{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"axolotl","slug":"axolotl","name":"Axolotl","type":"repo","url":"https://github.com/OpenAccess-AI-Collective/axolotl","page_url":"https://unfragile.ai/axolotl","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"axolotl__cap_0","uri":"capability://automation.workflow.yaml.based.training.recipe.configuration","name":"yaml-based training recipe configuration","description":"Declarative configuration system that translates YAML training recipes into executable fine-tuning pipelines. Uses a schema-driven approach to validate and parse training parameters (model architecture, learning rates, batch sizes, optimization strategies) into Python objects that drive the training loop. Eliminates boilerplate by centralizing all hyperparameters, data paths, and training strategies in a single human-readable file that can be version-controlled and shared across teams.","intents":["I want to define a complete fine-tuning job without writing Python training loops","I need to version-control and reproduce training configurations across different runs","I want to quickly experiment with different hyperparameter combinations by editing config files"],"best_for":["ML engineers and researchers who prefer declarative over imperative training code","Teams building reproducible fine-tuning pipelines with version control","Non-Python-expert practitioners who want to avoid writing training loops"],"limitations":["Complex custom training logic beyond standard supervised fine-tuning requires Python overrides","YAML schema validation errors can be cryptic without detailed error messages","No built-in schema IDE support — requires external YAML linting tools"],"requires":["Python 3.9+","PyYAML library","Valid model identifier (HuggingFace model card or local path)"],"input_types":["YAML configuration files","model identifiers (string)","dataset paths (string)"],"output_types":["Python training configuration objects","validated hyperparameter dictionaries"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"axolotl__cap_1","uri":"capability://code.generation.editing.multi.architecture.model.fine.tuning.with.unified.interface","name":"multi-architecture model fine-tuning with unified interface","description":"Abstraction layer that handles fine-tuning across diverse model architectures (LLaMA, Mistral, Phi, Qwen, etc.) through a single training pipeline. Internally detects model architecture from HuggingFace model cards, applies architecture-specific tokenization and attention patterns, and routes training through the appropriate PyTorch modules. Supports both base models and instruction-tuned variants without requiring separate training scripts per architecture.","intents":["I want to fine-tune different model architectures without rewriting training code for each one","I need to switch between LLaMA, Mistral, and Phi models while keeping the same training recipe","I want to ensure my training pipeline works across model families without manual architecture-specific tweaks"],"best_for":["Researchers comparing fine-tuning results across multiple model architectures","Teams building model-agnostic fine-tuning infrastructure","Practitioners who want to avoid architecture-specific training code"],"limitations":["Custom attention mechanisms or novel architectures not in HuggingFace transformers require manual integration","Architecture detection relies on HuggingFace model config — proprietary models may not be auto-detected","Some architecture-specific optimizations (e.g., Flash Attention for certain models) must be explicitly enabled in config"],"requires":["HuggingFace transformers library 4.30+","Model weights accessible via HuggingFace Hub or local path","PyTorch 2.0+ for optimal performance"],"input_types":["model identifiers (HuggingFace model card string)","local model paths","training datasets (HuggingFace datasets or local files)"],"output_types":["fine-tuned model weights","adapter weights (for LoRA/QLoRA)","merged model checkpoints"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"axolotl__cap_10","uri":"capability://planning.reasoning.validation.and.early.stopping.with.custom.metrics","name":"validation and early stopping with custom metrics","description":"Integrated validation loop that evaluates model performance on held-out data at configurable intervals during training. Supports custom evaluation metrics (perplexity, BLEU, exact match, F1) and early stopping based on validation performance. Automatically saves best-performing checkpoints and logs validation metrics to WandB. Handles metric computation across distributed training setups with proper synchronization.","intents":["I want to validate my model on a held-out set during training without writing evaluation code","I need to stop training early if validation loss stops improving to save compute","I want to track custom metrics (e.g., BLEU, exact match) alongside standard loss metrics"],"best_for":["Researchers running long training jobs and wanting to avoid overfitting","Teams with limited compute budgets needing early stopping","Practitioners tracking task-specific metrics beyond loss"],"limitations":["Custom metric implementations require Python code — not fully configuration-driven","Validation inference is synchronous and can add significant overhead (10-30% of training time)","Early stopping patience is fixed — no adaptive patience based on metric variance","Metric computation across distributed training requires careful synchronization — bugs can cause deadlocks"],"requires":["Validation dataset in same format as training data","Metric implementations (built-in: loss, perplexity; custom: user-defined)","PyTorch 1.13+ for distributed metric synchronization"],"input_types":["validation dataset","metric selection and configuration","early stopping patience threshold"],"output_types":["validation metrics per epoch","best checkpoint based on validation performance","early stopping signal"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"axolotl__cap_11","uri":"capability://data.processing.analysis.instruction.tuning.dataset.formatting.and.template.system","name":"instruction-tuning dataset formatting and template system","description":"Specialized data formatting system for instruction-tuning workflows that converts raw user/assistant conversation data into model-compatible prompt sequences. Supports multiple prompt templates (Alpaca, ChatML, Llama2, Mistral, etc.) with automatic template selection based on model architecture. Handles multi-turn conversations, system prompts, and special token insertion. Validates prompt formatting and provides debugging output for malformed data.","intents":["I want to format my instruction-tuning dataset in the correct prompt template for my model automatically","I need to support multiple prompt formats (Alpaca, ChatML, etc.) without rewriting data processing code","I want to validate that my prompts are formatted correctly before training"],"best_for":["Teams building instruction-tuned models with diverse data sources","Practitioners working with multiple model families requiring different prompt formats","Researchers comparing instruction-tuning approaches across models"],"limitations":["Built-in templates are limited to common formats — novel prompt structures require custom template definition","Template auto-selection is heuristic-based — may select wrong template for non-standard models","Multi-turn conversation handling is basic — complex conversation structures require manual formatting","No built-in validation for prompt quality (e.g., detecting truncated responses)"],"requires":["Instruction-tuning dataset with user/assistant fields","Model identifier for template auto-selection","Optional: custom template definitions in Python"],"input_types":["raw instruction-tuning data (JSON with user/assistant fields)","prompt template selection (auto or explicit)","optional system prompt"],"output_types":["formatted prompt sequences","tokenized training examples","formatting validation reports"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"axolotl__cap_12","uri":"capability://automation.workflow.batch.size.and.gradient.accumulation.optimization","name":"batch size and gradient accumulation optimization","description":"Automatically calculates effective batch size based on per-device batch size, number of GPUs, and gradient accumulation steps. Axolotl handles gradient accumulation logic transparently, allowing users to specify desired effective batch size in YAML and automatically computing accumulation steps. This enables training with large effective batch sizes on limited GPU memory.","intents":["Train with large effective batch sizes (1024+) on limited GPU memory","Automatically calculate gradient accumulation steps from desired batch size","Maintain consistent effective batch size across different GPU configurations","Optimize batch size for convergence without manual calculation"],"best_for":["Teams training on consumer GPUs with limited VRAM","Practitioners optimizing batch size for convergence","Researchers comparing batch size sensitivity across models"],"limitations":["Gradient accumulation adds training time overhead (proportional to accumulation steps)","Large accumulation steps may cause gradient staleness issues","Effective batch size must be divisible by number of GPUs — no automatic rounding","Accumulation steps are fixed — no dynamic adjustment based on loss"],"requires":["transformers library 4.30+","PyTorch 2.0+","Python 3.9+"],"input_types":["YAML config with per_device_train_batch_size, gradient_accumulation_steps","Number of GPUs"],"output_types":["Effective batch size calculation","Configured gradient accumulation"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"axolotl__cap_13","uri":"capability://automation.workflow.model.architecture.specific.optimizations.flash.attention.rope.scaling","name":"model architecture-specific optimizations (flash attention, rope scaling)","description":"Applies architecture-specific optimizations automatically: Flash Attention v2 for faster attention computation, RoPE (Rotary Position Embedding) scaling for longer context windows, and other model-specific tweaks. Axolotl detects model architecture and applies relevant optimizations via transformers library integrations. Flash Attention reduces attention complexity from O(n²) to O(n) with minimal accuracy loss.","intents":["Speed up attention computation by 2-3x using Flash Attention v2","Extend context window beyond training length using RoPE scaling","Apply architecture-specific optimizations automatically without manual code","Reduce memory footprint of attention layers"],"best_for":["Teams training on long-context tasks (>4K tokens)","Practitioners optimizing training speed on attention-heavy models","Researchers comparing optimization impact on model quality"],"limitations":["Flash Attention requires CUDA 11.8+ and specific GPU architectures (A100, H100, RTX 4090)","RoPE scaling may reduce model quality if scaling factor is too aggressive","Not all model architectures support Flash Attention — fallback to standard attention","Optimization benefits vary by model size and sequence length"],"requires":["CUDA 11.8+ for Flash Attention","transformers library 4.30+","flash-attn library 2.0+ (for Flash Attention v2)","Compatible GPU (A100, H100, RTX 4090)"],"input_types":["Model architecture (detected from HuggingFace model ID)","YAML config with optimization flags"],"output_types":["Optimized model with Flash Attention and RoPE scaling applied","Training logs showing speedup metrics"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"axolotl__cap_2","uri":"capability://automation.workflow.lora.and.qlora.parameter.efficient.fine.tuning","name":"lora and qlora parameter-efficient fine-tuning","description":"Implements Low-Rank Adaptation (LoRA) and Quantized LoRA (QLoRA) through integration with the PEFT (Parameter-Efficient Fine-Tuning) library. Automatically injects trainable low-rank decomposition matrices into model attention and linear layers while freezing base model weights. For QLoRA, additionally quantizes base model weights to 4-bit precision using bitsandbytes, reducing memory footprint by 75%+ while maintaining training quality. Configuration-driven rank selection, alpha scaling, and target module specification allow fine-grained control over adapter architecture.","intents":["I want to fine-tune large models on consumer GPUs with limited VRAM","I need to train multiple task-specific adapters from a single base model without duplicating weights","I want to reduce fine-tuning memory requirements from 80GB to 20GB while maintaining model quality"],"best_for":["Researchers and practitioners with limited GPU memory (single 24GB or 40GB GPUs)","Teams building multi-task systems where each task needs a separate adapter","Cost-conscious organizations wanting to fine-tune on smaller hardware"],"limitations":["QLoRA inference requires bitsandbytes library — not all deployment environments support it","LoRA rank selection is empirical — no principled method to choose optimal rank without experimentation","Merging LoRA adapters back into base model requires additional inference-time overhead if not merged during training","QLoRA training is slower than full fine-tuning due to quantization/dequantization overhead (~15-20% slower)"],"requires":["PEFT library (peft>=0.4.0)","bitsandbytes library for QLoRA (bitsandbytes>=0.39.0)","CUDA 11.8+ for bitsandbytes support","GPU with at least 8GB VRAM for QLoRA, 16GB for LoRA"],"input_types":["base model identifier","training dataset","LoRA configuration (rank, alpha, target modules)"],"output_types":["LoRA adapter weights (.safetensors or .bin)","adapter configuration files","merged model weights (optional)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"axolotl__cap_3","uri":"capability://automation.workflow.multi.gpu.distributed.training.orchestration","name":"multi-gpu distributed training orchestration","description":"Abstracts distributed training complexity through automatic detection of available GPUs and configuration of PyTorch Distributed Data Parallel (DDP) or DeepSpeed backends. Handles gradient accumulation, mixed-precision training (FP16/BF16), and synchronization across devices without requiring manual distributed training code. Supports both single-node multi-GPU and multi-node setups through environment variable detection and automatic rank/world-size configuration.","intents":["I want to scale training from 1 GPU to 8 GPUs without rewriting training code","I need to use gradient accumulation to simulate larger batch sizes on limited hardware","I want mixed-precision training to reduce memory and speed up training automatically"],"best_for":["Teams with multi-GPU clusters wanting to scale training without distributed training expertise","Researchers needing gradient accumulation for large effective batch sizes","Practitioners optimizing training speed and memory through mixed-precision"],"limitations":["DeepSpeed integration requires additional configuration and is not auto-enabled","Multi-node training requires manual NCCL environment variable setup (MASTER_ADDR, MASTER_PORT, RANK, WORLD_SIZE)","Gradient accumulation adds training time proportional to accumulation steps","Mixed-precision training (BF16) may cause numerical instability on older GPU architectures"],"requires":["PyTorch 1.13+ with CUDA support","NCCL 2.10+ for multi-GPU communication","Multiple GPUs (2+) or multi-node setup with proper networking","Optional: DeepSpeed library for advanced distributed strategies"],"input_types":["training configuration with batch_size and gradient_accumulation_steps","model and dataset","optional DeepSpeed config"],"output_types":["trained model weights synchronized across all GPUs","training logs with per-GPU metrics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"axolotl__cap_4","uri":"capability://data.processing.analysis.intelligent.data.preprocessing.and.tokenization.pipeline","name":"intelligent data preprocessing and tokenization pipeline","description":"Automated data loading and preprocessing system that handles multiple input formats (JSON, CSV, Parquet, HuggingFace datasets) and applies architecture-specific tokenization. Supports dataset concatenation, filtering, and sampling through configuration. Implements prompt templating for instruction-tuning datasets, automatically formatting user/assistant exchanges into model-compatible sequences. Handles special tokens, padding, and truncation with configurable strategies (e.g., 'right' padding for causal LMs, 'max_length' truncation).","intents":["I want to load diverse data formats and automatically tokenize them for my model without writing data loaders","I need to format instruction-tuning data (user/assistant pairs) into the correct prompt template for my model","I want to handle datasets larger than memory through streaming and batching"],"best_for":["Practitioners working with multiple data formats who want unified preprocessing","Teams building instruction-tuned models with custom prompt templates","Researchers needing reproducible data preprocessing across experiments"],"limitations":["Custom data transformations beyond built-in templates require Python code extensions","Prompt templating is limited to predefined formats — complex multi-turn conversations require manual template definition","Tokenization is synchronous — very large datasets may cause memory spikes during preprocessing","No built-in data validation or quality checks (e.g., detecting truncated sequences)"],"requires":["HuggingFace datasets library","Model tokenizer (from HuggingFace or custom)","Input data in supported format (JSON, CSV, Parquet, or HuggingFace dataset ID)"],"input_types":["JSON files","CSV/Parquet files","HuggingFace dataset identifiers","local dataset directories"],"output_types":["tokenized PyTorch datasets","attention masks and token type IDs","preprocessed batches ready for training"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"axolotl__cap_5","uri":"capability://automation.workflow.quantization.aware.training.with.gptq.and.gguf.export","name":"quantization-aware training with gptq and gguf export","description":"Integrates post-training quantization through GPTQ (Generative Pre-trained Transformer Quantization) and GGUF (GPT-Generated Unified Format) export pipelines. Supports 4-bit and 8-bit quantization with configurable group sizes and dynamic activation quantization. After fine-tuning, automatically exports models to GGUF format for CPU inference or GPTQ format for GPU inference with minimal accuracy loss. Quantization parameters are configuration-driven, allowing experimentation without code changes.","intents":["I want to quantize my fine-tuned model to 4-bit for faster inference and smaller file sizes","I need to export my model to GGUF format for CPU-only deployment","I want to compare quantization strategies (4-bit vs 8-bit) without retraining"],"best_for":["Teams deploying models on edge devices or CPU-only environments","Practitioners needing to reduce model size from 70GB to 17GB for inference","Researchers comparing quantization impact on fine-tuned models"],"limitations":["GPTQ quantization requires calibration data and is time-consuming (can take hours for large models)","GGUF export is one-way — cannot easily convert back to original precision","Quantization accuracy varies by model architecture — some models degrade significantly at 4-bit","GGUF inference performance depends on llama.cpp implementation, not guaranteed to match original"],"requires":["auto-gptq library for GPTQ quantization","llama-cpp-python or similar for GGUF inference","Calibration dataset for GPTQ (typically 128-256 samples)","Sufficient disk space for intermediate quantized models"],"input_types":["fine-tuned model weights","quantization configuration (bits, group_size, desc_act)","optional calibration dataset"],"output_types":["GPTQ quantized model (.safetensors)","GGUF format model (.gguf)","quantization metadata and statistics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"axolotl__cap_6","uri":"capability://automation.workflow.experiment.tracking.and.metrics.logging.with.wandb.integration","name":"experiment tracking and metrics logging with wandb integration","description":"Built-in integration with Weights & Biases (WandB) for real-time training metrics visualization, hyperparameter logging, and experiment comparison. Automatically logs loss curves, learning rates, gradient norms, and custom metrics to WandB dashboards. Supports local logging fallback and configuration-driven metric selection. Enables reproducibility through automatic logging of training configuration, model architecture, and dataset metadata to experiment records.","intents":["I want to visualize training progress in real-time without writing logging code","I need to compare multiple fine-tuning runs with different hyperparameters","I want to automatically log all training metadata for reproducibility and audit trails"],"best_for":["Research teams running multiple experiments and needing comparison dashboards","Practitioners who want real-time training monitoring without custom logging","Organizations requiring audit trails and reproducibility documentation"],"limitations":["WandB integration requires internet connectivity and WandB account","Logging overhead adds ~5-10% to training time for large-scale runs","Custom metrics require manual logging code — not all metrics are auto-logged","WandB free tier has storage limits (20GB) — large experiments may require paid plan"],"requires":["WandB account and API key (or local logging mode)","wandb Python library (wandb>=0.13.0)","Internet connectivity for cloud logging (optional: local mode available)"],"input_types":["training configuration","model and dataset metadata","training metrics (loss, accuracy, etc.)"],"output_types":["WandB experiment URLs","training dashboards with visualizations","experiment comparison reports","downloadable metrics CSV"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"axolotl__cap_7","uri":"capability://automation.workflow.checkpoint.management.and.model.merging","name":"checkpoint management and model merging","description":"Automated checkpoint saving, loading, and resumption system that persists model state, optimizer state, and training metadata at configurable intervals. Supports resuming training from any checkpoint without data reprocessing. Includes model merging utilities for combining LoRA adapters back into base models, converting between formats (SafeTensors, PyTorch, HuggingFace), and creating inference-ready artifacts. Handles checkpoint cleanup to manage disk space on long training runs.","intents":["I want to resume training after a GPU failure without losing progress or reprocessing data","I need to merge my LoRA adapters back into the base model for deployment","I want to save only the best checkpoint based on validation metrics to save disk space"],"best_for":["Teams running long training jobs on unstable infrastructure","Practitioners deploying LoRA-trained models and needing to merge adapters","Researchers comparing checkpoints at different training stages"],"limitations":["Checkpoint resumption requires exact same training configuration — config changes may cause loading errors","Model merging is synchronous and memory-intensive — merging 70B models requires 140GB+ RAM","Checkpoint storage is disk-intensive — full checkpoints can be 100GB+ for large models","No built-in checkpoint versioning or rollback — manual cleanup required"],"requires":["Sufficient disk space for checkpoints (2-3x model size recommended)","PyTorch and transformers libraries for checkpoint loading","Optional: safetensors library for efficient checkpoint I/O"],"input_types":["training configuration","model and optimizer state","training metadata (step, epoch, metrics)"],"output_types":["checkpoint directories with model/optimizer/metadata","merged model weights","inference-ready model artifacts"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"axolotl__cap_8","uri":"capability://planning.reasoning.custom.loss.functions.and.training.objectives","name":"custom loss functions and training objectives","description":"Extensible training objective system supporting standard supervised fine-tuning (SFT), DPO (Direct Preference Optimization), and custom loss functions. Allows configuration-driven selection of training objectives without code changes. Supports weighted loss combinations for multi-task training and custom loss implementations through Python function registration. Handles special token masking (e.g., ignoring padding tokens in loss calculation) automatically based on model configuration.","intents":["I want to use DPO (Direct Preference Optimization) for preference-aligned fine-tuning without implementing it from scratch","I need to combine multiple loss functions for multi-task training","I want to implement a custom loss function and integrate it into the training pipeline"],"best_for":["Researchers experimenting with novel training objectives (DPO, IPO, etc.)","Teams building preference-aligned models with human feedback","Practitioners needing multi-task training with weighted loss combinations"],"limitations":["DPO implementation requires paired preference data (chosen/rejected) — not all datasets have this format","Custom loss functions require Python code — not fully configuration-driven","Loss weighting is manual — no automatic balancing between multiple objectives","Some advanced objectives (e.g., RLHF) are not built-in and require external integration"],"requires":["Training dataset in appropriate format (SFT: instruction/response pairs; DPO: chosen/rejected pairs)","Python 3.9+ for custom loss implementations","PyTorch 1.13+ for loss computation"],"input_types":["training objective selection (sft, dpo, custom)","loss configuration (weights, masking strategy)","training dataset in objective-specific format"],"output_types":["trained model weights optimized for selected objective","loss curves and training metrics per objective"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"axolotl__cap_9","uri":"capability://automation.workflow.inference.ready.model.export.and.deployment.preparation","name":"inference-ready model export and deployment preparation","description":"Post-training export pipeline that prepares fine-tuned models for inference deployment. Automatically converts models to optimized formats (SafeTensors, ONNX, TensorRT), generates inference configs, and bundles tokenizers with model weights. Supports exporting both full models and LoRA adapters, with optional quantization during export. Generates deployment-ready artifacts including model cards, usage examples, and configuration files for popular inference frameworks (vLLM, TGI, llama.cpp).","intents":["I want to export my fine-tuned model in a format ready for production inference","I need to generate deployment configs and documentation for my model automatically","I want to export my model to multiple formats (SafeTensors, ONNX, GGUF) without manual conversion"],"best_for":["ML engineers preparing models for production deployment","Teams building model serving infrastructure","Practitioners sharing fine-tuned models on HuggingFace Hub"],"limitations":["ONNX export requires model-specific conversion code — not all architectures are supported","TensorRT export is NVIDIA-specific and requires CUDA toolkit","Inference config generation is template-based — complex serving scenarios require manual config","Model card generation is basic — requires manual editing for comprehensive documentation"],"requires":["Fine-tuned model weights","Tokenizer files (usually auto-downloaded from HuggingFace)","Optional: ONNX Runtime for ONNX export","Optional: TensorRT for GPU-optimized inference"],"input_types":["fine-tuned model path","export format selection (safetensors, onnx, gguf, etc.)","optional quantization config"],"output_types":["exported model files in target format","inference configuration files","model cards and documentation","deployment examples for vLLM/TGI/llama.cpp"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"axolotl__headline","uri":"capability://model.training.llm.fine.tuning.toolkit","name":"llm fine-tuning toolkit","description":"Axolotl is a streamlined tool designed for fine-tuning large language models (LLMs) using a YAML-based configuration, supporting various training methods and architectures.","intents":["best LLM fine-tuning toolkit","LLM fine-tuning for multi-GPU training","how to fine-tune LLMs with YAML","top tools for training language models","efficient LLM training solutions"],"best_for":["researchers","developers"],"limitations":["requires familiarity with LLMs"],"requires":["Python","PyTorch"],"input_types":["text data","YAML configuration"],"output_types":["fine-tuned model"],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"low","permissions":["Python 3.9+","PyYAML library","Valid model identifier (HuggingFace model card or local path)","HuggingFace transformers library 4.30+","Model weights accessible via HuggingFace Hub or local path","PyTorch 2.0+ for optimal performance","Validation dataset in same format as training data","Metric implementations (built-in: loss, perplexity; custom: user-defined)","PyTorch 1.13+ for distributed metric synchronization","Instruction-tuning dataset with user/assistant fields"],"failure_modes":["Complex custom training logic beyond standard supervised fine-tuning requires Python overrides","YAML schema validation errors can be cryptic without detailed error messages","No built-in schema IDE support — requires external YAML linting tools","Custom attention mechanisms or novel architectures not in HuggingFace transformers require manual integration","Architecture detection relies on HuggingFace model config — proprietary models may not be auto-detected","Some architecture-specific optimizations (e.g., Flash Attention for certain models) must be explicitly enabled in config","Custom metric implementations require Python code — not fully configuration-driven","Validation inference is synchronous and can add significant overhead (10-30% of training time)","Early stopping patience is fixed — no adaptive patience based on metric variance","Metric computation across distributed training requires careful synchronization — bugs can cause deadlocks","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.690Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=axolotl","compare_url":"https://unfragile.ai/compare?artifact=axolotl"}},"signature":"Tz66xRiU0doyVEm54oGvCmdOi5UI+I124MC//1QpUY8IO4rBA7i5xZJAS63xKpz0S+OL4qW5xIKevHvNCWk+CQ==","signedAt":"2026-06-20T04:05:02.684Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/axolotl","artifact":"https://unfragile.ai/axolotl","verify":"https://unfragile.ai/api/v1/verify?slug=axolotl","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"}}