%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
Llama O1
Overview :
LLaMA-O1 is a large inference model framework that integrates Monte Carlo Tree Search (MCTS), self-reinforcement learning, Proximal Policy Optimization (PPO), and draws from the dual strategy paradigm of AlphaGo Zero alongside large language models. This model primarily targets Olympic-level mathematical reasoning problems, providing an open platform for training, inference, and evaluation. According to product background information, this is an individual experimental project and is not affiliated with any third-party organizations or institutions.
Target Users :
The primary audience includes data scientists, machine learning engineers, and researchers who require a powerful inference model to tackle complex mathematical and logical problems. LLaMA-O1 offers an open platform that enables these users to experiment and innovate, thus advancing the technology behind large inference models.
Use Cases
Example 1: A data scientist uses LLaMA-O1 for reasoning and solving Olympic-level mathematical problems.
Example 2: A machine learning engineer utilizes the LLaMA-O1 framework for training and optimizing self-reinforcement learning models.
Example 3: Researchers employ LLaMA-O1 for inference and evaluation of large language models, exploring new algorithms and applications.
Features
? Supports Monte Carlo Tree Search (MCTS) for inference optimization.
? Integrates self-reinforcement learning techniques to enhance the model's self-learning capabilities.
? Employs the PPO algorithm, improving the model's strategy optimization potential.
? Leverages AlphaGo Zero's strategy paradigm to enhance decision-making quality.
? Compatible with PyTorch and Hugging Face, facilitating ease of use for developers.
? Provides a personal experimentation platform, allowing users to conduct custom training and evaluation.
? Offers tutorials and guidance from AlphaGo Zero to RLHF.
? Supports pre-training using LLaMaFactory.
How to Use
1. Install the necessary environment: Use pip to install torch, transformers, accelerate, peft, and datasets.
%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%230080ff;%20}%20%3c/style%3e%3c/defs%3e%3cpath%20class='st0'%20d='M16.2,11.1c.4.5.4,1.2,0,1.8l-4.7,7.1h-3.8l5.3-8L7.6,4h3.8l4.7,7.1Z'/%3e%3c/svg%3e)
2. Clone the code: Use the git clone command to copy the LLaMA-O1 repository to your local machine.
3. Navigate to the directory: Use the cd command to enter the LLaMA-O1 directory.
4. Pull the latest code: Execute the git pull command to ensure you have the most recent code.
5. Run the training: Start model training by using the command python main.py.
6. Use Accelerate: If needed, use the accelerate config and accelerate launch main.py commands to run the training.
7. Inference and evaluation: Utilize the model for inference and evaluation tasks as required.
Featured AI Tools
Elicit
Elicit is an AI assistant that analyzes research papers at super speed. It automates tedious research tasks like paper summarization, data extraction, and synthesizing research findings. Users can search for relevant papers, get one-sentence summaries, extract and organize detailed information from papers, and find themes and concepts. Elicit is highly accurate, user-friendly, and has earned the trust and praise of researchers worldwide.
Research Instruments
603.9K
Tensorpool
TensorPool is a cloud GPU platform dedicated to simplifying machine learning model training. It provides an intuitive command-line interface (CLI) enabling users to easily describe tasks and automate GPU orchestration and execution. Core TensorPool technology includes intelligent Spot instance recovery, instantly resuming jobs interrupted by preemptible instance termination, combining the cost advantages of Spot instances with the reliability of on-demand instances. Furthermore, TensorPool utilizes real-time multi-cloud analysis to select the cheapest GPU options, ensuring users only pay for actual execution time, eliminating costs associated with idle machines. TensorPool aims to accelerate machine learning engineering by eliminating the extensive cloud provider configuration overhead. It offers personal and enterprise plans; personal plans include a $5 weekly credit, while enterprise plans provide enhanced support and features.
Model Training and Deployment
307.2K