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Mistral-NeMo-Minitron-8B-Base - GGUF

Mistral-NeMo-Minitron-8B-Base is a base text-to-text model that can be adopted for a variety of natural language generation tasks. It is a large language model (LLM) obtained by pruning and distilling the Mistral-NeMo 12B; specifically, we prune the embedding dimension and MLP intermediate dimension in the model. Following pruning, we perform continued training with distillation using 380 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.

Model Developer: NVIDIA

Model Dates: Mistral-NeMo-Minitron-8B-Base was trained between July 24, 2024 and August 10, 2024.

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

Mistral-NeMo-Minitron-8B-Base uses a model embedding size of 4096, 32 attention heads, MLP intermediate dimension of 11520, with 40 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: Mistral-NeMo

Input Type(s): Text

Input Format(s): String

Input Parameters: One Dimensional (1D)

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:

Operating System(s):

Data Collection Method by Dataset: Automated

Labeling Method by Dataset: Not Applicable

Properties: The training corpus for Mistral-NeMo-Minitron-8B-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: Training was done in 2024, 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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