Introduction
In today’s rapidly evolving digital landscape, leveraging powerful language models is essential for businesses aiming to enhance operations, drive innovation, and stay competitive. Generative AI (Gen AI) technologies, particularly Large Language Models (LLMs), have transformed tasks such as customer service, content creation, and data analysis, making them indispensable for commercial projects. However, with numerous options available, selecting the right Gen AI model to align with specific needs can be daunting.
A key advantage of open LLMs is the ability to self-host, empowering businesses to maintain complete control over their data and operations, ensuring enhanced privacy, security, and protection of trade secrets. Self-hosting allows for greater customization and fine-tuning to meet safety standards and compliance requirements, providing tailored solutions for unique organizational needs. To navigate this landscape, we’ve curated a list of 9 Powerful Open LLMs perfectly suited for commercial applications. These models excel in performance, scalability, and flexibility, enabling businesses to integrate advanced AI capabilities seamlessly. From Meta’s versatile Llama series to innovative models like Mixtral and RWKV, discover the best Gen AI tools to unlock success in your commercial endeavors and propel your projects to new heights.
Llama 3.3
Meta’s Llama 3.3 is a 70-billion-parameter multilingual large language model optimized for dialogue in English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. It employs an auto-regressive transformer architecture enhanced with supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to improve helpfulness and safety. Pretrained on over 15 trillion tokens from publicly available data, Llama 3.3 supports multilingual text and code with a 128k context length. It outperforms many open and closed chat models on industry benchmarks.
Hugging Face URL: https://huggingface.co/meta-llama/Llama-3.3-70B-InstructParams (B):
70B
Context Window:
128k
License:
https://github.com/meta-llama/llama-models/blob/main/models/llama3_3/LICENSE
License Notes:
The use of LLaMA 3 is free for entities with fewer than 700 million users. Additionally, the agreement stipulates that outputs generated by LLaMA 3 cannot be used to train other language models, except for LLaMA 3 itself and its derivatives.
MMLU Score for the Largest Model:
86.0
Llama 3.1
Meta’s Llama 3.1 is a collection of multilingual large language models available in 8B, 70B, and 405B sizes, optimized for dialogue in English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. These auto-regressive transformer models incorporate supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to enhance helpfulness and safety. Trained on over 15 trillion tokens of publicly available data and utilizing Grouped-Query Attention (GQA) for better inference scalability, Llama 3.1 outperforms many open and closed chat models on industry benchmarks. Released on July 23, 2024.
Hugging Face URL: https://huggingface.co/meta-llama/Llama-3.1-405BParams (B):
8B, 70B, 405B
Context Window:
128k
License:
https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct/blob/main/LICENSE
License Notes:
The use of LLaMA 3 is free for entities with fewer than 700 million users. Additionally, the agreement stipulates that outputs generated by LLaMA 3 cannot be used to train other language models, except for LLaMA 3 itself and its derivatives.
MMLU Score for the Largest Model:
88.6
Qwen1.5
Qwen1.5, the beta version of Qwen2, is a transformer-based decoder-only language model available in nine sizes (0.5B to 110B), including a 14B MoE model with 2.7B activated. It features significant improvements in chat performance, multilingual support, and a stable 32K context length without requiring trust_remote_code. Built on an optimized Transformer architecture with SwiGLU activation and group query attention, Qwen1.5 includes an enhanced tokenizer for multiple languages and code. The models are pretrained on extensive data and fine-tuned using supervised learning and preference optimization. More details can be found on their blog and GitHub repository. Qwen is developed by Alibaba from China.
Hugging Face URL: https://huggingface.co/Qwen/Qwen1.5-110B-ChatParams (B):
0.5B, 1.8B, 4B, 7B, 14B, 32B, 72B, and 110B
Context Window:
32k
License:
https://huggingface.co/Qwen/Qwen1.5-7B-Chat/blob/main/LICENSE
License Notes:
Free if you have under 100M users and you cannot use Qwen outputs to train other LLMs besides Qwen and its derivatives
MMLU Score for the Largest Model:
80.4
Mixtral8x22B v0.1
Mixtral 8x22B is Mistral’s latest open, sparse Mixture-of-Experts (SMoE) language model with 141B parameters and 39B active, offering superior cost efficiency. It supports English, French, Italian, German, and Spanish, excels in math and coding, and features native function calling and a 64K token context window. Released under the permissive Apache 2.0 license, it outperforms other open models on benchmarks like MMLU, HellaSwag, and Arc Challenge. Mixtral 8x22B promotes openness and collaboration, making it ideal for fine-tuning and scalable application development.
Hugging Face URL: https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1Params (B):
141B
Context Window:
64k
License:
Apache 2.0
License Notes:
Not specified
MMLU Score for the Largest Model:
77.3
Flan-T5
Flan-T5 is an advanced version of the T5 (Text-To-Text Transfer Transformer) model, fine-tuned using the FLAN (Fine-tuned LAnguage Net) methodology. Developed by Google, Flan-T5 leverages extensive instruction tuning on diverse tasks to enhance its ability to follow instructions and perform various NLP tasks with higher accuracy. It transforms all tasks into a text-to-text format, enabling seamless handling of translation, summarization, question answering, and more. Flan-T5 models range from small to large sizes, providing scalability for different applications. This model excels in understanding and generating coherent, contextually relevant text, making it ideal for applications requiring robust language comprehension and generation capabilities.
Hugging Face URL: https://huggingface.co/google/flan-t5-xxlParams (B):
0.780B,3B, 11B
Context Window:
512 ()
License:
Apache 2.0
License Notes:
Not specified
MMLU Score for the Largest Model:
75.2
Falcon
Falcon-180B, developed by TII, is a 180-billion-parameter causal decoder-only model trained on 3,500 billion tokens from RefinedWeb and curated corpora. Released under a permissive license for commercial use, it outperforms models like LLaMA-2 and StableLM. Optimized for inference with a multiquery architecture, it requires at least 400GB of memory and PyTorch 2.0. Available in Falcon-180B-Chat and smaller versions (7B, 40B), it supports English, German, Spanish, French, and other languages. Further fine-tuning is recommended for most use cases.
Hugging Face URL: https://huggingface.co/tiiuae/falcon-180BParams (B):
7B, 40B, 180B
Context Window:
2048
License:
https://huggingface.co/spaces/tiiuae/falcon-180b-license/blob/main/LICENSE.txt
License Notes:
Can’t be offered as standalone chargeable service.
MMLU Score for the Largest Model:
70.6
Phi-3
Phi-3 Medium-4K-Instruct ONNX CUDA models are optimized 14B parameter models designed for efficient inference with ONNX Runtime on NVIDIA GPUs. Trained on high-quality synthetic and filtered web data, they support 4K and 128K token contexts. Post-trained with supervised fine-tuning and preference optimization, they excel in reasoning, language, math, and coding benchmarks. Available in FP16 and INT4 CUDA formats for various platforms, they offer easy integration via a new API. Users can select models based on GPU availability using Hugging Face CLI.
Hugging Face URL: https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cudaParams (B):
7B, 14B
Context Window:
4096, 128k
License:
MIT
License Notes:
Not specified
MMLU Score for the Largest Model:
68.8
Mistral 7B
Mistral 7B is a 7.3-billion-parameter language model by Mistral AI, optimized for various NLP tasks including commonsense reasoning, reading comprehension, and mathematical reasoning. It features an advanced transformer architecture with Grouped-query Attention (GQA) for faster inference, Sliding Window Attention (SWA) for handling long sequences efficiently, and a Local Attention Mechanism to optimize memory usage. Open-sourced under Apache 2.0, Mistral 7B supports applications like text generation, question answering, code generation, translation, and conversational agents. Its efficient architecture allows processing up to 131K tokens, making it a powerful and accessible tool for developers and researchers.
Hugging Face URL: https://huggingface.co/mistralai/Mistral-7B-v0.3Params (B):
7B
Context Window:
32k
License:
Apache 2.0
License Notes:
Not specified
MMLU Score for the Largest Model:
61.84
RWKV 6 v3
RWKV-6 World is a cutting-edge large language model developed by BlinkDL, utilizing a unique 100% recurrent neural network (RNN) architecture for efficient processing of long sequences and dependencies. Trained on over 1.4 trillion tokens from diverse sources in more than 100 languages and programming code, it excels in text generation, translation, code assistance, and conversational AI. The model ensures low-latency, coherent outputs and is adaptable for various applications. Released under a permissive license, RWKV-6 is ideal for content creation, language translation, coding help, and powering chatbots, making it a versatile tool for developers and researchers.
Hugging Face URL: https://huggingface.co/BlinkDL/rwkv-6-worldParams (B):
1.6B, 3B, 7B
Context Window:
4096
License:
Apache 2.0
License Notes:
Not specified
MMLU Score for the Largest Model:
54.2
Conclusion
Choosing the right Large Language Model can transform your commercial projects, driving efficiency and innovation. The 9 Powerful Open Source LLMs discussed offer diverse capabilities, from multilingual support to advanced reasoning and coding. Self-hosting these models ensures enhanced privacy, safety, and protection of trade secrets by giving you full control over your data and infrastructure.
Are you looking to create successful Gen AI projects using powerful LLMs? Contact Us today to unlock the full potential of these advanced models and take your business to the next level!
Author's Bio:
Pritam Barhate, with an experience of 14+ years in technology, heads Technology Innovation at Mobisoft Infotech. He has a rich experience in design and development. He has been a consultant for a variety of industries and startups. At Mobisoft Infotech, he primarily focuses on technology resources and develops the most advanced solutions.
