{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-unslothai--unsloth","slug":"unslothai--unsloth","name":"unsloth","type":"webapp","url":"https://unsloth.ai/docs","page_url":"https://unfragile.ai/unslothai--unsloth","categories":["model-training"],"tags":["agent","deepseek","fine-tuning","gemma","gemma3","gpt-oss","llama","llama3","llm","llms","mistral","openai","qwen","reinforcement-learning","self-hosted","text-to-speech","tts","ui","unsloth"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-unslothai--unsloth__cap_0","uri":"capability://code.generation.editing.custom.triton.kernel.accelerated.attention.dispatch","name":"custom-triton-kernel-accelerated-attention-dispatch","description":"Implements a dynamic attention dispatch system using custom Triton kernels that automatically select optimized attention implementations (FlashAttention, PagedAttention, or standard) based on model architecture, hardware, and sequence length. The system patches transformer attention layers at model load time, replacing standard PyTorch implementations with kernel-optimized versions that reduce memory bandwidth and compute overhead. This achieves 2-5x faster training throughput compared to standard transformers library implementations.","intents":["Reduce training time for large language models without changing training code","Lower VRAM consumption during fine-tuning to fit larger models on consumer GPUs","Accelerate inference latency for deployed models with optimized attention computation"],"best_for":["ML engineers fine-tuning open models on limited GPU memory (8GB-40GB)","Teams building cost-efficient training pipelines for Llama, Gemma, Qwen models","Researchers optimizing inference performance on edge devices"],"limitations":["Triton kernel compilation adds 30-60 seconds to first model load","Custom kernels only support NVIDIA GPUs (CUDA compute capability 7.0+); no AMD/CPU fallback","Attention dispatch logic requires model architecture to be in supported registry; custom architectures fall back to standard implementation","FP8 quantization kernels have numerical precision trade-offs requiring careful validation on downstream tasks"],"requires":["NVIDIA GPU with CUDA 11.8+","Python 3.9+","PyTorch 2.0+","Triton 2.0+ (auto-installed)","transformers library 4.36+"],"input_types":["model_name (HuggingFace identifier)","model_weights (safetensors or PyTorch format)","quantization_config (optional: int4, int8, fp8)"],"output_types":["patched_model (PyTorch nn.Module with kernel-optimized layers)","performance_metrics (throughput, memory usage)"],"categories":["code-generation-editing","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unslothai--unsloth__cap_1","uri":"capability://memory.knowledge.model.architecture.registry.with.automatic.name.resolution","name":"model-architecture-registry-with-automatic-name-resolution","description":"Maintains a centralized model registry mapping HuggingFace model identifiers to architecture-specific optimization profiles (Llama, Gemma, Mistral, Qwen, DeepSeek, etc.). The loader performs automatic name resolution using regex patterns and HuggingFace config inspection to detect model family, then applies architecture-specific patches for attention, normalization, and quantization. Supports vision models, mixture-of-experts architectures, and sentence transformers through specialized submodules that extend the base registry.","intents":["Load any HuggingFace model and automatically apply optimal Unsloth optimizations without manual configuration","Support new model architectures by extending the registry with custom patch definitions","Handle model naming variations and aliases across different HuggingFace organizations"],"best_for":["Developers wanting one-line model loading with automatic optimization detection","Teams managing diverse model portfolios across Llama, Gemma, Qwen, and proprietary architectures","Researchers experimenting with emerging open models without rewriting training code"],"limitations":["Registry must be manually updated when new model architectures are released; no automatic discovery","Name resolution relies on regex patterns and config inspection, which can fail for non-standard model naming","Custom architectures not in registry fall back to standard transformers behavior with no optimization","Vision model support limited to models with explicit vision architecture definitions (e.g., LLaVA, Qwen-VL)"],"requires":["HuggingFace transformers 4.36+","Model config.json accessible (local or via HuggingFace Hub)","Python 3.9+"],"input_types":["model_id (string: 'meta-llama/Llama-2-7b', 'google/gemma-7b', etc.)","quantization_type (optional: 'int4', 'int8', 'fp8')","max_seq_length (optional: integer)"],"output_types":["model (PyTorch nn.Module with architecture-specific patches applied)","tokenizer (PreTrainedTokenizer)","architecture_metadata (dict with detected family, quantization strategy)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unslothai--unsloth__cap_10","uri":"capability://tool.use.integration.huggingface.hub.integration.for.model.sharing.and.versioning","name":"huggingface-hub-integration-for-model-sharing-and-versioning","description":"Provides seamless integration with HuggingFace Hub for uploading trained models, managing versions, and tracking training metadata. The system handles authentication, model card generation, and automatic versioning of model weights and LoRA adapters. Supports pushing models as private or public repositories, managing multiple versions, and downloading models for inference. Integrates with Unsloth's model loading pipeline to enable one-command model sharing.","intents":["Upload trained models to HuggingFace Hub for sharing and collaboration","Manage multiple versions of trained models with automatic versioning","Track training metadata and model lineage for reproducibility"],"best_for":["Researchers publishing models and wanting to share with the community","Teams collaborating on model development across organizations","Organizations managing internal model registries on HuggingFace Hub"],"limitations":["Requires HuggingFace account and API token; authentication must be configured","Large model uploads (>50GB) may timeout or fail; no built-in retry logic","Model card generation is basic; custom markdown requires manual editing","No built-in model versioning; versions are managed via git branches or manual naming","Private repositories require HuggingFace Pro account; no built-in access control"],"requires":["HuggingFace account with API token","huggingface-hub library 0.16.0+","Internet connection for Hub access","Git installed (for model card and metadata management)"],"input_types":["model (PyTorch model or model identifier)","repo_id (string: 'username/model-name')","private (boolean: whether to create private repository)","model_card_metadata (dict with description, tags, training details)"],"output_types":["hub_url (string: URL to model on HuggingFace Hub)","model_card (markdown file with model description)","version_info (dict with commit hash, timestamp, training config)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unslothai--unsloth__cap_11","uri":"capability://automation.workflow.multi.gpu.distributed.training.with.deepspeed.integration","name":"multi-gpu-distributed-training-with-deepspeed-integration","description":"Provides integration with DeepSpeed for distributed training across multiple GPUs and nodes, enabling training of larger models with reduced per-GPU memory footprint. The system handles DeepSpeed configuration, gradient accumulation, and synchronization across devices. Supports ZeRO-2 and ZeRO-3 optimization stages for memory efficiency. Integrates with Unsloth's kernel optimizations to maintain performance benefits across distributed setups.","intents":["Train larger models by distributing computation across multiple GPUs","Reduce per-GPU memory footprint using DeepSpeed ZeRO optimizations","Scale training to multiple nodes for very large models (70B+)"],"best_for":["Teams with access to multi-GPU clusters (8+ GPUs)","Organizations training very large models (70B+ parameters)","Researchers studying distributed training efficiency"],"limitations":["DeepSpeed integration adds complexity; requires careful configuration of ZeRO stages and gradient accumulation","Communication overhead between GPUs can reduce scaling efficiency; optimal for 8+ GPUs","Debugging distributed training is harder; errors may occur on specific ranks and be hard to reproduce","DeepSpeed checkpoint format is incompatible with standard PyTorch; requires special loading logic","Not all Unsloth optimizations are compatible with DeepSpeed; some kernel fusions may be disabled","Requires NCCL or Gloo for inter-GPU communication; network bandwidth can be a bottleneck"],"requires":["Multiple NVIDIA GPUs (8+ recommended) with CUDA 11.8+","DeepSpeed 0.10.0+","PyTorch 2.0+ with distributed training support","NCCL 2.14+ for GPU communication","Python 3.9+"],"input_types":["model (PyTorch model)","train_dataset (HuggingFace Dataset or DataLoader)","deepspeed_config (JSON: ZeRO stage, gradient accumulation, optimizer config)","num_gpus (integer: number of GPUs to use)"],"output_types":["trained_model (model state dict from rank 0)","deepspeed_checkpoint (directory with distributed checkpoint)","training_metrics (loss, throughput, communication overhead)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unslothai--unsloth__cap_12","uri":"capability://text.generation.language.fast.inference.with.vllm.backend.and.kv.cache.optimization","name":"fast-inference-with-vllm-backend-and-kv-cache-optimization","description":"Integrates vLLM backend for high-throughput inference with optimized KV cache management, enabling batch inference and continuous batching. The system manages KV cache allocation, implements paged attention for memory efficiency, and supports multiple inference backends (transformers, vLLM, GGUF). Provides a unified inference API that abstracts backend selection and handles batching, streaming, and tool calling.","intents":["Serve models with high throughput and low latency using vLLM's continuous batching","Reduce memory footprint during inference with optimized KV cache management","Support batch inference and streaming responses for production deployments"],"best_for":["Teams deploying models in production with high request volume","Organizations optimizing inference cost and latency","Researchers benchmarking inference performance across backends"],"limitations":["vLLM backend requires NVIDIA GPU; no CPU inference support","KV cache optimization adds complexity; debugging cache issues is difficult","Streaming responses require client-side handling of partial tokens","Batch size and sequence length must be tuned per model; no automatic optimization","vLLM may not support all model architectures; fallback to transformers backend required","Memory overhead of KV cache grows with batch size and sequence length; OOM errors possible with large batches"],"requires":["NVIDIA GPU with CUDA 11.8+","vLLM 0.2.0+","PyTorch 2.0+","transformers 4.36+","Python 3.9+"],"input_types":["model (PyTorch model or model identifier)","inference_backend (string: 'vllm', 'transformers', 'gguf')","batch_size (integer: number of requests to batch)","max_tokens (integer: maximum tokens to generate per request)"],"output_types":["generated_text (string or list of strings for batch)","inference_metrics (dict with latency, throughput, cache hit rate)","token_ids (list of integers for each generated token)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unslothai--unsloth__cap_2","uri":"capability://code.generation.editing.quantization.aware.lora.training.with.kernel.fusion","name":"quantization-aware-lora-training-with-kernel-fusion","description":"Enables efficient fine-tuning of quantized models (int4, int8, fp8) by fusing LoRA computation with quantization kernels, eliminating the need to dequantize weights during forward passes. The system integrates PEFT's LoRA adapter framework with custom Triton kernels that compute (W_quantized @ x + LoRA_A @ LoRA_B @ x) in a single fused operation. This reduces memory bandwidth and enables training on quantized models with minimal overhead compared to full-precision LoRA training.","intents":["Fine-tune quantized models (4-bit, 8-bit) on consumer GPUs with minimal memory overhead","Achieve comparable convergence to full-precision LoRA training while using 50-70% less VRAM","Merge trained LoRA adapters back into quantized base models for deployment"],"best_for":["Individual developers and small teams with limited GPU memory (8GB-24GB)","Cost-conscious organizations training multiple model variants on shared infrastructure","Researchers studying quantization-aware adaptation techniques"],"limitations":["Quantization introduces numerical precision loss; downstream task performance may degrade 1-3% vs full-precision baseline","FP8 quantization requires careful hyperparameter tuning (learning rate, warmup steps) to avoid divergence","LoRA rank and alpha must be tuned per model; no automatic configuration","Merging quantized LoRA adapters back into base model requires dequantization, negating some memory savings","Only supports LoRA; QLoRA and other adapter types not integrated with quantization kernels"],"requires":["NVIDIA GPU with CUDA 11.8+","PyTorch 2.0+","PEFT library 0.4.0+","bitsandbytes 0.41.0+ (for int4/int8 quantization)","Triton 2.0+"],"input_types":["model (quantized PyTorch model)","train_dataset (list of dicts with 'input_ids', 'attention_mask', 'labels')","lora_config (dict: rank, alpha, target_modules, dropout)","quantization_config (dict: bits, compute_dtype, bnb_4bit_quant_type)"],"output_types":["trained_model (model with LoRA adapters in memory)","adapter_weights (safetensors file with LoRA A/B matrices)","training_metrics (loss, perplexity, throughput)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unslothai--unsloth__cap_3","uri":"capability://data.processing.analysis.sample.packing.and.padding.free.training","name":"sample-packing-and-padding-free-training","description":"Implements a data loading strategy that concatenates multiple training examples into a single sequence up to max_seq_length, eliminating padding tokens and reducing wasted computation. The system uses a custom collate function that packs examples with special tokens as delimiters, then masks loss computation to ignore padding and cross-example boundaries. This increases GPU utilization and training throughput by 20-40% compared to standard padded batching, particularly effective for variable-length datasets.","intents":["Increase training throughput and GPU utilization without changing model architecture or hyperparameters","Reduce training time for datasets with highly variable sequence lengths","Improve data efficiency by eliminating wasted computation on padding tokens"],"best_for":["Teams training on large, diverse datasets with variable sequence lengths (e.g., instruction-tuning, chat data)","Cost-sensitive organizations optimizing training efficiency on cloud GPUs","Researchers studying the impact of packing strategies on model convergence"],"limitations":["Sample packing changes the effective batch composition; may require retuning learning rate and warmup steps","Loss masking adds ~5-10% overhead to backward pass computation","Incompatible with some training techniques (e.g., sequence-level loss weighting, per-example gradient clipping)","Requires careful handling of special tokens and chat templates to avoid cross-example contamination","Debugging is harder because examples are concatenated; error messages reference packed sequences rather than original examples"],"requires":["PyTorch 2.0+","transformers 4.36+","Custom collate function (provided by Unsloth)","Dataset with consistent tokenization"],"input_types":["dataset (HuggingFace Dataset or list of dicts with 'input_ids', 'attention_mask')","max_seq_length (integer, typically 2048-4096)","packing_enabled (boolean)"],"output_types":["packed_dataset (DataLoader yielding packed batches)","packing_metadata (dict with packing ratio, examples per batch)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unslothai--unsloth__cap_4","uri":"capability://automation.workflow.gguf.export.and.quantization.pipeline","name":"gguf-export-and-quantization-pipeline","description":"Provides an end-to-end pipeline for exporting trained models to GGUF format with optional quantization (Q4_K_M, Q5_K_M, Q8_0, etc.), enabling deployment on CPU and edge devices via llama.cpp. The export process converts PyTorch weights to GGUF tensors, applies quantization kernels, and generates a GGUF metadata file with model config, tokenizer, and chat templates. Supports merging LoRA adapters into base weights before export, producing a single deployable artifact.","intents":["Export fine-tuned models to GGUF format for CPU inference and edge deployment","Quantize models to reduce file size and memory footprint for mobile/embedded devices","Create standalone model artifacts that don't require Python or PyTorch at inference time"],"best_for":["Teams deploying models on CPU-only or resource-constrained devices (Raspberry Pi, mobile phones)","Organizations distributing models to non-technical users without Python environment setup","Researchers benchmarking model performance across different quantization levels"],"limitations":["GGUF export is one-way; cannot convert GGUF back to PyTorch without external tools","Quantization introduces 2-5% accuracy loss depending on quantization level; Q4_K_M is more aggressive than Q8_0","Export process requires loading full model into memory; no streaming export for very large models (>100B parameters)","Chat templates must be manually verified in exported GGUF; some custom templates may not be fully supported","Inference speed on CPU is significantly slower than GPU; GGUF is optimized for latency, not throughput"],"requires":["PyTorch model (trained or pretrained)","Python 3.9+","transformers 4.36+","llama-cpp-python or similar GGUF runtime (for inference validation)","Sufficient disk space for temporary model copies during export"],"input_types":["model (PyTorch nn.Module or HuggingFace model identifier)","tokenizer (PreTrainedTokenizer)","quantization_type (string: 'q4_k_m', 'q5_k_m', 'q8_0', 'f16')","lora_adapters (optional: list of LoRA checkpoint paths to merge)"],"output_types":["gguf_file (binary GGUF format file)","quantization_metadata (dict with quantization stats, file size reduction)","export_log (text log with conversion steps and validation results)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unslothai--unsloth__cap_5","uri":"capability://planning.reasoning.reinforcement.learning.training.with.dpo.and.ppo","name":"reinforcement-learning-training-with-dpo-and-ppo","description":"Integrates reinforcement learning training methods (DPO, PPO) with Unsloth's optimized kernels, enabling preference-based fine-tuning and reward model training. The system implements DPO (Direct Preference Optimization) loss computation with efficient gradient computation, and provides a PPO training loop that samples from the model, computes rewards, and updates weights using policy gradient methods. Both methods leverage Unsloth's kernel optimizations for 2-5x faster training compared to standard implementations.","intents":["Fine-tune models using human preference data without requiring a separate reward model (DPO)","Train models with reinforcement learning to optimize for custom reward functions (PPO)","Implement RLHF workflows with reduced computational overhead"],"best_for":["Teams implementing preference-based fine-tuning with DPO for alignment","Researchers experimenting with RL-based model optimization","Organizations building custom reward models and training pipelines"],"limitations":["DPO training requires paired preference data (chosen vs rejected responses); data collection is non-trivial","PPO training is computationally expensive; requires sampling, reward computation, and policy updates in each step","Reward model training requires separate labeled dataset; no automatic reward signal generation","Convergence is sensitive to hyperparameters (learning rate, KL penalty, reward scaling); requires careful tuning","No built-in support for multi-GPU distributed RL training; single-GPU only","Gradient accumulation with RL training can cause memory spikes during sampling phase"],"requires":["PyTorch 2.0+","transformers 4.36+","trl library 0.7.0+ (for DPO/PPO implementations)","Preference dataset (for DPO) or reward model (for PPO)","NVIDIA GPU with 24GB+ VRAM for PPO (due to sampling overhead)"],"input_types":["model (PyTorch language model)","train_dataset (list of dicts with 'prompt', 'chosen', 'rejected' for DPO; or 'prompt', 'response' for PPO)","reward_model (optional: separate model for PPO reward computation)","training_config (dict: learning_rate, num_epochs, beta for DPO, kl_penalty for PPO)"],"output_types":["trained_model (model with RL-optimized weights)","training_metrics (loss, reward, KL divergence, policy gradient norm)","checkpoint (model state at best validation reward)"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unslothai--unsloth__cap_6","uri":"capability://automation.workflow.studio.web.ui.with.interactive.training.and.inference","name":"studio-web-ui-with-interactive-training-and-inference","description":"Provides a full-featured web interface (React frontend + FastAPI backend) for training, inference, and model management without command-line usage. The backend orchestrates training via subprocess workers, manages model lifecycle (loading, inference, export), and exposes REST APIs for chat, tool calling, and model configuration. The frontend includes a chat playground, training progress visualization, recipe editor, and model browser. Built on FastAPI with subprocess worker pattern for process isolation and fault tolerance.","intents":["Enable non-technical users to fine-tune and deploy models via a graphical interface","Provide a unified workspace for training, testing, and exporting models","Expose model inference via REST APIs for integration with external applications"],"best_for":["Non-technical founders and product managers prototyping LLM applications","Teams wanting a unified training + inference interface without CLI expertise","Organizations building internal model management platforms"],"limitations":["Web UI adds latency compared to direct Python API; REST API calls add 50-200ms overhead per request","Subprocess worker pattern isolates processes but adds inter-process communication overhead","No built-in multi-user authentication or role-based access control; suitable for single-user or trusted environments","Training progress visualization is approximate; actual training may diverge from UI estimates","Chat playground does not support streaming responses; full response must be generated before display","Model uploads limited to HuggingFace Hub models; local model files require manual file system access"],"requires":["Python 3.9+","Node.js 16+ (for frontend development)","FastAPI 0.100+","React 18+ (frontend)","NVIDIA GPU with CUDA 11.8+ (for training/inference)","8GB+ RAM for backend service"],"input_types":["model_id (HuggingFace identifier or local path)","training_data (CSV/JSON file or HuggingFace dataset identifier)","training_config (JSON: learning_rate, num_epochs, batch_size, lora_rank)","inference_prompt (text string for chat playground)"],"output_types":["trained_model (saved to HuggingFace Hub or local storage)","inference_response (text generated by model)","training_logs (JSON with loss, throughput, memory usage over time)","REST API responses (JSON)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unslothai--unsloth__cap_7","uri":"capability://data.processing.analysis.chat.template.and.tokenizer.management","name":"chat-template-and-tokenizer-management","description":"Provides utilities for managing chat templates and tokenizers across different model families, automatically detecting and applying the correct chat format for inference. The system maintains a registry of chat templates (ChatML, Llama2, Alpaca, etc.), applies them during tokenization to format prompts correctly, and handles special tokens (BOS, EOS, PAD) according to model specifications. Supports custom chat templates and validates template syntax before application.","intents":["Automatically format chat prompts in the correct template for any model without manual formatting","Handle special tokens and chat history correctly across different model families","Validate and debug chat template issues during inference"],"best_for":["Developers building chatbot applications with multiple model backends","Teams managing diverse model deployments with different chat formats","Researchers studying the impact of prompt formatting on model behavior"],"limitations":["Chat template detection relies on model config inspection; non-standard models may not be auto-detected","Custom chat templates require manual definition; no automatic template inference from examples","Template validation is syntactic only; semantic errors (e.g., missing role markers) are not caught","Some models have undocumented or non-standard chat formats; manual override required","Chat history management is stateless; long conversation context requires manual truncation"],"requires":["transformers 4.36+","Model config with chat_template field (or manual template definition)","Python 3.9+"],"input_types":["model_id (HuggingFace identifier)","messages (list of dicts with 'role' and 'content' keys)","custom_template (optional: Jinja2 template string)"],"output_types":["formatted_prompt (string ready for tokenization)","token_ids (list of integers)","template_metadata (dict with template name, special tokens)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unslothai--unsloth__cap_8","uri":"capability://data.processing.analysis.synthetic.data.generation.for.vision.and.language.models","name":"synthetic-data-generation-for-vision-and-language-models","description":"Provides utilities for generating synthetic training data for vision-language models (VLMs) and language models, including image captioning, visual question answering, and instruction-following data. The system integrates with existing VLMs to generate synthetic captions and QA pairs, formats data according to model-specific requirements, and handles image processing (resizing, normalization). Supports batch generation and dataset composition from multiple sources.","intents":["Generate synthetic training data for vision-language models without manual annotation","Create diverse instruction-following datasets for model fine-tuning","Augment existing datasets with synthetic examples to improve coverage"],"best_for":["Teams building VLM applications without large labeled image datasets","Researchers studying synthetic data quality and its impact on model performance","Organizations augmenting limited human-annotated data with synthetic examples"],"limitations":["Synthetic data quality depends on the generator model; biases in generator propagate to training data","Generation is computationally expensive; creating large datasets requires significant GPU time","No automatic quality filtering; generated data may contain errors or inconsistencies","Image processing is limited to standard transformations; complex augmentations not supported","No built-in diversity metrics; no guarantee that generated data covers the full problem space"],"requires":["Vision-language model for generation (e.g., LLaVA, Qwen-VL)","Image dataset (local files or HuggingFace dataset)","PyTorch 2.0+","transformers 4.36+","NVIDIA GPU with 24GB+ VRAM for VLM inference"],"input_types":["image_dataset (list of image paths or PIL Images)","generation_template (string: 'caption', 'vqa', 'instruction')","generator_model (VLM model identifier)","num_samples (integer: number of synthetic examples to generate)"],"output_types":["synthetic_dataset (list of dicts with 'image', 'text', 'metadata')","generation_logs (dict with success rate, generation time per example)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-unslothai--unsloth__cap_9","uri":"capability://automation.workflow.recipe.studio.visual.editor.for.training.workflows","name":"recipe-studio-visual-editor-for-training-workflows","description":"Provides a visual editor for composing training workflows as directed acyclic graphs (DAGs) of data processing, model loading, training, and export steps. The editor allows drag-and-drop composition of recipes, parameter configuration via UI forms, and execution via the backend. Recipes are serialized as JSON and can be version-controlled, shared, and reused across projects. The backend executes recipes via a DAG runner that handles dependencies and error propagation.","intents":["Enable non-technical users to compose complex training workflows without code","Create reusable training recipes that can be shared across teams","Visualize and debug training pipelines with dependency graphs"],"best_for":["Non-technical product managers and domain experts designing training workflows","Teams standardizing training processes across multiple projects","Organizations building internal MLOps platforms"],"limitations":["Visual editor is limited to predefined recipe components; custom Python code not supported","DAG execution is sequential; no built-in parallelization across independent steps","Error handling is basic; failures in one step stop the entire recipe without rollback","Recipe versioning is manual; no automatic tracking of recipe changes or lineage","No built-in monitoring or alerting for long-running recipes"],"requires":["React 18+ (frontend)","FastAPI 0.100+ (backend)","Python 3.9+","Node.js 16+ (for frontend development)"],"input_types":["recipe_definition (JSON DAG with nodes and edges)","node_parameters (dict with step-specific configuration)","input_datasets (list of dataset identifiers)"],"output_types":["recipe_execution_log (JSON with step results, timing, errors)","trained_model (output of final training step)","recipe_artifact (JSON serialization for version control)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["NVIDIA GPU with CUDA 11.8+","Python 3.9+","PyTorch 2.0+","Triton 2.0+ (auto-installed)","transformers library 4.36+","HuggingFace transformers 4.36+","Model config.json accessible (local or via HuggingFace Hub)","HuggingFace account with API token","huggingface-hub library 0.16.0+","Internet connection for Hub access"],"failure_modes":["Triton kernel compilation adds 30-60 seconds to first model load","Custom kernels only support NVIDIA GPUs (CUDA compute capability 7.0+); no AMD/CPU fallback","Attention dispatch logic requires model architecture to be in supported registry; custom architectures fall back to standard implementation","FP8 quantization kernels have numerical precision trade-offs requiring careful validation on downstream tasks","Registry must be manually updated when new model architectures are released; no automatic discovery","Name resolution relies on regex patterns and config inspection, which can fail for non-standard model naming","Custom architectures not in registry fall back to standard transformers behavior with no optimization","Vision model support limited to models with explicit vision architecture definitions (e.g., LLaVA, Qwen-VL)","Requires HuggingFace account and API token; authentication must be configured","Large model uploads (>50GB) may timeout or fail; no built-in retry logic","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4420699426898362,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:57:19.180Z","last_commit":"2026-05-02T20:34:06Z"},"community":{"stars":63491,"forks":5573,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=unslothai--unsloth","compare_url":"https://unfragile.ai/compare?artifact=unslothai--unsloth"}},"signature":"KRxRuzNc1RMAPPOlzI2Zn1Rd9ZD/GxtHoRLAem6p0DQHVdA15O/yi3biDVFWqLgLCIRIKJFoz5wdX4y9TnCzAw==","signedAt":"2026-06-22T01:51:47.049Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/unslothai--unsloth","artifact":"https://unfragile.ai/unslothai--unsloth","verify":"https://unfragile.ai/api/v1/verify?slug=unslothai--unsloth","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}