Yes, the Llama Community License explicitly allows fine-tuning Llama 4 and redistributing the resulting derivative models, which is one of the license's most useful provisions for enterprises that want to customize the model on proprietary data. Any derivative model that is redistributed must include Llama at the beginning of its name, must carry a copy of the Llama license and acceptable use policy, and if the redistributed product includes an AI model, it must state that the model is built with Llama as required attribution. The output-use restriction still applies to derivatives: neither the original model nor a fine-tuned version may be used to train or improve a separate, non-Llama large language model. The 700 million monthly active user threshold that requires a special license from Meta applies to derivatives as well as the base model, so a highly successful product built on a Llama fine-tune could eventually trigger that requirement. For internal, non-redistributed fine-tunes used only within a company, none of the naming or attribution requirements apply since the model is not being distributed externally. Nanobase AI handles both the fine-tuning work and the license compliance review when building custom Llama-based models for clients.
Compliance is a process, not a one-time reading of the license
Knowing that fine-tuning and redistribution are allowed under the Llama Community License is only the starting point; the practical work is building a repeatable process so every team that fine-tunes a Llama model internally follows the same naming, attribution and output-use rules without needing a lawyer to review every release. Always check the current license text before finalizing a release, since Meta can update terms for future model versions even though a model already downloaded stays governed by the version in effect at download time.
Checklist before redistributing a Llama-based fine-tune
- Confirm the derivative's name begins with "Llama", as the license requires for any redistributed model built from Llama weights.
- Include a copy of the Llama license and acceptable use policy alongside the redistributed model or product.
- Add a "Built with Llama" notice if the product being distributed includes the AI model itself, not just its outputs.
- Verify the derivative is not being used to train a separate, non-Llama large language model, since the output-use restriction applies to fine-tunes as well as the base model.
- Check whether the product or service has crossed, or is likely to cross, 700 million monthly active users, since that threshold requires a separate license directly from Meta and applies to derivatives, not just the original model.
- Log the license version and download date for the specific Llama checkpoint the fine-tune was built from, so a future audit can reference exactly which terms applied.
Internal use versus external redistribution: a meaningful line
| Scenario | Naming/attribution required | Output-use restriction applies | 700M MAU threshold applies |
|---|---|---|---|
| Fine-tuned internally, used only by employees | No | Yes | No |
| Fine-tuned and offered as a feature inside a paid product | Yes | Yes | Yes, once scale is reached |
| Fine-tuned and released publicly (open weights) | Yes | Yes | Yes, once scale is reached |
| Fine-tuned, outputs sold as a service but weights never distributed | Depends on exact license wording for the current version | Yes | Yes |
The naming and attribution obligations exist specifically because Meta considers redistribution of the model itself, not merely its outputs, to be the triggering event, which is why an internal-only fine-tune carries a meaningfully lighter compliance burden than any deployment that hands the weights to a party outside the organization.
The clause most teams overlook
The output-use restriction, prohibiting use of Llama or its derivatives to train or improve a separate non-Llama model, is easy to violate unintentionally when a team uses a Llama model's outputs as training data for an entirely different model architecture, a common practice in distillation workflows. Any distillation pipeline that uses Llama-generated outputs to train a non-Llama student model needs a specific compliance check against this clause before proceeding, since it is one of the few Llama license terms that has no equivalent in a fully permissive license like Apache 2.0.
Frequently asked questions
Does fine-tuning Llama on proprietary company data change any license obligations?
No, the license terms apply to the model itself and its redistribution, not to the training data used for fine-tuning. Proprietary training data ownership is a separate matter governed by your own data policies, not by the Llama license.
Can a Llama-based fine-tune be sold commercially?
Yes, the Llama Community License permits commercial use of derivatives, subject to the naming, attribution and output-use terms, and to the 700 million monthly active user threshold that requires a direct license from Meta once crossed.
What happens if Meta updates the license after we've already deployed a fine-tune?
Generally, models downloaded under an earlier version of the license continue to be governed by that version's terms, but always check the current license text for the specific version attached to your downloaded checkpoint rather than assuming this applies uniformly, since license structures can change between major model releases.
How Nanobase AI helps
Nanobase AI handles both the fine-tuning engineering and the license compliance review when building custom Llama-based models for clients, maintaining the version-and-download log this checklist requires. This work often pairs with our guidance on choosing between Llama 4 and Qwen 3 for enterprise workloads when a client is still deciding which base model family to standardize on.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.