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Average Ratings 0 Ratings
Description
Beam represents Reflection’s inaugural open-weight model, characterized as a sparse Mixture-of-Experts framework featuring a staggering 501 billion parameters, of which 23 billion are actively utilized, specifically crafted for tasks involving coding, reasoning, and agentic functions. Its prowess is derived from extensive pretraining and reinforcement learning, having been developed using a vast dataset of 23.8 trillion diverse, high-quality tokens sourced from the web, public domains, and proprietary licensed materials. With a targeted emphasis on enhancing coding and agentic capabilities, Beam is engineered to provide competitive open-weight performance while ensuring efficient inference compute. This model adeptly handles a wide array of tasks, including intricate software engineering, terminal operations, STEM activities, web searches, tool utilization, and general knowledge inquiries. Reinforcement learning techniques have been employed to bolster its abilities in multi-step reasoning, tool application, and responsiveness to environmental feedback. Moreover, users have the flexibility to manage the balance between efficiency and performance through an adjustable reasoning effort parameter, thus tailoring the model's output to better suit their specific needs.
Description
NVIDIA NeMo Megatron serves as a comprehensive framework designed for the training and deployment of large language models (LLMs) that can range from billions to trillions of parameters. As a integral component of the NVIDIA AI platform, it provides a streamlined, efficient, and cost-effective solution in a containerized format for constructing and deploying LLMs. Tailored for enterprise application development, the framework leverages cutting-edge technologies stemming from NVIDIA research and offers a complete workflow that automates distributed data processing, facilitates the training of large-scale custom models like GPT-3, T5, and multilingual T5 (mT5), and supports model deployment for large-scale inference. The process of utilizing LLMs becomes straightforward with the availability of validated recipes and predefined configurations that streamline both training and inference. Additionally, the hyperparameter optimization tool simplifies the customization of models by automatically exploring the optimal hyperparameter configurations, enhancing performance for training and inference across various distributed GPU cluster setups. This approach not only saves time but also ensures that users can achieve superior results with minimal effort.
API Access
Has API
No
API Access
Has API
No
Integrations
Amazon SageMaker Model Training
No
NVIDIA BioNeMo
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Reflection
Country
United States
Website
reflection.ai/blog/introducing-beam
Vendor Details
Company Name
NVIDIA
Founded
1993
Country
United States
Website
developer.nvidia.com/nemo/megatron