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Home/Categories/AI & Machine Learning/OpenLLaMA
icon of OpenLLaMA

OpenLLaMA

Open source alternative to OpenAI, Google Cloud Vertex AI (Gemini) and

Anthropic (Claude)

An open-source reproduction of Meta AI’s LLaMA models, offering permissively licensed weights in 3B, 7B, and 13B parameter sizes compatible with both PyTorch and JAX.

7.5K starsApache-2.0
Visit websiteGitHub repo
image of OpenLLaMA
Contents
  1. 01Who OpenLLaMA is for
  2. 02The problem it solves
  3. 03How it solves it
  4. 04Strengths and trade-offs
  5. 05FAQ
  6. 06Similar open-source tools
TL;DR

OpenLLaMA is an open reproduction of Meta's LLaMA-style language models for researchers who need downloadable model weights and training transparency. The project released 3B, 7B, and 13B parameter models compatible with PyTorch and JAX. Best for LLM researchers and builders who need a historical open-weight baseline rather than a current frontier model.Apache-2.0 · 7.5K stars

who it's for

Who OpenLLaMA is for#

Researchers comparing LLM training methods

OpenLLaMA gives labs a known LLaMA-style baseline for controlled experiments across sizes and frameworks.

Skip if:

Use a newer open model if your goal is production answer quality rather than reproducible research.

Tool builders testing model infrastructure

Teams can use OpenLLaMA to test inference, fine-tuning, conversion, and evaluation pipelines without relying on a closed API.

Skip if:

Skip it if your system requires current instruction-following performance or commercial support.

the problem
frequently asked

FAQ#

Is OpenLLaMA the same as Meta LLaMA?
What model sizes did OpenLLaMA release?
Should I use OpenLLaMA for production?
also worth a look

Similar open-source tools#

Qwen

Qwen

Alibaba's Apache 2.0 LLM in sizes from small to frontier scale

21.1KPythonApache-2.0

Repository

Stars
7.5K
Forks
406
License
Apache-2.0
Last commit
1051 days ago
Last verified
May 13, 2026
Repo
openlm-research/open_llama ↗

Additional details

Open issues
40
Contributors
3
First release
2023

Categories

AI & Machine LearningLLMOps & AI ToolingDeveloper Tools

Tags

LLMAI SDKDeveloper FrameworkAI AgentsLLMOpsSelf Hosted

The problem it solves#

Proprietary or restricted-weight LLMs make it hard to reproduce research, inspect training choices, or run controlled experiments. Teams can call an API, but they cannot easily compare tokenizer choices, pretraining data, fine-tuning methods, or deployment behavior against an accessible baseline.

Open-weight models help, but they age quickly. Researchers need to know whether they are choosing a current production model or a reproducible reference point.

how OpenLLaMA solves it

How it solves it#

LLaMA-style open reproduction

OpenLLaMA was created as an open reproduction of Meta's LLaMA family, giving researchers a downloadable model family for experimentation.

Multiple model sizes

The project published 3B, 7B, and 13B parameter versions, which lets teams choose between smaller experiments and larger baseline comparisons.

PyTorch and JAX compatibility

The project publishes PyTorch weights for Hugging Face Transformers and JAX weights for EasyLM, covering two common research and deployment stacks.

Apache-2.0 code and checkpoint license

The project README states that the EasyLM training framework and OpenLLaMA checkpoint weights are permissively licensed under Apache-2.0. Teams should still review the exact Hugging Face model page before commercial use.

strengths · trade-offs

Strengths and trade-offs#

Strengths

  • Useful reproducible baselineOpenLLaMA gives researchers a known open-weight LLaMA-style baseline for experiments, model comparisons, and tooling tests.
  • Smaller sizes lower experiment costThe 3B and 7B options are more practical for labs and individual builders than only working with very large models.
  • Works with common ML stacksPyTorch and JAX compatibility makes it easier to integrate OpenLLaMA into existing research code and fine-tuning workflows.

Trade-offs

  • -Not a current frontier modelOpenLLaMA is valuable as a baseline, but newer open models usually outperform it for production assistants, coding, and instruction-following tasks.
  • -Weight terms need separate reviewRepository licensing does not always answer every model-weight use case. Teams should verify the exact terms attached to the weights they download.

Usually no for new production apps. It is more useful as a research baseline or infrastructure test model than as a current best-performing LLM.

Steel‑LLM

Steel‑LLM

1B Chinese LLM with public weights, training code, and data

800Jupyter Notebook
TinyLLaMA

TinyLLaMA

Compact 1.1B LLaMA model trained on 3 trillion tokens

9KPythonApache-2.0
Falcon LLM

Falcon LLM

Apache 2.0-licensed LLM from TII, from 1B to 180B parameters

9.2KPythonApache-2.0
jcode

jcode

Next-gen coding agent harness for efficient workflows

6KRustMIT
9Router

9Router

Smart AI Router with 3-Tier Fallback

9.8KJavaScriptMIT

No. OpenLLaMA is an open reproduction inspired by LLaMA-style models, not an official Meta model release.

OpenLLaMA is commonly associated with 3B, 7B, and 13B parameter model releases.