NVIDIA Nemotron-4 340B
NVIDIA unveiled Nemotron-4 340B, an open model family that allows developers to produce synthetic data for large language model (LLM) training in the industrial, retail, healthcare, and finance sectors, among other industries.
Robust training datasets might be prohibitively expensive and difficult to get, but they are essential to the performance, accuracy, and quality of responses from a bespoke LLM.
Nemotron-4 340B provides developers with a scalable, free method of creating synthetic data that may be used to construct robust LLMs, with a uniquely liberal open model licence.
Nemotron
The base, instruct, and reward models in the Nemotron-4 340B family work together to create synthetic data that is used to train and improve LLMs. The models are designed to function with NVIDIA NeMo, an open-source platform that enables data curation, customisation, and evaluation during the whole model training process. Additionally, they are designed using the open-source NVIDIA TensorRT-LLM library in mind for inference.
You may now get Nemotron-4 340B from Hugging Face. The models will be packaged as an NVIDIA NIM microservice with a standard application programming interface that can be deployed anywhere.
Getting Around the Nemotron to Produce Synthetic Data
LLMs can be useful in situations where access to big, diverse labelled datasets is limited for developers creating synthetic training data.
The Nemotron-4 340B Instruct model generates a variety of synthetic data that closely resembles real-world data, enhancing data quality to boost the robustness and performance of custom LLMs in a range of domains.
A large language model (LLM) called Nemotron-4-340B-Instruct can be utilised in a pipeline for synthetic data creation to produce training data that will aid in the development of LLMs by researchers and developers. This is a refined Nemotron-4-340B-Base model designed for English-speaking single- and multi-turn chat scenarios. A context length of 4,096 tokens is supported.
A dataset of 9 trillion tokens, comprising a wide range of English-based literature, more than 50 natural languages, and more than 40 coding languages, was used to pre-train the base model. The Nemotron-4-340B-Instruct model then underwent more alignment procedures, such as:
- Monitoring and Adjustment (SFT)
- Optimisation of Direct Preference (DPO)
- Preference Optimisation with Reward Awareness (RPO)
While over 98% of the data utilised for supervised fine-tuning and preference fine-tuning (DPO & RPO) was synthesised by NVIDIA’s data creation pipeline, the company only relied on about 20,000 human-annotated data throughout the alignment process.
As a result, a model that can produce high-quality synthetic data for a range of use scenarios is created that is matched for human chat preferences and enhances mathematical thinking, coding, and instruction following.
NVIDIA affirms under the terms of the NVIDIA Open Model Licence:
- The models can be used commercially.
- It is not prohibited for you to develop and share derivative models.
- Any outputs produced utilising the Models or Derivative Models are not attributed to NVIDIA.
Developers can then utilise the Nemotron-4 340B Reward model to filter for high-quality responses, which will improve the quality of the AI-generated data. Five criteria are used by Nemotron-4 340B Reward to score responses: verbosity, coherence, accuracy, helpfulness, and complexity. As of right now, it holds the top spot on the AI2-created Hugging Face RewardBench scoreboard, which assesses the strengths, vulnerabilities, and safety of reward models.
By combining their private data with the included HelpSteer2 dataset, researchers can further customise the Nemotron-4 340B Base model to construct their own teach or reward models.
Large language models (LLMs) such as Nemotron-4-340B-Base can be utilised in a synthetic data production pipeline to produce training data that aids in the development of LLMs by researchers and developers. With 4,096 tokens in the context, this model supports 340 billion parameters. It has been pre-trained on a total of 9 trillion tokens, which include more than 40 coding languages, more than 50 natural languages, and a wide range of English-based writings.
To enhance the quality of the pre-trained model, a continuous pre-training of 1 trillion tokens was carried out on top of the pre-trained model following an initial pre-training phase of 8 trillion tokens. NVIDIA changed the distribution of the data used during continuous pre-training from the one that was present at the start of training.
TensorRT-LLM Inference Optimisation, NeMo Fine-Tuning
Developers can maximise the effectiveness of their instruct and reward models to provide synthetic data and score responses by utilising the open-source NVIDIA NeMo and NVIDIA TensorRT-LLM.
Tensor parallelism a kind of model parallelism in which individual weight matrices are divided among several GPUs and servers is a sort of parallelism that is optimised into all Nemotron-4 340B models using TensorRT-LLM. This allows for effective inference at scale.
Nemotron-4 340B the NeMo architecture allows Base, which was trained on 9 trillion tokens, to be tailored to certain use cases or domains. Extensive pretraining data aids in this fine-tuning process, which produces outputs that are more accurate for particular downstream tasks.
The NeMo framework offers a range of customisation options, such as parameter-efficient fine-tuning techniques like low-rank adaptation, or LoRA, and supervised fine-tuning techniques.
Developers can use NeMo Aligner and datasets annotated by Nemotron-4 340B Reward to align their models and improve model quality. Using methods like reinforcement learning from human feedback (RLHF), a model’s behaviour is refined during alignment, a crucial phase in LLM training, to make sure its outputs are accurate, safe, acceptable for the context, and compatible with the model’s stated goals.
NeMo and TensorRT-LLM are also available to businesses via the cloud-native NVIDIA AI Enterprise software platform, which offers rapid and effective runtimes for generative AI foundation models. This platform is ideal for those looking for enterprise-grade support and security for production environments.
Assessing Model Security and Beginning
After undergoing a thorough safety examination that included adversarial tests, the Nemotron-4 340B Instruct model demonstrated good performance over a broad spectrum of risk indicators. It is still important for users to carefully assess the model’s outputs to make sure the artificially created data is appropriate, secure, and accurate for their use case.
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