These LLMs are all licensed for commercial use (e.g., Apache 2.0, MIT, OpenRAIL-M). Contributions welcome!
| Language Model | Release Date | Checkpoints | Paper/Blog | Params (B) | Context Length | Licence |
|---|---|---|---|---|---|---|
| SantaCoder | 2023/01 | santacoder | SantaCoder: don't reach for the stars! | 1.1 | 2048 | OpenRAIL-M v1 |
| StarCoder | 2023/05 | starcoder | StarCoder: A State-of-the-Art LLM for Code, StarCoder: May the source be with you! | 15 | 8192 | OpenRAIL-M v1 |
| StarChat Alpha | 2023/05 | starchat-alpha | Creating a Coding Assistant with StarCoder | 16 | 8192 | OpenRAIL-M v1 |
| Replit Code | 2023/05 | replit-code-v1-3b | Training a SOTA Code LLM in 1 week and Quantifying the Vibes — with Reza Shabani of Replit | 2.7 | infinity? (ALiBi) | CC BY-SA-4.0 |
| CodeGen2 | 2023/04 | codegen2 1B-16B | CodeGen2: Lessons for Training LLMs on Programming and Natural Languages | 1 - 16 | 2048 | Apache 2.0 |
| CodeT5+ | 2023/05 | CodeT5+ | CodeT5+: Open Code Large Language Models for Code Understanding and Generation | 0.22 - 16 | 512 | BSD-3-Clause |
| Name | Release Date | Paper/Blog | Dataset | Tokens (T) | License |
|---|---|---|---|---|---|
| starcoderdata | 2023/05 | StarCoder: A State-of-the-Art LLM for Code | starcoderdata | 0.25 | Apache 2.0 |
| RedPajama | 2023/04 | RedPajama, a project to create leading open-source models, starts by reproducing LLaMA training dataset of over 1.2 trillion tokens | RedPajama-Data | 1.2 | Apache 2.0 |
| Name | Release Date | Paper/Blog | Dataset | Samples (K) | License |
|---|---|---|---|---|---|
| MPT-7B-Instruct | 2023/05 | Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs | dolly_hhrlhf | 59 | CC BY-SA-3.0 |
| databricks-dolly-15k | 2023/04 | Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM | databricks-dolly-15k | 15 | CC BY-SA-3.0 |
| OIG (Open Instruction Generalist) | 2023/03 | THE OIG DATASET | OIG | 44,000 | Apache 2.0 |
| Name | Release Date | Paper/Blog | Dataset | Samples (K) | License |
|---|---|---|---|---|---|
| OpenAssistant Conversations Dataset | 2023/04 | OpenAssistant Conversations - Democratizing Large Language Model Alignment | oasst1 | 161 | Apache 2.0 |
- Leaderboard by lmsys.org
- Evals by MosaicML
- Holistic Evaluation of Language Models (HELM)
- LLM-Leaderboard
- TextSynth Server Benchmarks
- Open LLM Leaderboard by Hugging Face
- Apache 2.0: Allows users to use the software for any purpose, to distribute it, to modify it, and to distribute modified versions of the software under the terms of the license, without concern for royalties.
- MIT: Similar to Apache 2.0 but shorter and simpler. Also, in contrast to Apache 2.0, does not require stating any significant changes to the original code.
- CC BY-SA-4.0: Allows (i) copying and redistributing the material and (ii) remixing, transforming, and building upon the material for any purpose, even commercially. But if you do the latter, you must distribute your contributions under the same license as the original. (Thus, may not be viable for internal teams.)
- OpenRAIL-M v1: Allows royalty-free access and flexible downstream use and sharing of the model and modifications of it, and comes with a set of use restrictions (see Attachment A)
- BSD-3-Clause: This version allows unlimited redistribution for any purpose as long as its copyright notices and the license's disclaimers of warranty are maintained.
Disclaimer: The information provided in this repo does not, and is not intended to, constitute legal advice. Maintainers of this repo are not responsible for the actions of third parties who use the models. Please consult an attorney before using models for commercial purposes.
- Complete entries for context length, and check entries with
? -
Add number of tokens trained?(see considerations) - Add (links to) training code?
- Add (links to) eval benchmarks?