Lamini

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Lamini creates optimized Large Language Models for enterprises, offering customization, privacy, and flexibility.

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What is Lamini?

Lamini is an innovative platform that focuses on creating private and highly optimized Large Language Models (LLMs) for enterprises and developers. It enhances existing models like GPT-3 and ChatGPT by tailoring them to specific company languages and use cases using proprietary data. This customization leads to improved performance on tasks relevant to the user. The platform offers the flexibility to export models for self-hosting and provides tools for rapid development and deployment, with a special emphasis on data privacy and security.

Customers using Lamini have highlighted its benefits in terms of data privacy, ownership, flexibility, cost control, latency, and throughput. The platform incorporates various cutting-edge technologies and research to optimize LLMs, such as fine-tuning, retrieval-augmented training, data augmentation, and GPU optimization. Lamini's pricing structure includes a free tier for small LLM training and a customizable Enterprise tier for larger models with more control over size, type, throughput, and latency.

Additionally, Lamini offers extensive support for model development, deployment, and optimization. The platform enables efficient tuning, evaluation, and deployment processes through a user-friendly interface, Python library, and REST APIs. It ensures seamless integration with the ability to handle up to 1 million tokens per job and 10,000 monthly inference calls with Lamini Pro. Furthermore, the platform provides enterprise-class support for training LLMs tailored to specific product requirements.

Who created Lamini?

Lamini was co-founded by Greg Diamos, an individual with a strong background in the field of artificial intelligence and computer engineering. Greg Diamos has significant experience in the industry, having served in key positions at reputable companies like Baidu and NVIDIA. His expertise includes AI scaling laws, Tensor Cores, and involvement in the development of MLPerf, an industry standard for machine learning performance. Lamini offers a classifier SDK that has been positively received by customers for its ease of use and efficiency, saving significant amounts of engineering time and enabling rapid deployment of models into production environments. Overall, with Greg Diamos at the helm, Lamini aims to deliver cutting-edge solutions in the realm of Large Language Models, providing enterprises and developers with a powerful platform for private, optimized LLMs.

What is Lamini used for?

Optimizing Lamini Memory for achieving >95% accuracy with thousands of specific IDs or internal data
Running Lamini anywhere, including air-gapped environments
Guaranteeing JSON output with 100% schema accuracy through reengineering the decoder
Delivering around 52 times more queries per second in inference compared to vLLM for massive throughput
Using Lamini to understand company-specific language and specific use cases by leveraging data
Deploying Lamini to any cloud service or on-premise for running LLM in own environment
Enhancing AI potential by easily creating private, highly optimized Large Language Models with rapid development and deployment via Lamini platform
Providing flexibility to fine-tune powerful models, process up to 1 million tokens per job, and handle 10,000 inference calls monthly with Lamini Pro
Supporting seasoned and novice developers with a user-friendly interface, robust Python library, and REST APIs for seamless integration and deployment
Offering full enterprise-class support for training LLMs tailored to specific product requirements

Who is Lamini for?

Developers
Enterprise teams
Enterprise professionals

How to use Lamini?

To use Lamini, follow these steps:

Sign up for an account on the Lamini platform.
Choose a model from options like Llama 3, Mistral 2, Phi 3, or opt for a custom solution.
Train your selected model using Lamini's optimized compute platform.
Fine-tune the model swiftly, with the ability to process up to 1 million tokens per job.
Evaluate the model's performance and make necessary adjustments.
Deploy the tuned model for inference, with up to 10,000 inference calls monthly.
Enjoy the flexibility of exporting model weights for self-hosting if desired.
Utilize the user-friendly interface, Python library, and REST APIs for seamless integration.
Benefit from enterprise-class support and guidance from AI experts for creating tailored models for various applications. With thorough training, evaluation, and deployment processes simplified, Lamini caters to both novice and experienced developers alike.

Pros

User-friendly interface and robust Python library.
Focus on rapid development and deployment for generative AI technology
Deployment flexibility to run LLM on any cloud service or on premise.
Use of latest generation models for best performance.
Great starting point with models like GPT-3 and ChatGPT.
Get a big infrastructure lift.
Build up AI know-how and an AI moat internally at your company.
Dedicated AI engineers available for assistance.
Seamless integration without deep machine learning expertise.
Data privacy: Use private data in your own secure environment. Use all of your data, rather than what fits into a prompt.
Full enterprise-class support for training LLMs.
Fine-tuning is swift, chaining numerous LLMs can be done easily.
Enjoy the flexibility to fine-tune powerful models.
Control (cost, latency, throughput): With ownership, you have more control over the cost, latency, and model.
Ownership & Flexibility: Own the LLM you train with your own engineering team.

Cons

Lack of detailed insight into the fine-tuning process and level of control compared to other tools.
Free tier may have restrictions on model size and capabilities
May have limitations in terms of scaling for large deployments
Unclear whether the platform provides automated updates for new models
Limited flexibility in deploying the model locally
May not perform as well on generic tasks without specific data
Limited model selection compared to other providers like OpenAI
Cost control may require more hands-on engineering team involvement
Possible trial and error process for fine-tuning models
Limited documentation for fine-tuning compared to other tools like sklearn
Data privacy concerns when using private data in your own secure environment
The platform's value for money compared to other AI tools in the industry is not explicitly justified.
Data privacy concerns when using private data in a secure environment
No mention of extensive preset model libraries for different applications, which may limit versatility.
Not as effective for generic tasks without specific company data.

Pros

Cons

User-friendly interface and robust Python library.
Focus on rapid development and deployment for generative AI technology
Deployment flexibility to run LLM on any cloud service or on premise.
Use of latest generation models for best performance.
Great starting point with models like GPT-3 and ChatGPT.
Get a big infrastructure lift.
Build up AI know-how and an AI moat internally at your company.
Dedicated AI engineers available for assistance.
Seamless integration without deep machine learning expertise.
Data privacy: Use private data in your own secure environment. Use all of your data, rather than what fits into a prompt.
Full enterprise-class support for training LLMs.
Fine-tuning is swift, chaining numerous LLMs can be done easily.
Enjoy the flexibility to fine-tune powerful models.
Control (cost, latency, throughput): With ownership, you have more control over the cost, latency, and model.
Ownership & Flexibility: Own the LLM you train with your own engineering team.

Lack of detailed insight into the fine-tuning process and level of control compared to other tools.
Free tier may have restrictions on model size and capabilities
May have limitations in terms of scaling for large deployments
Unclear whether the platform provides automated updates for new models
Limited flexibility in deploying the model locally
May not perform as well on generic tasks without specific data
Limited model selection compared to other providers like OpenAI
Cost control may require more hands-on engineering team involvement
Possible trial and error process for fine-tuning models
Limited documentation for fine-tuning compared to other tools like sklearn
Data privacy concerns when using private data in your own secure environment
The platform's value for money compared to other AI tools in the industry is not explicitly justified.
Data privacy concerns when using private data in a secure environment
No mention of extensive preset model libraries for different applications, which may limit versatility.
Not as effective for generic tasks without specific company data.

Lamini Pricing and plans

Paid plans start at $250/year and include:

Upto 10 projects
Customizable dashboard
Upto 50 tasks
Upto 1 GB storage
Unlimited proofings
Unlimited proofings

Lamini FAQs

How is Lamini different than just using a single provider’s APIs off the shelf?: 3 major reasons: 1. Data privacy: Use private data in your secure environment. 2. Ownership & Flexibility: Own the LLM you train with your engineering team. 3. Control (cost, latency, throughput): Have more control over the cost and latency of the model.

What does the LLM platform do?: The platform runs and optimizes your LLM using various technologies like fine-tuning, RLHF, retrieval-augmented training, data augmentation, and GPU optimization.

How expensive is using Lamini's platform to build and use a model?: There is a free tier for training small LLMs and $20 free inference credits with each new account. Enterprise tier offers more control over model size, type, and throughput.

Do you build large models on Lamini?: Yes, the resulting model is large, dependent on the base model selected or automatically chosen based on your data and use case.

What LLMs is the LLM platform using under the hood?: The platform builds on the latest models, including those on HuggingFace and OpenAI, tailored to specific use cases and data constraints.

Can I export the model and run it myself?: Yes, you can deploy on any cloud service or on premise, including setup for running your LLM in your own environment for scaled inference.