Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
EXAONE Deep represents a collection of advanced language models that are enhanced for reasoning, created by LG AI Research, and come in sizes of 2.4 billion, 7.8 billion, and 32 billion parameters. These models excel in a variety of reasoning challenges, particularly in areas such as mathematics and coding assessments. Significantly, the EXAONE Deep 2.4B model outshines other models of its size, while the 7.8B variant outperforms both open-weight models of similar dimensions and the proprietary reasoning model known as OpenAI o1-mini. Furthermore, the EXAONE Deep 32B model competes effectively with top-tier open-weight models in the field. The accompanying repository offers extensive documentation that includes performance assessments, quick-start guides for leveraging EXAONE Deep models with the Transformers library, detailed explanations of quantized EXAONE Deep weights formatted in AWQ and GGUF, as well as guidance on how to run these models locally through platforms like llama.cpp and Ollama. Additionally, this resource serves to enhance user understanding and accessibility to the capabilities of EXAONE Deep models.
Description
LFM3 represents Liquid AI's suite of streamlined, open-weight foundational models designed for a variety of applications, including text, vision, audio, and multimodal tasks. This lineup features variants for text generation, vision-language processing, audio, omni tasks, and decision-making models. Developers have the option to download the model weights, customize the supported models via fine-tuning, and deploy them on local edge hardware, or alternatively, utilize Liquid AI’s hosted API for access. Potential applications encompass conversational agents, tool invocation, speech transcription and synthesis, understanding of images and videos, document extraction, classification, routing, and robotics functionalities. LFM3 aims to cater to developers, researchers, and organizations focused on creating tailored AI solutions that require adaptable deployment options, including the ability for local inference. Furthermore, the open-weight models operate under Liquid AI’s LFM Open License, where the eligibility for commercial use and its associated terms are contingent upon the annual revenue of the organization. This flexibility allows users to tailor their AI implementations precisely to their needs, enhancing the overall utility of the models.
API Access
Has API
No
API Access
Has API
Yes
Integrations
Ollama
Yes
Hugging Face
No
Hugging Face Transformers
No
LM Studio
No
OpenAI
Yes
OpenAI o1-mini
Yes
OpenRouter
No
Unsloth
No
Vercel AI Gateway
No
vLLM
No
Integrations
Ollama
Yes
Hugging Face
Yes
Hugging Face Transformers
Yes
LM Studio
Yes
OpenAI
No
OpenAI o1-mini
No
OpenRouter
Yes
Unsloth
Yes
Vercel AI Gateway
Yes
vLLM
Yes
Pricing Details
Free
Free Trial
No
Free Version
Yes
Pricing Details
$0.08/MTok
LFM3-1.7B API usage is $0.08 per million input tokens, $0.02 per million cached input tokens, and $0.20 per million output tokens. New organizations receive $2 in API credit. Open-weight models can be used in production free by developers, researchers, nonprofits, and businesses with less than $10 million in annual revenue; businesses above that threshold require a paid license.
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
Yes
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
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
LG
Founded
1947
Country
South Korea
Website
github.com/LG-AI-EXAONE/EXAONE-Deep
Vendor Details
Company Name
Liquid AI
Founded
2023
Country
United States
Website
www.liquid.ai/blog/lfm3