Lamini
3.60
0
Lamini creates optimized Large Language Models for enterprises, offering customization, privacy, and flexibility.
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.
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
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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.
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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.
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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.
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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.
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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.
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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.
Get started with Lamini
Lamini reviews
Jamal Ochieng
December 7, 2024
What do you like most about using Lamini?
I appreciate the flexibility that Lamini provides in customizing models for our specific needs. The ability to fine-tune the language model to match our company's terminology is a standout feature.
What do you dislike most about using Lamini?
The documentation can be a bit lacking, especially for new users. It took some time to figure out how to best utilize the API and the various features.
What problems does Lamini help you solve, and how does this benefit you?
Lamini helps us streamline our customer service interactions by creating a tailored model that understands our products better, which ultimately enhances customer satisfaction.
How would you rate Lamini?
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Priya Singh
January 2, 2025
What do you like most about using Lamini?
The data privacy features are impressive. We can train models without worrying about exposing sensitive information.
What do you dislike most about using Lamini?
The pricing structure can be a bit confusing. It would be helpful if there was a clearer breakdown of what each tier offers.
What problems does Lamini help you solve, and how does this benefit you?
Lamini allows us to control costs while still getting high-performance models, which is crucial for our budget constraints.
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Karim Ngoya
January 13, 2025
What do you like most about using Lamini?
The interface is user-friendly when it works well. I like that I can integrate it with our existing systems.
What do you dislike most about using Lamini?
I've experienced frequent downtimes which have affected our deployment schedules. It can be quite frustrating.
What problems does Lamini help you solve, and how does this benefit you?
When it works, Lamini has the potential to enhance our operations by providing tailored responses, but the inconsistency has hindered our progress.
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