Llama-3.1-Minitron-4B-Width-Base is a base text-to-text model that can be adopted for a variety of natural language generation tasks. It is obtained by pruning Llama-3.1-8B; specifically, we prune model embedding size and MLP intermediate dimension. Following pruning, we perform continued training with distillation using 94 billion tokens to arrive at the final model; we use the continuous pre-training data corpus used in Nemotron-4 15B for this purpose. Please refer to our technical report for more details.

This model is ready for commercial use.

Model Developer: NVIDIA

Model Dates: Llama-3.1-Minitron-4B-Width-Base was trained between July 29, 2024 and Aug 3, 2024.

This model is released under the NVIDIA Open Model License Agreement.

Llama-3.1-Minitron-4B-Width-Base uses a model embedding size of 3072, 32 attention heads, MLP intermediate dimension of 9216, with 32 layers in total. Additionally, it uses Grouped-Query Attention (GQA) and Rotary Position Embeddings (RoPE).

Architecture Type: Transformer Decoder (Auto-Regressive Language Model)

Network Architecture: Llama-3.1

Input Type(s): Text

Input Format(s): String

Input Parameters: None

Other Properties Related to Input: Works well within 8k characters or less.

Output Type(s): Text

Output Format: String

Output Parameters: 1D

Other Properties Related to Output: None

Support for this model will be added in the upcoming transformers release. In the meantime, please install the library from source:

We can now run inference on this model:

Supported Hardware Microarchitecture Compatibility:

[Preferred/Supported] Operating System(s):

Data Collection Method by Dataset: Automated

Labeling Method by Dataset: Not Applicable

Properties: The training corpus for Llama-3.1-Minitron-4B-Width-Base consists of English and multilingual text, as well as code. Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. The corpus spans domains including legal, math, science, finance, and more. In our continued training set, we introduce a small portion of question-answering, and alignment style data to improve model performance.

Data Freshness: The pretraining data has a cutoff of June 2023.

5-shot performance. Language Understanding evaluated using Massive Multitask Language Understanding:

Zero-shot performance. Evaluated using select datasets from the LM Evaluation Harness with additions:

Code generation performance. Evaluated using MBPP:

Engine: TensorRT-LLM

Test Hardware: NVIDIA A100

DType: BFloat16

The model was trained on data that contains toxic language, unsafe content, and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.

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