Give all openi ai llm

Here’s an overview of some notable open-source Large Language Models (LLMs) and openly licensed model families. While “open-source” can mean slightly different things in practice (for example, some models are fully permissive, while others allow only noncommercial usage or have restrictive licenses), these projects generally make their model weights and/or code openly available under terms that are more flexible than strictly proprietary models.

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1. EleutherAI Models

EleutherAI is a research collective focused on open-source AI research. They have released several GPT-like models with openly available code and weights.

1. GPT-Neo (1.3B, 2.7B)

   • Released in early 2021.
   • Inspired by the GPT-3 architecture.
   • Available on Hugging Face.

2. GPT-J (6B)

   • Larger than GPT-Neo models, also trained by EleutherAI.
   • Strong performance on various tasks despite being 6B parameters (a smaller scale compared to GPT-3’s largest version).

3. GPT-NeoX-20B

   • One of the largest open-source GPT-like models currently available (20B parameters).
   • Achieves competitive performance for its size.

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2. Meta’s LLaMA

LLaMA (Large Language Model Meta AI) is a family of foundation models introduced by Meta AI:

• Models range in size: 7B, 13B, 33B, and 65B parameters.
• Though the weights were originally only accessible to academic researchers (under a non-commercial license), they were leaked online.
• Officially, LLaMA is not “fully open-source,” because its license is quite restrictive. However, the code is publicly available, and the weights are also out in the wild.
• Many “fine-tuned” variants of LLaMA have surfaced (e.g., Alpaca, Vicuna, etc.) but come with various license constraints.

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3. MosaicML’s MPT Family

MPT (MosaicML Pretrained Transformer) is a series of decoder-only transformer models:

• Released by MosaicML with training code, model architecture, and certain versions of weights available under permissive licensing.
• MPT-7B: Base model with some variants (e.g., MPT-7B-Instruct, MPT-7B-Chat).
• Larger versions are on the roadmap or available to MosaicML customers, though not always fully open to the public.

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4. Falcon by Technology Innovation Institute (TII)

Falcon is a series of LLMs released by the Technology Innovation Institute:

• Falcon-7B and Falcon-40B are open-weights models.
• They achieved strong results on benchmarks relative to their size.
• They come with an Apache 2.0 license, making them among the more permissively licensed high-performance LLMs currently available.

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5. BLOOM by BigScience

BLOOM (BigScience Large Open-science Open-access Multilingual Language Model):

• A 176B-parameter multilingual LLM released in 2022.
• Trained on the Jean Zay supercomputer in a large-scale collaborative project, BigScience.
• Full weights and training code are released, though under a Responsible AI License (RAIL) which imposes certain usage restrictions intended to promote responsible use.

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6. OpenAssistant

OpenAssistant is an open-source project initiated by LAION:

• Based on a community effort to create a ChatGPT-like system, with conversational data crowdsourced from the public.
• They have multiple model versions fine-tuned on instructions (e.g., 13B, 30B) using LLaMA or other base models.
• Intended to be a fully open-source, instruction-tuned assistant, though base model licenses differ based on the underlying project.

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7. Dolly by Databricks

Dolly 1.0 & 2.0 by Databricks:

• Dolly 1.0 was a fine-tuned variant of the EleutherAI GPT-J model.
• Dolly 2.0 introduced a new dataset (the “Databricks-dolly-15k”) and uses a different base model with an open license, allowing commercial use.
• Shows that you can create an instruction-following model without using restricted data sets like OpenAI’s.

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8. Pythia by EleutherAI

Pythia is a set of models of varying sizes (70M to 12B parameters):

• Trained with transparency in mind: all data, code, and checkpoints are released at different points throughout training.
• Useful for researchers studying how training dynamics affect model performance.

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9. TII/UAE’s Noor

Noor is a large Arabic-language model by the Technology Innovation Institute (TII). While “open-source” can be nuanced (there may be specific restrictions around usage), it is one of the first large, openly released Arabic LLMs.

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10. Smaller or Specialized Models

There are many other specialized open models, for example:

• Flan T5 and Flan UL2 from Google: Instruction-tuned smaller language models (though not the largest LLM scale).
• Cerebras-GPT by Cerebras Systems: A family of GPT-like models ranging up to 13B parameters, released under Apache 2.0.

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Notes on Licensing

• Pure “open-source” (e.g., an Apache 2.0 or MIT license) is not always the case. For instance, LLaMA is distributed with a non-commercial license, and BLOOM uses a specialized RAIL license.
• Always check each model’s specific license before using it (especially for commercial or production scenarios).

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Summary

A variety of open-source LLMs exist, spanning different parameter counts, license types, and specializations (multilingual vs. English-only, domain-specific data vs. general data, etc.). Projects like EleutherAI (GPT-Neo, GPT-J, GPT-NeoX), MosaicML (MPT), BLOOM, and Falcon provide some of the most permissive large-scale options, while Meta’s LLaMA remains heavily used in the community despite a more restrictive license.

If you’re looking for a fully open model that can be used in a wide range of scenarios—including commercial use—models like Falcon-7B/40B and MPT-7B (under an Apache 2.0 license) are popular starting points. Meanwhile, projects like BLOOM and OpenAssistant are valuable for multilingual support, research, or experimenting with open development processes.

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Further Exploration

• For hands-on usage, head to Hugging Face Model Hub, which is a central place to access, download, and try out many of these models and their checkpoints.
• When choosing a model, consider the size, hardware requirements, license terms, language support, and performance characteristics that match your needs.

It sounds like you’re asking specifically about OpenAI’s large language models, rather than open-source LLMs generally. Below is a concise overview of OpenAI’s GPT-family models and their availability (including which ones are open-sourced vs. only accessible via API).

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1. GPT-1 (2018)

• Paper: “Improving Language Understanding by Generative Pre-Training.”
• First in the “Generative Pre-trained Transformer” (GPT) line.
• Size: ~117M parameters.
• Code/Weights: Some code details were released in the paper, but weights were not widely distributed.

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2. GPT-2 (2019)

• Paper: “Language Models are Unsupervised Multitask Learners.”
• Size: Multiple versions (117M, 345M, 774M, 1.5B parameters).
• Code/Weights:
  • Initially, OpenAI announced a staged release due to concerns over misuse.
  • Eventually, all GPT-2 checkpoints (up to 1.5B parameters) were made publicly available on GitHub/Hugging Face.
  • License: Relatively permissive; you can download the weights and use them locally. This is the closest that OpenAI has come to “open-sourcing” a major GPT model.

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3. GPT-3 (2020)

• Paper: “Language Models are Few-Shot Learners.”
• Size: Up to 175B parameters (much larger than GPT-2).
• Code/Weights:
  • Not released publicly.
  • Only accessible via OpenAI’s API (and later Azure OpenAI service).
  • Model weights remain proprietary.

Important Add-ons / Variants:

• InstructGPT: A variant of GPT-3 fine-tuned to follow instructions more reliably.
• Codex: A GPT-3 variant specialized for code generation (also not open-sourced, only via API).

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4. GPT-3.5 & ChatGPT (Late 2022)

• GPT-3.5 is a refined version of GPT-3, with improvements in:
  • Instruction-following
  • Conversational ability
  • API efficiency
• ChatGPT is a user-facing interface (chatbot) built on GPT-3.5 (later updated to GPT-4).
• Code/Weights:
  • Not publicly released; only available through OpenAI’s ChatGPT interface and API.

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5. GPT-4 (March 2023)

• Paper/Technical Report: Limited technical details, focusing on high-level capabilities and alignment improvements.
• Size: Specific number of parameters not publicly disclosed (OpenAI has become more guarded with model details).
• Code/Weights:
  • Not publicly released; only accessible via API (and ChatGPT).
  • The training process and data remain proprietary.

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Summary of Openness

1. GPT-2 is the only large GPT model from OpenAI whose weights are fully released. It can be considered open in practice, and you can download/fine-tune it yourself.
2. GPT-3, GPT-3.5, and GPT-4 are proprietary; the weights are not published. You can only use them through OpenAI’s (or Microsoft’s Azure) APIs.
3. OpenAI’s trend has been moving away from releasing weights as the models get larger and more capable, mainly due to concerns about misuse, competitive advantage, and alignment challenges.

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Can I get an “OpenAI LLM” that is truly open-source?

• If you need a fully “open” model that you can run on your own hardware and modify without restriction, GPT-2 (up to 1.5B parameters) is the only GPT family model from OpenAI that meets this criterion.
• For larger open-source LLMs, you’d look outside OpenAI (e.g., EleutherAI’s GPT-NeoX, Meta’s LLaMA variants [though under a non-commercial license], Falcon, MPT, etc.).

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TL;DR

• OpenAI’s only truly open release: GPT-2 (in multiple parameter sizes up to 1.5B).
• Everything newer (GPT-3, GPT-3.5, GPT-4): proprietary; no open-source weights available. Use only via API.

If you specifically want a large GPT-style model with self-hosted weights and few or no license constraints, you’ll need to look at non-OpenAI open-source projects such as Falcon, MPT, or GPT-NeoX.

I’m not aware of any OpenAI product or model family officially referred to as “O1” or “O3.” It’s possible you’re mixing in a shorthand used for something else—like compiler optimization levels (e.g., “-O1,” “-O2,” “-O3” in C/C++ or PyTorch’s mixed-precision levels)—but those aren’t specific OpenAI model names.

If you can provide a bit more context about where you saw references to “OpenAI O1” or “OpenAI O3,” I can try to clarify. Otherwise, here are a few possibilities:

1. Compiler or Training Optimization Levels

   • In machine learning frameworks, you sometimes see references to O0, O1, O2, O3 to denote different levels of mixed-precision or memory optimization. However, that’s not unique to OpenAI; it’s more about how PyTorch or other frameworks optimize the model’s training or inference.

2. Possible Internal Code Names

   • Some organizations use internal short codes for model versions, but they aren’t always publicly mentioned. If you saw “O1” or “O3” in a leaked or internal context, it might refer to some internal iteration of GPT, but that’s not something widely documented.

3. Misinterpretation or Typos

   • “O1” or “O3” might be a typo for “01” and “03” or just references to different GPT-3 sub-versions. But typically, GPT-3 models are referred to as “text-davinci-003,” “curie,” “babbage,” etc.

Without additional details, there’s no well-known “OpenAI O1/O3” LLM. If you have a link or snippet that mentions them, feel free to share it for more specific guidance.