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Blox - QT 70B 32K
In the swiftly evolving realm of large language models (LLMs), there is an unparalleled expansion, presenting groundbreaking possibilities for natural language processing in diverse sectors. Nonetheless, certain pivotal hurdles persist. At Dayzero Blox, we are at the forefront of this innovation, constructing the QT 70B 32k model — a trailblazing LLM initiative designed to tackle these essential challenges head-on.
Engaging in extensive experimentation with Open Source models such as the llama2 70B, Mistral 7B, and Falcon 40B, we have extended their pretraining phases and implemented fine-tuning through the innovative YaRN methodology to amplify the context length from 4k to 32k tokens. Preliminary tests indicate that the evolved models substantially surpass the performance of the base llama2 70B model, with expectations to rival the proficiency of Claude 2 in established MT benchmarks. We are in the process of conducting comprehensive benchmarks, the insights from which will be detailed in our forthcoming white paper. The introduction of this solution will mark yet another monumental advancement in the LLMs domain, and here's our approach :
1. Pre Training Enhancements
Limitations of the existing ecosystem :
Conventional Large Language Models (LLMs) have encountered limitations due to their inability to undergo pretraining on niche text data, significantly undermining their precision and applicability in specialised sectors such as insurance, healthcare, legal, and finance.
Benefits of our Model :
The QT 70B 32K Model will revolutionise this aspect by granting entities the capability to initiate pretraining protocols on domain-centric text datasets. This strategy fosters a more customised, relevant, and efficient model behaviour within specific realms.
2. Advanced Fine Tuning Capabilities
The scope of refining LLMs to accommodate distinct output configurations or styles has traditionally been restricted, impeding their versatility and utility in domains demanding precise, tailored responses.
In contrast, the Blox QT model will allow customizable finetuning, allowing for adjustments aligned with specific organisational objectives, thereby yielding more contextually appropriate and insightful outputs.
3. Reinforced Privacy Measures
The reliance on external servers operated by entities such as OpenAI and Anthropic for managing delicate user data has perpetuated significant apprehensions regarding security and privacy breaches.
We will addresses these vulnerabilities head-on by facilitating a secure, on-premise alternative, ensuring that organisations can process confidential information without jeopardising compliance standards or consumer confidence.
4. RAG Implementation
On requests, On-premise Retriever Augmented Generation (RAG) system, a development that promises expansive, secure, and intuitive interaction with extensive document repositories.
We underpin this innovation by overseeing on-site embeddings and vector databases, thereby eliminating concerns surrounding data privacy and offering a seamless, secure experience.