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Shaking incubator, Minitron - Double tower, Ø25 mmINFORS HT Minitron is the shaker of choice for Microbial- and Cell cultivation. The Minitron provides the optimum growth environment and can be manufactured to species. It has an easy to use colour display with a touch controller with a viewing distance of 15 m. The Minitron can be build single orDescriptionINFORS HT Minitron is the shaker of choice for Microbial- and Cell cultivation. The Minitron provides the optimum growth environment and can be manufactured to species. It has an easy to use colour display with a touch controller with a viewing distance of 15 m. The Minitron can be build single or double stacked, with Ø 25- or 50mm throw.Options: - Cooling- CO2 Control (0-20%)- Active Humidity Control (HHC)- LED growth light (Warm White)- Pass through port for external surveillance. - Static Shelf- Analog or Digital Output- EVE bioprocess software- FAT/SAT or IQ/OQ- Full range of clamping for microtiter plates, tubes, Erlenmeyer flasks, Ultra-Yield- og Optimum-Growth flasks. - Universal Sticky-Stuff tray.The Minitron is designed for easy cleaning and uses large 850 x 470 mm plates, with a Max. capacity of 3 x 5L Optimum Growth Shake Flask. Comes with lights in the chamber, for visual inspection of experiments while shaking, as standard.Contact us for your specific configurationPack: 1 eachSpecificationsAny questions, please contactCecilie Wille Bøgvad Hansen, [email protected], +45 44540064ProducentInforsTemperature [°C]RT +5 til +65°CSpeed [rpm]20 - 400Shaking diameter [mm]Ø25Topplate [mm]480 x 420Ext. dimensions (wxdxh) [mm]800 x 625 x 1490ModelMinitronQty per Salesunit1 eachSales Unit of MeasureEAUNSPSC41104405 Rysteinkubatorer

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

This post was originally published August 21, 2024 but has been revised with current data.Recently, NVIDIA and Mistral AI unveiled Mistral NeMo 12B, a leading state-of-the-art large language model (LLM). Mistral NeMo 12B consistently outperforms similarly sized models on a wide range of benchmarks. We announced Mistral-NeMo-Minitron 8B, one of the most advanced open-access models in its size class. This model consistently delivers leading accuracy on nine popular benchmarks. The Mistral-NeMo-Minitron 8B base model was obtained by width-pruning the Mistral NeMo 12B base model, followed by a light retraining process using knowledge distillation. This is a successful recipe that NVIDIA originally proposed in the paper, Compact Language Models via Pruning and Knowledge Distillation. It’s been proven time and again with NVIDIA Minitron 8B and 4B, and Llama-3.1-Minitron 4B models. Figure 1. Model pruning and distillation for Mistral-NeMo-Minitron-8B-Base and -Instruct modelsIn Figure 1, the Nemotron-4-340B-Instruct and -Reward models were used to generate synthetic data for the alignment.MMLU 5-shotGMS8k 0-shotGPQA 0-shotHumanEval0-shotMBPP 0-shotIFEvalMTBench (GPT4-Turbo)BFCL v2 LiveMistral-NeMo-Minitron 8B Instruct70.487.131.571.372.584.47.8667.6Llama-3.1-8B-Instruct69.483.930.472.672.879.77.7844.3Mistral-NeMo-12B-Instruct68.479.828.668.366.764.78.1047.9Table 1. Accuracy of the Mistral-NeMo-Minitron-8B-Instruct model compared to Llama-3.1-8B-Instruct and the teacher Mistral-NeMo-12B models. Bold numbers represent the best amongst the 8B model class.Training tokensWino-Grande 5-shotARCChallenge 25-shotMMLU 5-shotHellaSwag 10-shotGSM8K 5-shotTruthfulQA 0-shotXLSum en (20%)3-shotMBPP0-shotHumanEval0-shotLlama-3.1-8B15T77.2757.9465.2881.8048.6045.0630.0542.2724.76Gemma-7B6T786164825045173932Mistral-NeMo-Minitron-8B380B80.3564.4269.5183.0358.4547.5631.9443.7736.22Mistral-NeMo-12BN/A82.2465.1068.9985.1656.4149.7933.4342.6323.78Table 2. Accuracy of the Mistral-NeMo-Minitron-8B-Base model compared to Llama-3.1-8B-Base and the teacher Mistral-NeMo-12B models. Bold numbers represent the best amongst the 8B model class.Overview of model pruning and distillation Model pruning is the process of making a model smaller and leaner, either by dropping layers (depth pruning) or dropping neurons and attention heads and embedding channels (width pruning). Pruning is often accompanied by some amount of retraining for accuracy recovery.Model distillation is a technique used to transfer knowledge from a large, complex model, often called the teacher model, to a smaller, simpler student model. The goal is to create a more efficient model that retains much of the predictive power of the original, larger model while being faster and less resource-intensive to run. Herein, we employ distillation as a light retraining procedure after pruning, on a dataset much smaller than that used in model training from scratch.Iterative pruning and distillation is an

SLMs for cognition include: Mistral-Nemo-Minitron-8B-128k-Instruct - A state of the art small language model that tops the charts in terms of instruction following capabilities, a key competency for Autonomous Game Characters Mistral-Nemo-Minitron-4B-128k-Instruct - Same model, just smaller Mistral-Nemo-Minitron-2B-128k-Instruct - And even smaller! Fits in as little as 1.5GB of VRAM Action - Models To Act In The World Taking action comes in many forms - from speech, to game actions, to longer term planning. To effectively perform actions, developers can use a combination of models and strategies: Action Selection - Given the finite actions that can be taken in the game, the SLM can choose the best appropriate action (as in inZOI below) Text-to-Speech - Great text to speech models like Elevenlabs.io or Cartesia can be used to convert a text response to an aural response Strategic Planning - When processing and reasoning about a large corpus of data, these agents can reach out to larger models that can provide a higher level, lower frequency strategy. Often this is a cloud LLM API or a CoT (Chain-of-Thought) series of prompts to the SLM Reflection - One of the important actions is to reflect on the results of prior actions. “Did I choose the right thing?” This action can produce better future actions over time and allows the character to self correct Memory - Models To Remember The World Memory is crucial for Autonomous Game Characters to be able to recall their prior perceptions, actions, and cognitions. It’s also useful for tracking long term goals and motivations that may be less relevant in the immediate context. Using a technique called Retrieval Augmented Generation (RAG), developers can use similarity searches to “remember” relevant information to the current prompt: E5-Large-Unsupervised - Using the NVIDIA In-Game Inference SDK, developers can use our optimized embedding model for embeddings within the game process Using a combination of the models and techniques above, our partners have crafted the first autonomous game character experiences. Let’s take a glimpse into the future. Autonomous Characters Come To Games - From Smart AI Teammates To Constantly Evolving Enemies NVIDIA ACE characters