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Qmatic's top competitors include Q-nomy, QLess and Nemo-Q.Mikael HedlofCEO82/100100 - 250$0$25M - 100MMikael HedlofCEO82/100100 - 250Mikael HedlofCEO1Nadav ArzoanCEO100/100100 - 250- -$25M - 100MNadav ArzoanCEO100/100100 - 250Nadav ArzoanCEO2James HarveyCEO82/10025 - 100$20M$5M - 25MJames HarveyCEO82/10025 - 100James HarveyCEO3Michael BergCEO77/10053- -$13.4MMichael BergCEO4Joseph ZIRAHManaging Director69/100100 - 250- -$5M - 25MJoseph ZIRAHManaging Director69/100100 - 250Joseph ZIRAHManaging Director69/100Joseph ZIRAHManaging Director5CEOQmatic VS Q-nomyQ-nomy is the biggest rival of Qmatic. Q-nomy was founded in 2002, and its headquarters is in Weston, Florida. Q-nomy operates in the Scheduling Software industry. Q-nomy has 18 fewer employees than Qmatic.Qmatic VS QLessQLess has been one of Qmatic's top competitors. QLess is a Private company that was founded in Pasadena, California in 2007. QLess is in the Scheduling Software industry. QLess has 128 fewer employees vs. Qmatic.Qmatic VS Nemo-QNemo-Q is Qmatic's #3 rival. Nemo-Q was founded in 2002 in Mckinney, Texas. Nemo-Q is in the Scheduling Software field. Nemo-Q generates $46.8M less revenue than Qmatic.Looking for similar companies to Qmatic?Similar CompaniesFrequently Asked Questions about QmaticWho are Qmatic's top competitors?Qmatic's top 3 competitors are Q-nomy, QLess, Nemo-Q.Who is similar to Qmatic?Q-nomy is the most similar to Qmatic. QLess and Nemo-Q are also similar to Qmatic.Are Qmatic and Q-nomy competitors?Yes, Q-nomy is one of Qmatic’s top competitors.

Queuing in financeImproving Customer Experiences with NEMO-QReceive Additional Information Improve efficiency Improve your overall efficiency with clear insight to wait times, important KPI's, and employee proficiency. Modern solutions Allow customers to get in line at the nearest branch by using their favorite device. Walk-ins and appointments are blended seamlessly. Reduce frustration Provide an efficient and stress free experience for the customer. Multiple options for line entry demonstrate your respect for customer time Decrease lobby traffic Cut down on your number of walk-ins and free up critical space in your lobby. Make the customer's experience efficient and effective. Mobile friendly options With NEMO-Q Appointment Scheduling your customers can easily book online through a secure and safe mobile friendly website.the perfect queuing solutionA queue management system from NEMO-Qoffers a better way for financial institutions tomanage wait lines. With the NEMO-Q system, your customers’ queues are streamlined basedon their particular banking needs, ensuringa better experience and a shorter perceivedwait time. And with analytics built into thesystem, banks and credit unions can gainvaluable insights about operations, improvingboth efficiencies and member satisfaction.Modern and Transparent Before the digital age, paper clipboards werethe norm for queue management. However,in today’s digitally-advanced world, youcertainly don’t want a potential member’s firstimpression to be an outdated paper process.Further, antiquated clipboard queuing doesn’tprovide members any context on wait times,leaving them feeling unvalued with norealistic expectation of how long they’ll betied up. Reduce labor ExpensesUsing a third-party system to manuallyenter member names and the reason for theirvisit costs thousands in labor expenses .

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A global batch size of 768 using 380B tokens (the same dataset used in teacher fine-tuning).Mistral-NeMo-Minitron-8B-InstructWe applied an advanced alignment technique consisting of two-stage instruction finetuning and two-stage preference optimization, resulting in a state-of-the-art instruct model with excellent performance in instruction following, language reasoning, function calling, and safety benchmarks. The alignment data was synthetically generated using the Nemotron-340B-Instruct model in conjunction with the Nemotron-340B-Reward model. The model alignment was done with NVIDIA NeMo Aligner.Performance benchmarksWe optimized the Mistral-NeMo-Minitron-8B-Base model, the teacher Mistral-NeMo-12B model, and the LLama-3.1-8B model with NVIDIA TensorRT-LLM, an open-source toolkit for optimized LLM inference. Figures 2 and 3 show the throughput requests per second of different models in FP8 and BF16 precision on different use cases, represented as input sequence length/output sequence length (ISL/OSL) combinations at batch size 32 on one NVIDIA H100 80-GB GPU. The Llama-3.1-8B model is the fastest, at an average of ~1.4x throughput of Mistral-NeMo-12B, followed by Mistral-NeMo-Minitron-8B-Base at a 1.2x improvement over Mistral-NeMo-12B. This is primarily because the Llama-3.1-8B model has 32 layers compared to Mistral-NeMo-12B with 40 layers. Deployment in FP8 also delivers a performance boost of ~1.4x across all three models compared to BF16.Figure 2. Performance benchmarks for request BF16 throughput at different I/O length combinationsFigure 3. Performance benchmarks for request FP8 throughput at different I/O length combinationsConclusionMistral-NeMo-Minitron-8B provides class-leading accuracy and consistently outperforms recently introduced state-of-the-art models of similar size. Mistral-NeMo-Minitron-8B is our first work on the distillation of the Mistral-NeMo-12B model and provides strong support for our structured weight pruning combined with knowledge distillation best practices. Mistral-NeMo-Minitron-8B-Instruct also demonstrated our state-of-the-art alignment training recipe. Further work distilling, aligning, and obtaining even smaller and more accurate models is planned. Implementation support for depth pruning and distillation is available in the NVIDIA NeMo framework for generative AI training. Example usage is provided as a notebook.For more information, see the following resources:LLM Pruning and Distillation in Practice: The Minitron ApproachCompact Language Models via Pruning and Knowledge Distillation /NVlabs/Minitron GitHub repo/NVIDIA/NeMo-Aligner Github repo Mistral-NeMo-Minitron-8B-Base model on Hugging FaceMistral-NeMo-Minitron 8B-Instruct model on Hugging FaceMistral-NeMo-Minitron-8B-Instruct model on the NVIDIA API CatalogAcknowledgmentsThis work would not