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MPT-7B

by Community

Introducing MPT-7B, the first entry in our MosaicML Foundation Series. MPT-7B is a transformer trained from scratch on 1T tokens of text and code. It is open source, available fo

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OSS

MPT-7B

Added 1 June 2026

Overview

MPT-7B is an open-source transformer model trained from scratch on 1 trillion tokens of text and code. It matches the quality of LLaMA-7B and is available for commercial use. The model was trained on the MosaicML platform in 9.5 days with zero human intervention at a cost of ~$200k.

Best for

Best for
Developers and organizations needing a high-quality open-source LLM with commercial rights for text and code tasks.

Use cases

  • Fine-tuning for domain-specific language tasks
  • Generating text or code in production applications
  • Building custom LLM solutions with a commercially friendly license

Notes

MPT-7B is an open-source transformer model trained from scratch on 1 trillion tokens of text and code. It matches the quality of LLaMA-7B and is available for commercial use. The model was trained on the MosaicML platform in 9.5 days with zero human intervention at a cost of ~$200k.

Use cases

  • Fine-tuning for domain-specific language tasks
  • Generating text or code in production applications
  • Building custom LLM solutions with a commercially friendly license

Pros

  • Open source and freely available for commercial use
  • Matches LLaMA-7B quality despite lower training cost
  • Trained with zero human intervention, demonstrating scalability

Cons

  • Requires significant GPU resources for inference and fine-tuning
  • Smaller context window and capacity compared to larger models like MPT-30B
  • Community is smaller than LLaMA’s, potentially less third-party tooling

Indexed from awesome-llm and enriched against its public facts.

Pros

  • Open source and freely available for commercial use
  • Matches LLaMA-7B quality despite lower training cost
  • Trained with zero human intervention, demonstrating scalability

Cons

  • Requires significant GPU resources for inference and fine-tuning
  • Smaller context window and capacity compared to larger models like MPT-30B
  • Community is smaller than LLaMA’s, potentially less third-party tooling

Pairs with

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