%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)
Olmo 2 1124 13B DPO
Overview :
OLMo-2-1124-13B-DPO is a 13 billion parameter large language model that has undergone supervised fine-tuning and DPO training. It primarily targets English and aims to provide exceptional performance across various tasks such as chat, mathematics, GSM8K, and IFEval. This model is part of the OLMo series, designed to advance scientific research in language models. The training is based on the Dolma dataset, and the code, checkpoints, logs, and training details are publicly available.
Target Users :
This model is designed for researchers, developers, and educational institutions who can leverage it for natural language processing research, building chatbots, language translation tools, or other text generation applications. Its high performance and multi-task capability make it particularly suitable for scenarios that involve handling large amounts of English text data.
Use Cases
Example 1: Researchers utilize the OLMo-2-1124-13B-DPO model for sentiment analysis research.
Example 2: Developers integrate this model into a Q&A system, providing real-time natural language interaction.
Example 3: Educational institutions use this model to develop teaching aids that help students understand and learn complex language structures.
Features
? Text generation support: Capable of generating coherent and relevant text content.
? Multi-task performance: Excels in various tasks, including chat, mathematical problem-solving, GSM8K, and IFEval.
? Fine-tuning capability: The model is fine-tuned on specific datasets to enhance performance on targeted tasks.
? Easy integration: Can be effortlessly loaded and utilized via the Hugging Face platform.
? Apache 2.0 license compliance: Allows free use for research and educational purposes.
? Model series: As part of the OLMo series, it shares core architecture and training methods with other models.
? Research promotion: Aims to foster scientific research and technological innovation in language models.
How to Use
1. Install the Transformers library: Use the pip command to install the latest version of the Transformers library.
%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. Load the model: Access the OLMo-2-1124-13B-DPO model through the API provided by Hugging Face.
3. Data preprocessing: Format the input text to meet the model's requirements, for example, using a chat template.
4. Model inference: Input the preprocessed data into the model to obtain output results.
5. Result analysis: Conduct further analysis based on the model's output or apply it directly in practical scenarios.
6. Fine-tune the model: If necessary, fine-tune the model on a specific dataset to optimize performance.
7. Model deployment: Deploy the trained model to a production environment to provide services.
Featured AI Tools
Chinese Picks
Coze 扣子
Coze 扣子 is a no-code AI chatbot development platform that allows users to quickly create intelligent chatbots without programming. The platform provides a powerful visual flow editor, supporting the addition of natural language processing, knowledge bases, workflows, and more, enabling complex AI interactions.扣子 platform also offers rich debugging tools to test and optimize the dialogue flow between the robot and the user, greatly improving development efficiency. This product is suitable for various industry applications and can be deployed on social media, IM, and other channels to build unique brand voices.
Chatbots
3.3M
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
306.9K