yangjianxin1/Firefly
Firefly
Firefly is a training framework and inference engine for large language models. It functions as a toolkit for pre-training and fine-tuning various open-weight architectures, providing a system for model alignment and parameter-efficient fine-tuning.
The project includes utilities for merging adapter weights back into base models to create standalone files. It also provides a model alignment toolkit to format training data according to specific prompt templates, ensuring conversational consistency across different models.
The framework supports distributed model training and preference-based optimization. Inference capabilities include multi-turn dialogue execution with low-precision memory optimizations to reduce hardware requirements.
Features
- Language Model Training - Provides a comprehensive framework for pre-training, supervised fine-tuning, and preference optimization of large language models.
- Distributed Training - Implements data and model parallelism to train large neural networks across multiple hardware devices.
- Inference Execution Engines - Provides a runtime environment for executing multi-turn conversations with low-precision memory optimizations.
- Adapter Weight Mergers - Provides a utility for combining trained adapter weights back into a base model to create standalone files.
- Large Language Model Training Frameworks - Provides a specialized framework for training transformer-based models across single or multi-GPU environments.
- Alignment Toolkits - Ships a collection of tools for optimizing language models to follow human preferences and formatting consistency.
- Parameter Efficient Fine-Tuning - Implements memory-efficient adaptation by updating only a small subset of model parameters.
- Parameter-Efficient Training Toolkits - Provides a comprehensive system for reducing memory overhead by updating small subsets of parameters during model fine-tuning.
- Weight Merging - Provides utilities to combine learned adapter weights into a single unified model file for deployment.
- Adapter Merging - Combines trained adapter weights back into the base model to create standalone files for faster inference.
- LLM Inference Optimization - Optimizes the delivery and memory usage of LLM inference to reduce hardware requirements for deployment.
- Chat Template Configurations - Provides configurations to match training data to specific chat templates for conversational consistency.
- Half-Precision Inference - Uses reduced numerical precision for weights and activations to optimize memory consumption during inference.
- Weight Merging Utilities - Integrates adapter matrices into base model weights to eliminate inference latency and simplify model management.
- Training Acceleration Engines - Integrates specialized optimization libraries like Unsloth to reduce training duration and memory usage.
- Preference-Based Model Alignments - Refines model behavior using preference-based optimization to align outputs with human preferences.
- Multi-Turn Serving Optimizations - Provides optimized serving for stateful multi-turn interactions using memory-efficient inference.
- Dialogue Dataset Structuring - Converts raw conversational data into structured schemas and templates for model training.
- Prompt Template Alignment - Formats raw training datasets into specific prompt structures to ensure conversational consistency across different models.
- Fine-Tuning Frameworks - Training tool supporting a wide variety of large models.
- Text LLM Models - Project providing data, training code, and fine-tuned models for various architectures.
- Fine-Tuning Frameworks - Training tool supporting a wide variety of large models.
- Training Frameworks - Comprehensive framework for training and fine-tuning LLMs.
Star history
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