{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-hiyouga--llamafactory","slug":"hiyouga--llamafactory","name":"LlamaFactory","type":"finetune","url":"https://llamafactory.readthedocs.io","page_url":"https://unfragile.ai/hiyouga--llamafactory","categories":["model-training"],"tags":["agent","ai","deepseek","fine-tuning","gemma","gpt","instruction-tuning","large-language-models","llama","llama3","llm","lora","moe","nlp","peft","qlora","quantization","qwen","rlhf","transformers"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-hiyouga--llamafactory__cap_0","uri":"capability://code.generation.editing.unified.multi.model.fine.tuning.with.100.llm.vlm.support","name":"unified multi-model fine-tuning with 100+ llm/vlm support","description":"Provides a single configuration-driven interface to fine-tune 100+ model families (LLaMA, Qwen, GLM, Mistral, Gemma, Yi, DeepSeek, etc.) by abstracting model-specific loading logic through a centralized model registry and adapter system. The framework uses HuggingFace Transformers as the base loader, then applies model-specific patches and configurations via a modular patching system that handles architecture variations, attention mechanisms, and special token handling without requiring separate codebases per model.","intents":["I want to fine-tune a Qwen model using the same config format I used for LLaMA without rewriting training code","I need to support multiple model families in my product but can't maintain separate training pipelines","I want to experiment with different model architectures without learning each framework's unique API"],"best_for":["ML engineers building multi-model training infrastructure","researchers comparing performance across model families","teams migrating between different LLM providers"],"limitations":["Model-specific optimizations may not be as deep as single-model frameworks (e.g., vLLM's inference optimizations are more specialized)","Adding support for a new model family requires understanding LlamaFactory's patching system and model registry","Performance characteristics vary significantly across models; unified config doesn't guarantee equivalent training speed"],"requires":["Python 3.8+","PyTorch 2.0+","HuggingFace Transformers 4.36+","Model weights accessible via HuggingFace Hub or local path"],"input_types":["model_name_or_path (string identifier or local path)","adapter_name_or_path (for loading pre-trained adapters)","YAML/JSON configuration files"],"output_types":["fine-tuned model weights","adapter weights (LoRA/QLoRA/OFT)","merged model checkpoint"],"categories":["code-generation-editing","model-training","multi-model-support"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hiyouga--llamafactory__cap_1","uri":"capability://code.generation.editing.parameter.efficient.fine.tuning.with.lora.qlora.oft.adapter.system","name":"parameter-efficient fine-tuning with lora/qlora/oft adapter system","description":"Implements multiple parameter-efficient fine-tuning (PEFT) methods through a pluggable adapter architecture that wraps model layers without modifying base weights. Supports LoRA (low-rank decomposition), QLoRA (quantized LoRA for 4-bit models), and OFT (orthogonal fine-tuning) by integrating with HuggingFace PEFT library and extending it with custom implementations. The adapter system allows selective application to specific layer types (attention, MLP) and supports merging adapters back into base weights or keeping them separate for inference.","intents":["I want to fine-tune a 70B model on consumer GPUs by reducing trainable parameters from 70B to <1B","I need to maintain multiple task-specific adapters that can be swapped at inference time without reloading the base model","I want to compare LoRA vs QLoRA vs OFT performance on the same model with minimal code changes"],"best_for":["researchers with limited GPU memory (<24GB VRAM)","teams deploying multiple fine-tuned variants of the same base model","practitioners optimizing for inference latency and memory footprint"],"limitations":["LoRA rank/alpha hyperparameters require tuning; suboptimal choices can significantly impact convergence","QLoRA adds ~15-20% training time overhead due to quantization/dequantization operations","Adapter merging is lossy; merged adapters cannot be unmerged to recover original adapter weights","Some model architectures (e.g., MoE models) have limited adapter support"],"requires":["Python 3.8+","peft library (HuggingFace PEFT)","bitsandbytes 0.39+ (for QLoRA)","GPU with 8GB+ VRAM for LoRA, 4GB+ for QLoRA"],"input_types":["adapter_type (lora, qlora, oft)","lora_rank (int, typically 8-64)","lora_alpha (int, typically 16-128)","target_modules (list of layer names to adapt)"],"output_types":["adapter_config.json (adapter metadata)","adapter weights (safetensors or PyTorch format)","merged model weights (optional)"],"categories":["code-generation-editing","model-training","memory-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hiyouga--llamafactory__cap_10","uri":"capability://automation.workflow.model.export.and.adapter.merging.with.format.conversion","name":"model export and adapter merging with format conversion","description":"Enables exporting fine-tuned models and adapters in multiple formats (PyTorch, SafeTensors, GGUF, GPTQ) and merging adapters back into base model weights for deployment. The export system handles format conversion, quantization during export (e.g., exporting to GPTQ format), and adapter merging which combines LoRA weights with base model weights through a weighted sum operation. Supports exporting to HuggingFace Hub for easy sharing, and includes format-specific optimizations (e.g., GGUF export includes quantization and can target specific hardware like CPU or mobile).","intents":["I want to export my fine-tuned model to GGUF format for inference on CPU or mobile devices","I need to merge my LoRA adapter back into the base model weights for deployment","I want to upload my fine-tuned model to HuggingFace Hub so others can use it"],"best_for":["practitioners deploying models to edge devices or resource-constrained environments","teams sharing models via HuggingFace Hub","researchers publishing reproducible fine-tuned models"],"limitations":["Adapter merging is lossy; merged adapters cannot be unmerged","GGUF export requires quantization which may reduce accuracy by 1-3%","GPTQ export requires calibration data; without it, quantization quality suffers","Format conversion can be slow for large models (>30B parameters); may take 10-30 minutes","Some formats (e.g., GGUF) have limited operator support; models with custom operations may not export cleanly"],"requires":["Python 3.8+","HuggingFace Transformers 4.36+","For GGUF: llama-cpp-python or similar","For GPTQ: auto-gptq library","HuggingFace Hub credentials (for uploading)"],"input_types":["model_name_or_path (fine-tuned model)","adapter_name_or_path (LoRA/QLoRA adapter)","export_format (pytorch, safetensors, gguf, gptq)","output_dir (destination for exported model)"],"output_types":["model weights in target format (PyTorch, SafeTensors, GGUF, GPTQ)","config.json (model configuration)","tokenizer files (tokenizer.model, tokenizer.json, etc.)","merged model checkpoint (if adapter merging)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hiyouga--llamafactory__cap_11","uri":"capability://code.generation.editing.custom.optimizer.support.with.galore.badam.and.apollo","name":"custom optimizer support with galore, badam, and apollo","description":"Integrates custom optimizers (GaLore, BAdam, APOLLO) that improve training efficiency beyond standard Adam by reducing memory usage or improving convergence. GaLore (Gradient Low-Rank Projection) projects gradients into a low-rank subspace, reducing optimizer state memory by 50-70%. BAdam (Block-wise Adam) partitions parameters into blocks and maintains separate optimizer states per block, improving convergence on large models. APOLLO applies adaptive learning rates per parameter group. These optimizers are pluggable through the training system and can be selected via configuration.","intents":["I want to reduce optimizer memory usage by 50% using GaLore without changing my training code","I need to improve convergence on a large model using BAdam instead of Adam","I want to compare optimizer performance (Adam vs GaLore vs BAdam) on the same model"],"best_for":["researchers optimizing training efficiency","teams with limited GPU memory looking to reduce optimizer overhead","practitioners experimenting with advanced optimization techniques"],"limitations":["Custom optimizers add complexity; hyperparameter tuning (learning_rate, weight_decay) may differ from Adam","GaLore's low-rank projection adds computational overhead (~10-15% slower per step)","BAdam requires careful block size tuning; suboptimal block sizes can hurt convergence","Not all optimizers are compatible with all training stages (e.g., some may not work with PPO)","Limited documentation and community support compared to standard Adam"],"requires":["Python 3.8+","PyTorch 2.0+","For GaLore: galore-torch library","For BAdam: badam library","For APOLLO: apollo library"],"input_types":["optim_type (adam, adamw, galore, badam, apollo)","learning_rate (float, optimizer-specific defaults)","weight_decay (float)","galore_rank (int, for GaLore)","block_size (int, for BAdam)"],"output_types":["trained model weights","optimizer state (if checkpointing)","training logs with optimizer-specific metrics"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hiyouga--llamafactory__cap_12","uri":"capability://data.processing.analysis.dataset.loading.and.template.system.with.50.format.support","name":"dataset loading and template system with 50+ format support","description":"Provides a flexible dataset loading system that supports 50+ dataset formats (Alpaca, ShareGPT, OpenAI, JSONL, CSV, Parquet, etc.) through a template-based approach that maps raw data to standardized training formats. Each dataset format has a corresponding template that defines how to extract instruction, input, output, and history fields from the raw data. The system handles dataset discovery (from HuggingFace Hub or local paths), automatic format detection, and data validation. Custom templates can be defined in YAML to support new formats without code changes.","intents":["I want to fine-tune a model on my custom dataset without writing data loading code","I need to combine multiple datasets in different formats (Alpaca, ShareGPT, OpenAI) into a single training set","I want to add support for a new dataset format by defining a YAML template"],"best_for":["practitioners with custom datasets in non-standard formats","teams combining datasets from multiple sources","researchers experimenting with different dataset formats"],"limitations":["Template system requires understanding the data structure; complex nested formats may be hard to express","No automatic data quality validation; corrupted or malformed data can silently fail during training","Large datasets (>100GB) require careful memory management; loading entire dataset into memory is not feasible","Dataset format detection is heuristic-based; ambiguous formats may be misdetected","No built-in data augmentation or synthetic data generation"],"requires":["Python 3.8+","Datasets library (HuggingFace)","Pandas (for CSV/Parquet support)","YAML support (built-in)"],"input_types":["dataset_name (HuggingFace Hub identifier or local path)","template (alpaca, sharegpt, openai, jsonl, csv, parquet, or custom YAML)","dataset_config (dict with format-specific options)"],"output_types":["tokenized training sequences","batched tensors with attention masks","dataset statistics (size, token count, etc.)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hiyouga--llamafactory__cap_13","uri":"capability://automation.workflow.training.callbacks.and.monitoring.with.tensorboard.weights.biases.and.custom.metrics","name":"training callbacks and monitoring with tensorboard, weights & biases, and custom metrics","description":"Integrates training callbacks that track metrics, log to external services (TensorBoard, Weights & Biases, Wandb), and trigger custom actions during training. The callback system hooks into the training loop at key points (step, epoch, validation) and enables custom metric computation, early stopping, learning rate scheduling, and model checkpointing. Built-in callbacks include loss tracking, gradient norm monitoring, learning rate logging, and stage-specific metrics (e.g., reward model accuracy, PPO policy divergence). Custom callbacks can be defined by extending a base class.","intents":["I want to monitor training loss, learning rate, and gradient norms in real-time via TensorBoard","I need to log training metrics to Weights & Biases for experiment tracking and comparison","I want to implement early stopping based on validation loss"],"best_for":["researchers tracking experiments and comparing hyperparameters","teams using Weights & Biases for experiment management","practitioners monitoring long-running training jobs"],"limitations":["Callback overhead can add 5-10% training time, especially with frequent logging","TensorBoard/Wandb logging requires network connectivity; offline training won't log to cloud services","Custom metrics require understanding the callback API; complex metrics may be hard to implement","Early stopping requires validation data; not all training stages have validation","Callback state is not automatically saved; custom callbacks must implement checkpointing if needed"],"requires":["Python 3.8+","tensorboard (for TensorBoard logging)","wandb (for Weights & Biases logging)","HuggingFace Transformers 4.36+ (for callback integration)"],"input_types":["report_to (list of logging backends: tensorboard, wandb, etc.)","logging_steps (int, frequency of logging)","save_strategy (steps, epoch, no)","eval_strategy (steps, epoch, no)"],"output_types":["TensorBoard event files","Weights & Biases run logs","training checkpoints (PyTorch format)","metrics JSON (loss, learning_rate, etc.)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hiyouga--llamafactory__cap_2","uri":"capability://code.generation.editing.multi.stage.training.pipeline.with.sft.reward.modeling.and.rlhf.variants","name":"multi-stage training pipeline with sft, reward modeling, and rlhf variants","description":"Orchestrates sequential training stages (pre-training, supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, SimPO) through a stage-aware trainer system that swaps loss functions, data collators, and optimization strategies based on the selected training_stage parameter. Each stage has a dedicated trainer class (SFTTrainer, RewardTrainer, PPOTrainer, etc.) that inherits from HuggingFace Trainer and implements stage-specific logic like preference pair handling for reward models or policy gradient computation for PPO. The configuration system validates stage transitions and manages data format expectations per stage.","intents":["I want to implement RLHF training: first SFT on instruction data, then train a reward model, then PPO optimize against it","I need to compare DPO vs PPO vs ORPO on the same base model to see which alignment method works best","I want to train a model on domain-specific data then align it with human preferences using preference pairs"],"best_for":["ML engineers implementing full RLHF pipelines","researchers comparing alignment methods (DPO, PPO, KTO, ORPO, SimPO)","teams building instruction-tuned models with preference optimization"],"limitations":["PPO training is computationally expensive (~3-5x cost of SFT) and requires careful hyperparameter tuning (learning_rate, beta, gamma)","Reward modeling stage requires preference pair data which is expensive to collect; no automatic generation","Stage transitions are sequential; cannot parallelize reward modeling and SFT stages","DPO/ORPO/SimPO require preference pairs but don't require a separate reward model, creating trade-offs in data requirements vs. training cost"],"requires":["Python 3.8+","PyTorch 2.0+","HuggingFace Transformers 4.36+","For PPO: trl library (Transformers Reinforcement Learning)","GPU with 24GB+ VRAM for PPO training"],"input_types":["training_stage (sft, rm, ppo, dpo, kto, orpo, simpo)","instruction data (for SFT)","preference pairs (for RM, DPO, ORPO, SimPO)","reference model weights (for DPO/ORPO/SimPO)"],"output_types":["fine-tuned model weights","reward model weights (if RM stage)","training logs with stage-specific metrics","merged model checkpoint"],"categories":["code-generation-editing","model-training","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hiyouga--llamafactory__cap_3","uri":"capability://automation.workflow.declarative.yaml.json.configuration.system.with.validation.and.argument.parsing","name":"declarative yaml/json configuration system with validation and argument parsing","description":"Centralizes all training, inference, and data parameters through a unified configuration parser (hparams/parser.py) that accepts YAML/JSON files and validates inputs against typed argument classes (ModelArguments, DataArguments, TrainingArguments, etc.). The parser converts flat configuration dictionaries into strongly-typed Python dataclasses, performs cross-field validation (e.g., ensuring adapter_name_or_path exists if adapter_type is set), and distributes validated arguments to the appropriate subsystems. This eliminates the need for command-line argument parsing and enables reproducible training via version-controlled config files.","intents":["I want to version control my training configuration and reproduce results months later without remembering command-line flags","I need to validate my config before starting a 24-hour training job to catch errors early","I want to generate training configs programmatically from a template without manually editing YAML"],"best_for":["ML engineers managing multiple training experiments","teams implementing MLOps pipelines with config-driven training","researchers publishing reproducible training recipes"],"limitations":["YAML/JSON configs can become verbose for complex multi-stage pipelines with many hyperparameters","Validation errors are reported at parse time; some invalid combinations only fail during training (e.g., incompatible quantization + adapter combinations)","No built-in config templating or inheritance; users must manually duplicate common settings across configs","Config schema is tightly coupled to Python dataclasses; schema changes require code updates"],"requires":["Python 3.8+","PyYAML library","JSON support (built-in)"],"input_types":["YAML configuration file","JSON configuration file","command-line arguments (override config file values)"],"output_types":["validated argument objects (ModelArguments, DataArguments, TrainingArguments, etc.)","error messages with field-level validation details"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hiyouga--llamafactory__cap_4","uri":"capability://image.visual.multimodal.data.processing.with.image.video.and.audio.support","name":"multimodal data processing with image, video, and audio support","description":"Extends the data pipeline to handle multimodal inputs (images, videos, audio) alongside text through specialized data processors that convert visual/audio tokens into embeddings compatible with LLM training. The system uses vision transformers (e.g., CLIP, Qwen-VL) to encode images and videos into token sequences, and audio processors to convert audio into spectrograms or embeddings. Data templates define how to interleave text and multimodal tokens (e.g., <image>token_sequence</image>text), and the collator handles variable-length multimodal sequences with padding/truncation.","intents":["I want to fine-tune a vision-language model (VLM) on image-text pairs without writing custom data loading code","I need to train a model that can process both images and text in the same sequence","I want to add video understanding to my LLM by fine-tuning on video frames + captions"],"best_for":["researchers building vision-language models","teams training multimodal assistants","practitioners fine-tuning models like Qwen-VL, LLaVA, or Gemini-style architectures"],"limitations":["Multimodal training requires significantly more GPU memory than text-only training (2-3x increase)","Video processing adds latency to data loading; frame extraction and encoding can become bottleneck","Audio support is limited; no built-in speech recognition or audio-to-text conversion","Multimodal data templates are model-specific; switching models may require template adjustments","No automatic image/video quality validation; corrupted files can silently fail during training"],"requires":["Python 3.8+","vision_transformers library (for image encoding)","Pillow (for image processing)","ffmpeg (for video frame extraction)","GPU with 24GB+ VRAM for multimodal training"],"input_types":["image files (PNG, JPEG, WebP)","video files (MP4, MOV, AVI)","audio files (WAV, MP3)","JSON/JSONL with image/video paths and captions","multimodal data templates (YAML)"],"output_types":["tokenized multimodal sequences with image/video embeddings","batched tensors with aligned text and visual tokens","training logs with multimodal loss metrics"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hiyouga--llamafactory__cap_5","uri":"capability://code.generation.editing.quantization.aware.training.with.2.4.8.bit.precision.and.bitsandbytes.integration","name":"quantization-aware training with 2/4/8-bit precision and bitsandbytes integration","description":"Integrates bitsandbytes library to enable training with reduced precision (2-bit, 4-bit, 8-bit) through quantization-aware training (QAT) and post-training quantization (PTQ). The system loads models in quantized format using bitsandbytes' quantization kernels, then applies LoRA adapters on top of frozen quantized weights. For 4-bit quantization, it uses NF4 (normalized float 4) format which preserves more information than standard INT4. The training loop computes gradients only for adapter weights while keeping base model weights frozen in quantized format, reducing memory usage by 75-90% compared to full precision training.","intents":["I want to fine-tune a 70B model on a single 24GB GPU using 4-bit quantization","I need to reduce model size for deployment while maintaining accuracy through quantization","I want to compare training with different quantization levels (8-bit vs 4-bit vs 2-bit) to find the accuracy/speed trade-off"],"best_for":["researchers with limited GPU memory (<24GB VRAM)","teams deploying models on edge devices or resource-constrained environments","practitioners optimizing for inference latency and memory footprint"],"limitations":["Quantization introduces information loss; 4-bit models typically lose 1-3% accuracy vs. full precision","bitsandbytes quantization is CUDA-specific; no CPU or AMD GPU support","Quantized models cannot be easily converted back to full precision; quantization is lossy","Training with quantization is slower than full precision due to quantization/dequantization overhead (~15-20% slower)","Some operations (e.g., certain attention mechanisms) may not support quantized inputs, requiring fallback to full precision"],"requires":["Python 3.8+","PyTorch 2.0+","bitsandbytes 0.39+","CUDA 11.8+ (for GPU support)","GPU with 4GB+ VRAM (for 4-bit QLoRA)"],"input_types":["load_in_4bit or load_in_8bit (boolean flags)","bnb_4bit_compute_dtype (torch.float16, torch.bfloat16, torch.float32)","bnb_4bit_quant_type (nf4 or fp4)","bnb_4bit_use_double_quant (boolean, for nested quantization)"],"output_types":["quantized model weights (in bitsandbytes format)","adapter weights (LoRA/QLoRA)","training logs with quantization-specific metrics"],"categories":["code-generation-editing","model-training","memory-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hiyouga--llamafactory__cap_6","uri":"capability://automation.workflow.distributed.training.with.deepspeed.and.fsdp.support","name":"distributed training with deepspeed and fsdp support","description":"Enables distributed training across multiple GPUs/TPUs through integration with DeepSpeed and PyTorch FSDP (Fully Sharded Data Parallel). The system detects available hardware and automatically configures the appropriate distributed backend, handling gradient accumulation, gradient synchronization, and model sharding across devices. DeepSpeed integration includes support for ZeRO-1/2/3 optimization stages which partition optimizer states, gradients, and model parameters across devices to reduce per-GPU memory usage. FSDP provides pure PyTorch distributed training without external dependencies.","intents":["I want to train a 70B model across 8 GPUs using DeepSpeed ZeRO-3 to fit it in memory","I need to scale training from 1 GPU to 4 GPUs without changing my training code","I want to use FSDP for distributed training without installing DeepSpeed"],"best_for":["teams with multi-GPU infrastructure (4+ GPUs)","researchers training large models (>30B parameters)","practitioners optimizing for training speed and memory efficiency"],"limitations":["DeepSpeed ZeRO-3 adds communication overhead; effective speedup is typically 60-80% of theoretical maximum","Distributed training requires careful synchronization; debugging is harder than single-GPU training","Not all model architectures are compatible with FSDP (e.g., models with dynamic control flow)","DeepSpeed configuration is complex; suboptimal settings can significantly impact performance","Gradient accumulation with distributed training requires careful step counting to ensure correct batch size semantics"],"requires":["Python 3.8+","PyTorch 2.0+","For DeepSpeed: deepspeed 0.10+","For FSDP: PyTorch built-in (no additional dependencies)","Multi-GPU setup with NVLink or high-bandwidth interconnect (recommended)"],"input_types":["ddp_backend (nccl, gloo, mpi)","deepspeed_config_file (JSON with ZeRO configuration)","fsdp_config (dict with FSDP parameters)","num_processes (number of GPUs/TPUs)"],"output_types":["distributed model checkpoint (sharded across devices)","training logs with per-device metrics","merged model weights (consolidated from shards)"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hiyouga--llamafactory__cap_7","uri":"capability://tool.use.integration.inference.engine.abstraction.with.huggingface.transformers.vllm.sglang.and.ktransformers","name":"inference engine abstraction with huggingface transformers, vllm, sglang, and ktransformers","description":"Provides a pluggable inference backend system that abstracts away differences between inference engines (HuggingFace Transformers, vLLM, SGLang, KTransformers) through a unified ChatModel interface. Each backend implements the same generation API but with different optimization strategies: HuggingFace Transformers is the baseline, vLLM adds paged attention and continuous batching for throughput, SGLang adds structured generation and multi-modal support, KTransformers adds kernel-level optimizations for specific models. The system auto-selects the best backend based on model type and available hardware, or allows manual override via configuration.","intents":["I want to switch from HuggingFace Transformers inference to vLLM for 10x throughput improvement without changing my application code","I need to serve a model with structured generation (JSON output) and want to use SGLang's native support","I want to benchmark inference speed across different backends (Transformers vs vLLM vs SGLang) on my model"],"best_for":["teams building inference services that need to optimize for throughput or latency","researchers comparing inference backend performance","practitioners deploying models with specific inference requirements (structured output, streaming, etc.)"],"limitations":["Not all backends support all models; vLLM has limited support for MoE models, SGLang requires specific model modifications","Backend-specific optimizations may not apply to all model architectures; gains vary by model","Switching backends may require retuning generation hyperparameters (temperature, top_p, etc.) for consistent output","Some backends have higher memory overhead (e.g., vLLM's paged attention requires more GPU memory for KV cache)","Structured generation support varies by backend; not all backends support all constraint types"],"requires":["Python 3.8+","HuggingFace Transformers 4.36+","For vLLM: vllm 0.3+","For SGLang: sglang 0.1+","For KTransformers: ktransformers (model-specific)"],"input_types":["inference_backend (transformers, vllm, sglang, ktransformers)","model_name_or_path (string identifier or local path)","generation_config (temperature, top_p, max_tokens, etc.)"],"output_types":["generated text sequences","generation metadata (tokens, logits, etc.)","structured output (JSON, if using SGLang)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hiyouga--llamafactory__cap_8","uri":"capability://tool.use.integration.openai.compatible.api.server.for.model.serving","name":"openai-compatible api server for model serving","description":"Exposes fine-tuned models through an OpenAI-compatible REST API server that implements the Chat Completions and Embeddings endpoints, enabling drop-in replacement for OpenAI's API. The server uses the inference engine abstraction to support multiple backends (vLLM, SGLang, etc.) and handles request routing, batching, and streaming responses. Clients written for OpenAI's API can use LlamaFactory's server without modification, reducing integration friction. The server supports authentication via API keys and includes request logging and metrics collection.","intents":["I want to serve my fine-tuned model with an API that's compatible with OpenAI's client libraries","I need to replace OpenAI's API with my own model in my application without rewriting client code","I want to run a local inference server that my team can query via HTTP"],"best_for":["teams deploying models as services","practitioners building applications that use OpenAI's API and want to switch to local models","researchers comparing model performance via a standard API interface"],"limitations":["Not all OpenAI API features are supported (e.g., function calling, vision endpoints are partial)","Response format may differ slightly from OpenAI's API (e.g., model field contains local model name, not OpenAI model ID)","Streaming responses have higher latency than OpenAI's API due to local processing","No built-in rate limiting or quota management; requires external load balancer for production use","Authentication is basic (API key in header); no OAuth or advanced security features"],"requires":["Python 3.8+","FastAPI or similar web framework","GPU with sufficient VRAM for model inference","OpenAI Python client (for testing)"],"input_types":["POST /v1/chat/completions (JSON with messages, model, temperature, etc.)","POST /v1/embeddings (JSON with input text and model)","GET /v1/models (list available models)"],"output_types":["JSON response with choices (text completions)","JSON response with embeddings (vector representations)","Server-sent events (for streaming responses)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-hiyouga--llamafactory__cap_9","uri":"capability://automation.workflow.web.ui.llama.board.for.training.chat.and.evaluation","name":"web ui (llama board) for training, chat, and evaluation","description":"Provides a browser-based interface (LLaMA Board) built with Gradio/Streamlit that enables non-technical users to configure training jobs, monitor progress, run inference, and evaluate models without command-line interaction. The UI includes a training configuration builder that generates YAML configs, a real-time training monitor showing loss curves and metrics, a chat interface for testing models, and an evaluation dashboard for comparing model outputs. 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