KL3M
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About
KL3M is a family of clean Large Language Models (LLMs) built on principles of clean provenance, high-quality content, and ethical data sourcing. It ensures no copyright issues, no breach of contract from scraping, and avoids synthetic data from other LLMs or toxic sources. KL3M is the first language model to obtain the Fairly Trained L Certification. Early results show its models, like kl3m-170m and kl3m-1.7b, are highly efficient for business content, outperforming models with 10x the parameters on legal domain data. They also demonstrate the lowest rates of toxicity. KL3M models are already used for drafting invoices, time entries, contracts, and SEC filings. Users can continue training KL3M on their own content, fine-tune it for safe conversational AI or specific tasks, or license its 2.5T+ underlying training data. The models are designed for accessible use, running on consumer hardware.
Platform
Features
• fairly trained l certification
• no copyright or toxicity issues
• clean provenance training data
• low toxicity and bias
• supports local deployment on consumer hardware
• ability to license training data
• customizable via fine-tuning
• efficient for legal and financial content
FAQs
What kind of hardware do I need to run KL3M?
The first KL3M models have been designed with accessible use as a priority. kl3m-170 runs quickly on a MacBook Air M1, and kl3m-1.7b runs well on a $300 consumer GPU.
What architectures are your models?
Smaller KL3M models are trained using the GPT-NeoX architecture. Larger KL3M models are trained using the Mixtral Mixture-of-Experts architecture (trained from scratch).
How can I run KL3M?
KL3M is distributed as standard PyTorch model weights. KL3M architectures are supported for both HuggingFace transformers and vllm for inference.
Which languages are supported?
`kl3m-170m` and `kl3m-1.7b` have both been trained on predominantly English-language content. Larger models include content in English, Spanish (es-ES and es-MX), French, and German.
Do you provide an API?
Not yet. Our focus has been on enabling the use of small, local LLMs for information security and accessibility purposes, but we are evaluating the possibility of providing an API in the future.
Is it easy to fine-tune KL3M?
We have had excellent results fine-tuning KL3M on a number of use cases, including drafting, summarization, and classification. You can fine-tune kl3m-170 and kl3m-1.7b on consumer hardware.
How many tokens do you have?
We have collected over 2.5 trillion tokens of training data, and we are constantly adding more. Our training data is a mix of public domain and explicitly licensed content.
How many tokens have your models seen?
`kl3m-170m` and `kl3m-1.7b` have been trained on approximately 350B tokens of primarily English-language content. Larger models are being trained on between 500B to 1T tokens of content.
Do you have a conversational chat model?
Not yet. While our pretraining data does include a number of conversational sources, we have not yet trained a model that is designed for standard conversational rounds. Stay tuned.
Do you have a general instruction-aligned model?
Our base models already support a number of tasks like extractive/abstractive summarization or conversion, but we have not trained an open-ended model. Our first instruct model supports legal drafting and revision.
How do you pronounce KL3M?
As the 🍊 suggests, KL3M is pronounced like "Clem" or "Klem."
Why is it named KL3M?
KL3M was originally short for the Kelvin Legal Large Language Model, KLLLM. Because we're nerds, we shortened all those Ls to L cubed or L3, then shortened K-L3-M to KL3M.
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