruction format)","training config (learning rate, batch size, epochs)"],"output_types":["fine-tuned model checkpoint","training metrics (loss curves, validation scores)","optimizer state (for resuming training)"],"categories":["code-generation-editing","model-training","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litgpt__cap_4","uri":"capability://code.generation.editing.pretraining.from.scratch.with.custom.datasets.and.3t.token.support","name":"pretraining from scratch with custom datasets and 3t+ token support","description":"Supports training models from random initialization on custom datasets using PyTorch Lightning's distributed training infrastructure. Handles datasets up to 3 trillion tokens via streaming data loading and checkpoint resumption. Includes TinyLlama pretraining example (1.1B model trained on 3T tokens) demonstrating end-to-end pretraining workflow. Integrates with custom DataModules for flexible data loading (raw text, JSON, Parquet, HuggingFace datasets) and supports data shuffling, tokenization, and batching across multiple GPUs.","intents":["I want to pretrain a model from scratch on a custom domain-specific corpus","I need to train on a massive dataset (1T+ tokens) with checkpoint resumption","I want to reproduce TinyLlama or similar small models with custom data","I need to implement custom data loading and preprocessing for specialized domains"],"best_for":["organizations with proprietary datasets requiring custom pretraining","researchers exploring model scaling laws and architecture variations","teams building domain-specific LLMs (legal, medical, code-specific)"],"limitations":["Pretraining 70B+ models requires 100+ GPU-days, making it prohibitively expensive for most teams","No built-in data deduplication or quality filtering; requires external preprocessing","Checkpoint resumption requires careful management of random seeds and data shuffling to avoid data leakage","Tokenization is performed on-the-fly during training, adding ~10-15% training overhead vs pre-tokenized data"],"requires":["Python 3.9+","PyTorch 2.0+","PyTorch Lightning 2.0+","CUDA 11.8+ with 8+ GPUs (recommended for practical pretraining)","1TB+ storage for dataset and checkpoints","Custom DataModule implementation for non-standard data formats"],"input_types":["raw text files, JSON, Parquet, or HuggingFace datasets","model config (architecture, size, hyperparameters)","training config (learning rate, batch size, num_tokens)"],"output_types":["pretrained model checkpoint","training logs (loss, throughput, tokens/second)","tokenized dataset cache (optional)"],"categories":["code-generation-editing","model-training","data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litgpt__cap_5","uri":"capability://code.generation.editing.bidirectional.checkpoint.conversion.between.litgpt.and.huggingface.formats","name":"bidirectional checkpoint conversion between litgpt and huggingface formats","description":"Implements convert_hf_checkpoint.py and convert_lit_checkpoint.py scripts that enable seamless conversion between LitGPT's native checkpoint format and HuggingFace Transformers format. Handles weight mapping, layer name translation, and config serialization/deserialization. Supports converting HuggingFace checkpoints (Llama, Mistral, Phi, etc.) into LitGPT format for training, and exporting LitGPT checkpoints to HuggingFace format for ecosystem compatibility (inference with vLLM, deployment with HuggingFace Inference API).","intents":["I want to download a HuggingFace model and fine-tune it with LitGPT","I need to export my LitGPT-trained model to HuggingFace format for deployment","I want to use LitGPT for training but deploy with vLLM or other HuggingFace-compatible tools","I need to compare models trained with LitGPT vs HuggingFace using the same checkpoint"],"best_for":["teams using both LitGPT and HuggingFace ecosystems","workflows requiring model portability across frameworks","organizations deploying models with multiple inference engines"],"limitations":["Conversion requires exact layer name mapping; custom model architectures need manual conversion logic","Some HuggingFace model variants (e.g., MoE models with custom routing) may not convert cleanly","Conversion is one-way for some model families; bidirectional support depends on architecture similarity","No validation that converted checkpoints produce identical outputs; requires manual verification"],"requires":["Python 3.9+","PyTorch 2.0+","transformers library (HuggingFace)","Model checkpoint in source format (HuggingFace or LitGPT)"],"input_types":["HuggingFace model checkpoint (safetensors or PyTorch format)","LitGPT model checkpoint","model config (for weight mapping)"],"output_types":["converted checkpoint (opposite format)","config file (YAML or JSON)","conversion log (weight mapping details)"],"categories":["code-generation-editing","data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litgpt__cap_6","uri":"capability://code.generation.editing.quantization.with.bitsandbytes.4.bit.and.8.bit.support","name":"quantization with bitsandbytes 4-bit and 8-bit support","description":"Integrates BitsAndBytes quantization library to reduce model memory footprint via 4-bit (NF4) and 8-bit quantization. 4-bit quantization reduces a 70B model to ~6GB VRAM, enabling single-GPU inference and fine-tuning (QLoRA). Supports mixed precision quantization (e.g., quantize attention layers to 4-bit, keep feed-forward in FP16) and automatic dequantization during forward passes. Quantization is applied at model loading time via BitsAndBytes config, preserving model architecture and enabling standard inference APIs.","intents":["I want to run a 70B model on a single 24GB GPU","I need to reduce inference latency by quantizing to 8-bit while maintaining accuracy","I want to enable QLoRA fine-tuning on consumer GPUs","I need to compare 4-bit vs 8-bit quantization trade-offs on my task"],"best_for":["teams with limited GPU resources (single GPU or small clusters)","inference-heavy workloads where memory is the bottleneck","rapid prototyping on consumer hardware"],"limitations":["4-bit quantization introduces ~5-10% accuracy degradation on some tasks (varies by model and domain)","Dequantization during inference adds ~5-10% latency overhead vs FP16","BitsAndBytes quantization is CUDA-specific; no CPU or AMD GPU support","Quantized models cannot be easily converted back to full precision; requires retraining"],"requires":["Python 3.9+","PyTorch 2.0+","BitsAndBytes 0.41+","CUDA 11.8+ (NVIDIA GPU required)","8GB+ VRAM for 4-bit quantization, 16GB+ for 8-bit"],"input_types":["pretrained model checkpoint (FP32, FP16, or BF16)","quantization config (bits, compute_dtype, load_in_4bit/8bit)"],"output_types":["quantized model (in-memory, not saved to disk)","inference outputs (same format as unquantized model)","quantization metrics (memory usage, latency)"],"categories":["code-generation-editing","model-training","memory-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litgpt__cap_7","uri":"capability://code.generation.editing.distributed.training.with.fsdp.and.model.parallelism.across.multi.gpu.and.tpu","name":"distributed training with fsdp and model parallelism across multi-gpu and tpu","description":"Leverages PyTorch Lightning's FSDP (Fully Sharded Data Parallel) integration to automatically shard model weights, gradients, and optimizer states across multiple GPUs or TPUs. Supports both data parallelism (each GPU processes different data) and model parallelism (model layers distributed across devices). Handles gradient synchronization, communication optimization (gradient compression), and automatic checkpoint saving across distributed ranks. Enables training of 405B+ models by combining FSDP with pipeline parallelism.","intents":["I want to train a 70B model across 8 GPUs with automatic weight sharding","I need to reduce per-GPU memory usage by distributing model parameters across devices","I want to train on TPU clusters with automatic distributed setup","I need to implement pipeline parallelism for 405B+ models"],"best_for":["organizations with multi-GPU infrastructure (4+ GPUs)","large-scale pretraining and fine-tuning workflows","teams requiring model parallelism for 100B+ parameter models"],"limitations":["FSDP introduces ~10-15% communication overhead due to all-reduce operations for gradient synchronization","Requires careful tuning of sharding strategy (FULL_SHARD vs SHARD_GRAD_OP) for optimal performance","Debugging distributed training is complex; requires understanding of rank-specific behavior and collective operations","No automatic learning rate scaling; requires manual adjustment for larger batch sizes across more GPUs"],"requires":["Python 3.9+","PyTorch 2.0+","PyTorch Lightning 2.0+","CUDA 11.8+ or TPU runtime","4+ GPUs with high-bandwidth interconnect (NVLink or InfiniBand recommended)","Distributed training knowledge (ranks, world_size, synchronization)"],"input_types":["model checkpoint (or random initialization)","training dataset","FSDP config (sharding strategy, communication backend)"],"output_types":["distributed checkpoint (sharded across ranks)","training metrics (loss, throughput in tokens/second)","distributed logs (per-rank metrics)"],"categories":["code-generation-editing","model-training","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litgpt__cap_8","uri":"capability://text.generation.language.text.generation.with.multiple.decoding.strategies.greedy.sampling.beam.search","name":"text generation with multiple decoding strategies (greedy, sampling, beam search)","description":"Implements generation strategies in the inference module supporting greedy decoding (argmax), temperature-scaled sampling, top-k/top-p filtering, and beam search. Handles prompt formatting via the Prompt system (litgpt/prompts.py) which applies model-specific instruction templates (e.g., Llama Chat, Mistral Instruct). Supports streaming generation (token-by-token output), batch generation, and generation with constraints (max_length, stop tokens). Integrates with the LLM Python API for programmatic text generation.","intents":["I want to generate text using different decoding strategies (greedy vs sampling) and compare quality","I need to apply model-specific prompt formatting (e.g., Llama Chat template) automatically","I want to stream generated tokens in real-time for interactive applications","I need to batch generate completions for multiple prompts efficiently"],"best_for":["inference-heavy applications requiring flexible decoding","interactive chatbot and assistant applications","batch inference pipelines for evaluation and benchmarking"],"limitations":["Beam search is computationally expensive; requires 2-4x inference time vs greedy decoding","No built-in length penalty or diversity penalty for beam search; requires custom implementation","Prompt formatting is model-specific; requires manual template definition for custom models","Streaming generation adds ~5-10% latency overhead due to token-by-token processing"],"requires":["Python 3.9+","PyTorch 2.0+","Pretrained model checkpoint","Tokenizer (HuggingFace or SentencePiece)"],"input_types":["prompt text (string)","generation config (max_length, temperature, top_k, top_p)","model checkpoint"],"output_types":["generated text (string)","token probabilities (optional)","generation metadata (num_tokens, inference_time)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"litgpt__cap_9","uri":"capability://text.generation.language.python.api.llm.class.for.programmatic.model.inference.and.fine.tuning","name":"python api (llm class) for programmatic model inference and fine-tuning","description":"Provides a high-level LLM class that wraps model loading, tokenization, and generation into a simple Python API. Supports loading models from checkpoint paths or HuggingFace hub, automatic device placement (CPU/GPU), and generation via a single generate() method. Integrates with quantization (4-bit, 8-bit) and LoRA adapters transparently. 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