effective_llm_alignment

Effective LLM Alignment Toolkit

Stars: 105

Visit

This is a super customizable, concise, user-friendly, and efficient toolkit for training and aligning LLMs. It provides support for various methods such as SFT, Distillation, DPO, ORPO, CPO, SimPO, SMPO, Non-pair Reward Modeling, Special prompts basket format, Rejection Sampling, Scoring using RM, Effective FAISS Map-Reduce Deduplication, LLM scoring using RM, NER, CLIP, Classification, and STS. The toolkit offers key libraries like PyTorch, Transformers, TRL, Accelerate, FSDP, DeepSpeed, and tools for result logging with wandb or clearml. It allows mixing datasets, generation and logging in wandb/clearml, vLLM batched generation, and aligns models using the SMPO method.

README:

🎉 Toolkit for Effective LLM Alignment

✨ Description and Features

This is a super customizable, concise, user-friendly, and efficient toolkit for training and aligning LLMs. To get started, simply define a YAML configuration with parameters from HF TrainingArguments and some specific parameters for each method.

🛠️ Toolkit Foundations

Key Libraries:

Core: PyTorch, Transformers, TRL
Distributed Training: Accelerate, FSDP, DeepSpeed (Zero 2/3)
Acceleration: vLLM, Flash Attention, SDPA, Liger Kernel (for fused CrossEntropy in SFT)
Build and Installation: Poetry
Result Logging: Choose between wandb or clearml

📚 Supported Methods

SFT: With the possibility to disable loss on unwanted message roles
Distillation: Options include KL Div, JS Div, SLIM, Earth Mover, MSE, Soft CE, Cosine, Alpha-Beta Div
DPO: All TRL options (IPO, SLic-HF, RPO, etc)
ORPO: All TRL options
CPO and SimPO: All TRL options
SMPO: Our own most stable alignment method (details below)
Non-pair Reward Modeling: With margins and centring support from TRL
Special prompts basket format: Our prompts basket format (vi-basket) allows to generate dialogs with follow-ups and system-prompts.
Rejection Sampling: Effective async preference dataset generation using vLLM and RM
Scoring using RM: Anync generation and scoring of answers based on some prompts basket, using external RM and vLLM
Effective FAISS Map-Reduce Deduplication: We have tools for Map-Reduce based deduplication for dense embeddings. It can deduplicate VERY large datasets in parallel.
LLM scoring using RM: Use RM model and your dataset to caluclate RM scores statistics to compare models.
NER, CLIP, Classification, STS: Not native to this toolkit but tested (Work in Progress)

📌 Additional Features

All datasets follow the JSON lines format and conform to Hugging Face standards (storing messages in the format [{'role': ..., 'content': ...}]).
The ability to mix any number of datasets for training, provided they use the same column names for replicas.
Generation and logging in wandb/clearml of test replicas during evaluation runs for SFT and Preference training (using generate_eval_examples and num_gen_examples options in configs).
vLLM batched generation of answers for some datasets using an OpenAI-like server.

SMPO - Simple Margin Preference Optimization

Our own alignment method designed for PO stability. The method is inspired by such methods as IPO, SimPO, C-RLFT, as well as introducing its own loss function of separating chosen and rejected pairs.

The main idea of the method is the desire to smoothly achieve the desired margin level without forcing the model to retrain by adding a balancing SFT loss on chosen and rejected at the same time.

The implementation of the method is here, and the config is here.

🚀 How to Use

📦 Installation

🛠️ Project Installation

Run the following commands inside the project folder:

Install Poetry:
```
pip install poetry
```
Install project dependencies:
```
poetry install
```
Verify with:
```
poetry show
```
(Optional) Set the environment variable HF_HOME to your desired folder:
```
export HF_HOME=/mnt/hf/
```
(Optional) Log in to Hugging Face CLI:
```
poetry run huggingface-cli login
```
(Optional) Log in to Weights & Biases:
```
poetry run wandb login
```
Check the configuration settings in the accelerate/ folder (number of GPUs, etc.).

⚙️ Prerequisites and Troubleshooting

First, make sure you have all the necessary developer Linux libraries installed, including GCC and G++ version 8 or higher. You can check this by running:

gcc --version

It is recommened to do this steps before any installation:

apt update
apt install build-essential zlib1g-dev libffi-dev libssl-dev libbz2-dev libreadline-dev libsqlite3-dev liblzma-dev libncurses-dev tk-dev

Next, ensure that CUDA is version 11.8 or higher (preferably 12.1) and that all your GPUs are detected. Use the command:

nvidia-smi

After completing the first step of installation, check that you have Poetry version 1.8+ installed, and it’s best to use Python 3.10.16. If not, update Poetry with:

poetry self update

and run:

poetry env use 3.10.16

You can install 3.10.16 version of python via PyEnv.

After the second installation step, make sure that running poetry run ds_report returns meaningful text. Additionally, verify the version of Torch and the presence of NVIDIA packages.

If you encounter an error related to DeepSpeed and fused_adam during training, you need to remove DeepSpeed from your environment and install it with:

DS_BUILD_FUSED_ADAM=1 poetry run pip install deepspeed==0.14.5

Sometimes, deepspeed errors depends on Python version, on 3.10.16 everythong was tested in different environments.

🏃‍♂️ Example of Running a Script from Config

You need to select a DeepSpeed config + a training config + the script itself. Here’s an example command to start SFT training using YAML config:

PYTHONPATH="${PYTHONPATH}:src/" poetry run accelerate launch --config_file accelerate/stage2_config.yaml scripts/sft.py training_configs/sft/sft-llama-3.1-8b-it-lora-GrandmasterRAG-v1.yaml

📝 YAML config examples

SFT Training

Config for training SFT Llama 3.1, using Liger kernel, only assistant answers, modified chat template, LoRA, generating examples on eval.

model_name_or_path: "unsloth/Meta-Llama-3.1-8B-Instruct"
dataset:
  - "Vikhrmodels/GrandMaster-PRO-MAX"
  - "Vikhrmodels/Grounded-RAG-RU-v2"
train_only_on_completions: True
per_device_train_batch_size: 1
per_device_eval_batch_size: 1
num_train_epochs: 1
save_strategy: "steps"
save_steps: 400
save_total_limit: 6
learning_rate: 0.00004
gradient_accumulation_steps: 8
gradient_checkpointing: True
logging_steps: 1
remove_unused_columns: False
dataloader_num_workers: 2
save_only_model: True
generate_eval_examples: True
use_liger: True
max_seq_length: 16000
evaluation_strategy: "steps"
eval_steps: 400
run_name: "sft-grndmrag-llama-3.1-unsloth-lora-256-qkvogudlm-v1"
output_dir: "/home/models/sft-grndmrag-llama-3.1-unsloth-lora-256-qkvogudlm-v1"
warmup_steps: 20
report_to: "wandb"
conversation_field: "conversation"
bf16: True
seed: 42
logging_first_step: True
use_peft: True
lora_target_modules:
  - "k_proj"
  - "v_proj"
  - "q_proj"
  - "o_proj"
  - "gate_proj"
  - "up_proj"
  - "down_proj"
  - "lm_head"
lora_r: 256
lora_alpha: 256
assistant_message_template: "<|start_header_id|>assistant<|end_header_id|>\n\n"
pad_token: "<|reserved_special_token_0|>"
eos_token: "<|eot_id|>"
chat_template: "{{ bos_token }}{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}"
force_chat_template: True

For Tasks:

Click tags to check more tools for each tasks

train llm models align llms generate prompts score answers deduplicate embeddings

For Jobs:

machine learning engineer data scientist research scientist ai engineer nlp specialist

Alternative AI tools for effective_llm_alignment

Similar Open Source Tools

effective_llm_alignment

github

: 105

py-llm-core

PyLLMCore is a light-weighted interface with Large Language Models with native support for llama.cpp, OpenAI API, and Azure deployments. It offers a Pythonic API that is simple to use, with structures provided by the standard library dataclasses module. The high-level API includes the assistants module for easy swapping between models. PyLLMCore supports various models including those compatible with llama.cpp, OpenAI, and Azure APIs. It covers use cases such as parsing, summarizing, question answering, hallucinations reduction, context size management, and tokenizing. The tool allows users to interact with language models for tasks like parsing text, summarizing content, answering questions, reducing hallucinations, managing context size, and tokenizing text.

github

: 118

jido

Jido is a toolkit for building autonomous, distributed agent systems in Elixir. It provides the foundation for creating smart, composable workflows that can evolve and respond to their environment. Geared towards Agent builders, it contains core state primitives, composable actions, agent data structures, real-time sensors, signal system, skills, and testing tools. Jido is designed for multi-node Elixir clusters and offers rich helpers for unit and property-based testing.

github

: 276

llmgateway

The llmgateway repository is a tool that provides a gateway for interacting with various LLM (Large Language Model) models. It allows users to easily access and utilize pre-trained language models for tasks such as text generation, sentiment analysis, and language translation. The tool simplifies the process of integrating LLMs into applications and workflows, enabling developers to leverage the power of state-of-the-art language models for various natural language processing tasks.

github

: 463

exospherehost

Exosphere is an open source infrastructure designed to run AI agents at scale for large data and long running flows. It allows developers to define plug and playable nodes that can be run on a reliable backbone in the form of a workflow, with features like dynamic state creation at runtime, infinite parallel agents, persistent state management, and failure handling. This enables the deployment of production agents that can scale beautifully to build robust autonomous AI workflows.

github

: 65

gpt-computer-assistant

GPT Computer Assistant (GCA) is an open-source framework designed to build vertical AI agents that can automate tasks on Windows, macOS, and Ubuntu systems. It leverages the Model Context Protocol (MCP) and its own modules to mimic human-like actions and achieve advanced capabilities. With GCA, users can empower themselves to accomplish more in less time by automating tasks like updating dependencies, analyzing databases, and configuring cloud security settings.

github

: 5.8k

ai-chunking

AI Chunking is a powerful Python library for semantic document chunking and enrichment using AI. It provides intelligent document chunking capabilities with various strategies to split text while preserving semantic meaning, particularly useful for processing markdown documentation. The library offers multiple chunking strategies such as Recursive Text Splitting, Section-based Semantic Chunking, and Base Chunking. Users can configure chunk sizes, overlap, and support various text formats. The tool is easy to extend with custom chunking strategies, making it versatile for different document processing needs.

github

: 67

WordLlama

WordLlama is a fast, lightweight NLP toolkit optimized for CPU hardware. It recycles components from large language models to create efficient word representations. It offers features like Matryoshka Representations, low resource requirements, binarization, and numpy-only inference. The tool is suitable for tasks like semantic matching, fuzzy deduplication, ranking, and clustering, making it a good option for NLP-lite tasks and exploratory analysis.

github

: 1.4k

rag-chat

The `@upstash/rag-chat` package simplifies the development of retrieval-augmented generation (RAG) chat applications by providing Next.js compatibility with streaming support, built-in vector store, optional Redis compatibility for fast chat history management, rate limiting, and disableRag option. Users can easily set up the environment variables and initialize RAGChat to interact with AI models, manage knowledge base, chat history, and enable debugging features. Advanced configuration options allow customization of RAGChat instance with built-in rate limiting, observability via Helicone, and integration with Next.js route handlers and Vercel AI SDK. The package supports OpenAI models, Upstash-hosted models, and custom providers like TogetherAi and Replicate.

github

: 203

videokit

VideoKit is a full-featured user-generated content solution for Unity Engine, enabling video recording, camera streaming, microphone streaming, social sharing, and conversational interfaces. It is cross-platform, with C# source code available for inspection. Users can share media, save to camera roll, pick from camera roll, stream camera preview, record videos, remove background, caption audio, and convert text commands. VideoKit requires Unity 2022.3+ and supports Android, iOS, macOS, Windows, and WebGL platforms.

github

: 114

graphiti

Graphiti is a framework for building and querying temporally-aware knowledge graphs, tailored for AI agents in dynamic environments. It continuously integrates user interactions, structured and unstructured data, and external information into a coherent, queryable graph. The framework supports incremental data updates, efficient retrieval, and precise historical queries without complete graph recomputation, making it suitable for developing interactive, context-aware AI applications.

github

: 17.7k

Avalon-LLM

Avalon-LLM is a repository containing the official code for AvalonBench and the Avalon agent Strategist. AvalonBench evaluates Large Language Models (LLMs) playing The Resistance: Avalon, a board game requiring deductive reasoning, coordination, collaboration, and deception skills. Strategist utilizes LLMs to learn strategic skills through self-improvement, including high-level strategic evaluation and low-level execution guidance. The repository provides instructions for running AvalonBench, setting up Strategist, and conducting experiments with different agents in the game environment.

github

: 69

weave

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

github

: 980

redisvl

Redis Vector Library (RedisVL) is a Python client library for building AI applications on top of Redis. It provides a high-level interface for managing vector indexes, performing vector search, and integrating with popular embedding models and providers. RedisVL is designed to make it easy for developers to build and deploy AI applications that leverage the speed, flexibility, and reliability of Redis.

github

: 158

clearml-serving

ClearML Serving is a command line utility for model deployment and orchestration, enabling model deployment including serving and preprocessing code to a Kubernetes cluster or custom container based solution. It supports machine learning models like Scikit Learn, XGBoost, LightGBM, and deep learning models like TensorFlow, PyTorch, ONNX. It provides a customizable RestAPI for serving, online model deployment, scalable solutions, multi-model per container, automatic deployment, canary A/B deployment, model monitoring, usage metric reporting, metric dashboard, and model performance metrics. ClearML Serving is modular, scalable, flexible, customizable, and open source.

github

: 132

agents-starter

A starter template for building AI-powered chat agents using Cloudflare's Agent platform, powered by agents-sdk. It provides a foundation for creating interactive chat experiences with AI, complete with a modern UI and tool integration capabilities. Features include interactive chat interface with AI, built-in tool system with human-in-the-loop confirmation, advanced task scheduling, dark/light theme support, real-time streaming responses, state management, and chat history. Prerequisites include a Cloudflare account and OpenAI API key. The project structure includes components for chat UI implementation, chat agent logic, tool definitions, and helper functions. Customization guide covers adding new tools, modifying the UI, and example use cases for customer support, development assistant, data analysis assistant, personal productivity assistant, and scheduling assistant.

github

: 503

For similar tasks

Awesome-LLM-in-Social-Science

This repository compiles a list of academic papers that evaluate, align, simulate, and provide surveys or perspectives on the use of Large Language Models (LLMs) in the field of Social Science. The papers cover various aspects of LLM research, including assessing their alignment with human values, evaluating their capabilities in tasks such as opinion formation and moral reasoning, and exploring their potential for simulating social interactions and addressing issues in diverse fields of Social Science. The repository aims to provide a comprehensive resource for researchers and practitioners interested in the intersection of LLMs and Social Science.

github

: 136

LMOps

LMOps is a research initiative focusing on fundamental research and technology for building AI products with foundation models, particularly enabling AI capabilities with Large Language Models (LLMs) and Generative AI models. The project explores various aspects such as prompt optimization, longer context handling, LLM alignment, acceleration of LLMs, LLM customization, and understanding in-context learning. It also includes tools like Promptist for automatic prompt optimization, Structured Prompting for efficient long-sequence prompts consumption, and X-Prompt for extensible prompts beyond natural language. Additionally, LLMA accelerators are developed to speed up LLM inference by referencing and copying text spans from documents. The project aims to advance technologies that facilitate prompting language models and enhance the performance of LLMs in various scenarios.

github

: 3.6k

effective_llm_alignment

github

: 105

ComfyUI-IF_AI_tools

ComfyUI-IF_AI_tools is a set of custom nodes for ComfyUI that allows you to generate prompts using a local Large Language Model (LLM) via Ollama. This tool enables you to enhance your image generation workflow by leveraging the power of language models.

github

: 610

Awesome-AI-GPTs

Awesome AI GPTs is an open repository that collects resources and fun ways to use OpenAI GPTs. It includes databases, search tools, open-source projects, articles, attack and defense strategies, installation of custom plugins, knowledge bases, and community interactions related to GPTs. Users can find curated lists, leaked prompts, and various GPT applications in this repository. The project aims to empower users with AI capabilities and foster collaboration in the AI community.

github

: 935

kor

Kor is a prototype tool designed to help users extract structured data from text using Language Models (LLMs). It generates prompts, sends them to specified LLMs, and parses the output. The tool works with the parsing approach and is integrated with the LangChain framework. Kor is compatible with pydantic v2 and v1, and schema is typed checked using pydantic. It is primarily used for extracting information from text based on provided reference examples and schema documentation. Kor is designed to work with all good-enough LLMs regardless of their support for function/tool calling or JSON modes.

github

: 1.6k

Awesome-LLM-Survey

This repository, Awesome-LLM-Survey, serves as a comprehensive collection of surveys related to Large Language Models (LLM). It covers various aspects of LLM, including instruction tuning, human alignment, LLM agents, hallucination, multi-modal capabilities, and more. Researchers are encouraged to contribute by updating information on their papers to benefit the LLM survey community.

github

: 223

awesome-gpt-prompt-engineering

Awesome GPT Prompt Engineering is a curated list of resources, tools, and shiny things for GPT prompt engineering. It includes roadmaps, guides, techniques, prompt collections, papers, books, communities, prompt generators, Auto-GPT related tools, prompt injection information, ChatGPT plug-ins, prompt engineering job offers, and AI links directories. The repository aims to provide a comprehensive guide for prompt engineering enthusiasts, covering various aspects of working with GPT models and improving communication with AI tools.

github

: 802

For similar jobs

sweep

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

github

: 7.1k

teams-ai

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

github

: 502

ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

github

: 159

classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 669

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 953

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 1.3k

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.2k