Hello,
I would like to indicate "Copilot" in the Edge browser as being potentially relevant to Wikipedia [1][2].
It is foreseeable that end-users will be able to open sidebars in their Web browsers and subsequently chat with large language models about the contents of specific Web documents, e.g., encyclopedia articles. Using Web browsers, there can be task contexts available, including the documents or articles in users' current tabs, potentially including users' scroll positions, potentially including users' selections or highlightings of content.
I, for one, am thinking about how Web standards, e.g., Web schema, can be of use for amplifying these features and capabilities for end-users.
Best regards,
Adam Sobieski
[1] https://learn.microsoft.com/en-us/deployedge/microsoft-edge-relnote-stable-channel?ranMID=24542#version-1110166141-march-13-2023
[2] https://www.engadget.com/microsoft-edge-ai-copilot-184033427.html
________________________________
From: Kimmo Virtanen <kimmo.virtanen@wikimedia.fi>
Sent: Friday, March 17, 2023 8:17 AM
To: Wikimedia Mailing List <wikimedia-l@lists.wikimedia.org>
Subject: [Wikimedia-l] Re: Bing-ChatGPT
Hi,
The development of open-source large language models is going forward. The GPT-4 was released and it seems that it passed the Bar exam and tried to hire humans to solve catchpas which were too complex. However, the development in the open source and hacking side has been pretty fast and it seems that there are all the pieces for running LLM models in personal hardware (and in web browsers). Biggest missing piece is fine tuning of open source models such as Neox for the English language. For multilingual and multimodal (for example images+text) the model is also needed.
So this is kind of a link dump for relevant things for creation of open source LLM model and service and also recap where the hacker community is now.
1.) Creation of an initial unaligned model.
* Possible models
* 20b Neo(X)<https://github.com/EleutherAI/gpt-neox> by EleutherAI (Apache 2.0)
* Fairseq Dense<https://huggingface.co/KoboldAI/fairseq-dense-13B> by Facebook (MIT-licence)
* LLaMa<https://ai.facebook.com/blog/large-language-model-llama-meta-ai/> by Facebook (custom license, leaked research use only)
* Bloom<https://huggingface.co/bigscience/bloom> by Bigscience (custom license<https://huggingface.co/spaces/bigscience/license>. open, non-commercial)
2.) Fine-tuning or align
* Example: Standford Alpaca is ChatGPT fine-tuned LLaMa
* Alpaca: A Strong, Replicable Instruction-Following Model<https://crfm.stanford.edu/2023/03/13/alpaca.html>
* Train and run Stanford Alpaca on your own machine<https://replicate.com/blog/replicate-alpaca>
* Github: Alpaca-LoRA: Low-Rank LLaMA Instruct-Tuning<https://github.com/tloen/alpaca-lora>
3.) 8,4,3 bit-quantization of model for reduced hardware requirements
* Running LLaMA 7B and 13B on a 64GB M2 MacBook Pro with llama.cpp<https://til.simonwillison.net/llms/llama-7b-m2>
* Github: bloomz.cpp<https://github.com/NouamaneTazi/bloomz.cpp> & llama.cpp<https://github.com/ggerganov/llama.cpp> (C++ only versions)
* Int-4 LLaMa is not enough - Int-3 and beyond<https://nolanoorg.substack.com/p/int-4-llama-is-not-enough-int-3-and>
* How is LLaMa.cpp possible?<https://finbarrtimbers.substack.com/p/how-is-llamacpp-possible>
4.) Easy-to-use interfaces
* Transformer.js<https://xenova.github.io/transformers.js/> (WebAssembly libraries to run LLM models in the browser)
* Dalai<https://github.com/cocktailpeanut/dalai> ( run LLaMA and Alpaca in own computer as Node.js web service)
* web-stable-diffusion<https://github.com/mlc-ai/web-stable-diffusion> (stable diffusion image generation in browser)
Br,
-- Kimmo Virtanen
On Fri, Mar 17, 2023 at 1:53?PM Kimmo Virtanen <kimmo.virtanen@gmail.com<mailto:kimmo.virtanen@gmail.com>> wrote:
Hi,
The development of open-source large language models is going forward. The GPT-4 was released and it seems that it passed the Bar exam and tried to hire humans to solve catchpas which were too complex to it. However, the development in open source and hacking side has been pretty fast and it seems that there is all the pieces for running LLM models in personal hardware (and in web browser). Biggest missing piece is fine tuning of open source model such as Neox for english language. For multilingual and multimodal (for example images+text) the model is also needed.
So this is kind of link dump for relevant things for creation of open source LLM model and service and also recap where hacker community is now.
1.) Creation of an initial unaligned model.
* Possible models
* 20b Neo(X)<https://github.com/EleutherAI/gpt-neox> by EleutherAI (Apache 2.0)
* Fairseq Dense<https://huggingface.co/KoboldAI/fairseq-dense-13B> by Facebook (MIT-licence)
* LLaMa<https://ai.facebook.com/blog/large-language-model-llama-meta-ai/> by Facebook (custom license, leaked research use only)
* Bloom<https://huggingface.co/bigscience/bloom> by Bigscience (custom license<https://huggingface.co/spaces/bigscience/license>. open, non-commercial)
2.) Fine-tuning or align
* Example: Standford Alpaca is ChatGPT fine-tuned LLaMa
* Alpaca: A Strong, Replicable Instruction-Following Model<https://crfm.stanford.edu/2023/03/13/alpaca.html>
* Train and run Stanford Alpaca on your own machine<https://replicate.com/blog/replicate-alpaca>
* Github: Alpaca-LoRA: Low-Rank LLaMA Instruct-Tuning<https://github.com/tloen/alpaca-lora>
3.) 8,4,3 bit-quantization of model for reduced hardware requirements
* Running LLaMA 7B and 13B on a 64GB M2 MacBook Pro with llama.cpp<https://til.simonwillison.net/llms/llama-7b-m2>
* Github: bloomz.cpp<https://github.com/NouamaneTazi/bloomz.cpp> & llama.cpp<https://github.com/ggerganov/llama.cpp> (C++ only versions)
* Int-4 LLaMa is not enough - Int-3 and beyond<https://nolanoorg.substack.com/p/int-4-llama-is-not-enough-int-3-and>
* How is LLaMa.cpp possible?<https://finbarrtimbers.substack.com/p/how-is-llamacpp-possible>
4.) Easy-to-use interfaces
* Transformer.js<https://xenova.github.io/transformers.js/> (WebAssembly libraries to run LLM models in the browser)
* Dalai<https://github.com/cocktailpeanut/dalai> ( run LLaMA and Alpaca in own computer as Node.js web service)
* web-stable-diffusion<https://github.com/mlc-ai/web-stable-diffusion> (stable diffusion image generation in browser)
Br,
-- Kimmo Virtanen
On Mon, Mar 6, 2023 at 6:50?AM Steven Walling <steven.walling@gmail.com<mailto:steven.walling@gmail.com>> wrote:
On Sun, Mar 5, 2023 at 8:39 PM Luis (lu.is<http://lu.is>) <luis@lu.is<mailto:luis@lu.is>> wrote:
On Feb 22, 2023 at 9:28 AM -0800, Sage Ross <ragesoss+wikipedia@gmail.com<mailto:ragesoss%2Bwikipedia@gmail.com>>, wrote:
Luis,
OpenAI researchers have released some info about data sources that
trained GPT-3 (and hence ChatGPT): https://arxiv.org/abs/2005.14165
See section 2.2, starting on page 8 of the PDF.
The full text of English Wikipedia is one of five sources, the others
being CommonCrawl, a smaller subset of scraped websites based on
upvoted reddit links, and two unrevealed datasets of scanned books.
(I've read speculation that one of these datasets is basically the
Library Genesis archive.) Wikipedia is much smaller than the other
datasets, although they did weight it somewhat more heavily than any
other dataset. With the extra weighting, they say Wikipedia accounts
for 3% of the total training.
Thanks, Sage. Facebookâs recently-released LLaMa also shares some of their training sources, it turns out, with similar weighting for Wikipedia - only 4.5% of training text, but more heavily weighted than most other sources:
https://twitter.com/GuillaumeLample/status/1629151234597740550
Those stats are undercounting, since the top source (CommonCrawl) also itself includes Wikipedia as its third largest source.
https://commoncrawl.github.io/cc-crawl-statistics/plots/domains
<https://twitter.com/GuillaumeLample/status/1629151234597740550>
_______________________________________________
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Public archives at https://lists.wikimedia.org/hyperkitty/list/wikimedia-l@lists.wikimedia.org/message/W3HAFQIMQWBZDTZL6EYZKFG3D2KL7XDL/
To unsubscribe send an email to wikimedia-l-leave@lists.wikimedia.org<mailto:wikimedia-l-leave@lists.wikimedia.org>
_______________________________________________
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Public archives at https://lists.wikimedia.org/hyperkitty/list/wikimedia-l@lists.wikimedia.org/message/6UKCJWOUR2KVTS7QZYKPMKQGONXZ72QR/
To unsubscribe send an email to wikimedia-l-leave@lists.wikimedia.org<mailto:wikimedia-l-leave@lists.wikimedia.org>
I would like to indicate "Copilot" in the Edge browser as being potentially relevant to Wikipedia [1][2].
It is foreseeable that end-users will be able to open sidebars in their Web browsers and subsequently chat with large language models about the contents of specific Web documents, e.g., encyclopedia articles. Using Web browsers, there can be task contexts available, including the documents or articles in users' current tabs, potentially including users' scroll positions, potentially including users' selections or highlightings of content.
I, for one, am thinking about how Web standards, e.g., Web schema, can be of use for amplifying these features and capabilities for end-users.
Best regards,
Adam Sobieski
[1] https://learn.microsoft.com/en-us/deployedge/microsoft-edge-relnote-stable-channel?ranMID=24542#version-1110166141-march-13-2023
[2] https://www.engadget.com/microsoft-edge-ai-copilot-184033427.html
________________________________
From: Kimmo Virtanen <kimmo.virtanen@wikimedia.fi>
Sent: Friday, March 17, 2023 8:17 AM
To: Wikimedia Mailing List <wikimedia-l@lists.wikimedia.org>
Subject: [Wikimedia-l] Re: Bing-ChatGPT
Hi,
The development of open-source large language models is going forward. The GPT-4 was released and it seems that it passed the Bar exam and tried to hire humans to solve catchpas which were too complex. However, the development in the open source and hacking side has been pretty fast and it seems that there are all the pieces for running LLM models in personal hardware (and in web browsers). Biggest missing piece is fine tuning of open source models such as Neox for the English language. For multilingual and multimodal (for example images+text) the model is also needed.
So this is kind of a link dump for relevant things for creation of open source LLM model and service and also recap where the hacker community is now.
1.) Creation of an initial unaligned model.
* Possible models
* 20b Neo(X)<https://github.com/EleutherAI/gpt-neox> by EleutherAI (Apache 2.0)
* Fairseq Dense<https://huggingface.co/KoboldAI/fairseq-dense-13B> by Facebook (MIT-licence)
* LLaMa<https://ai.facebook.com/blog/large-language-model-llama-meta-ai/> by Facebook (custom license, leaked research use only)
* Bloom<https://huggingface.co/bigscience/bloom> by Bigscience (custom license<https://huggingface.co/spaces/bigscience/license>. open, non-commercial)
2.) Fine-tuning or align
* Example: Standford Alpaca is ChatGPT fine-tuned LLaMa
* Alpaca: A Strong, Replicable Instruction-Following Model<https://crfm.stanford.edu/2023/03/13/alpaca.html>
* Train and run Stanford Alpaca on your own machine<https://replicate.com/blog/replicate-alpaca>
* Github: Alpaca-LoRA: Low-Rank LLaMA Instruct-Tuning<https://github.com/tloen/alpaca-lora>
3.) 8,4,3 bit-quantization of model for reduced hardware requirements
* Running LLaMA 7B and 13B on a 64GB M2 MacBook Pro with llama.cpp<https://til.simonwillison.net/llms/llama-7b-m2>
* Github: bloomz.cpp<https://github.com/NouamaneTazi/bloomz.cpp> & llama.cpp<https://github.com/ggerganov/llama.cpp> (C++ only versions)
* Int-4 LLaMa is not enough - Int-3 and beyond<https://nolanoorg.substack.com/p/int-4-llama-is-not-enough-int-3-and>
* How is LLaMa.cpp possible?<https://finbarrtimbers.substack.com/p/how-is-llamacpp-possible>
4.) Easy-to-use interfaces
* Transformer.js<https://xenova.github.io/transformers.js/> (WebAssembly libraries to run LLM models in the browser)
* Dalai<https://github.com/cocktailpeanut/dalai> ( run LLaMA and Alpaca in own computer as Node.js web service)
* web-stable-diffusion<https://github.com/mlc-ai/web-stable-diffusion> (stable diffusion image generation in browser)
Br,
-- Kimmo Virtanen
On Fri, Mar 17, 2023 at 1:53?PM Kimmo Virtanen <kimmo.virtanen@gmail.com<mailto:kimmo.virtanen@gmail.com>> wrote:
Hi,
The development of open-source large language models is going forward. The GPT-4 was released and it seems that it passed the Bar exam and tried to hire humans to solve catchpas which were too complex to it. However, the development in open source and hacking side has been pretty fast and it seems that there is all the pieces for running LLM models in personal hardware (and in web browser). Biggest missing piece is fine tuning of open source model such as Neox for english language. For multilingual and multimodal (for example images+text) the model is also needed.
So this is kind of link dump for relevant things for creation of open source LLM model and service and also recap where hacker community is now.
1.) Creation of an initial unaligned model.
* Possible models
* 20b Neo(X)<https://github.com/EleutherAI/gpt-neox> by EleutherAI (Apache 2.0)
* Fairseq Dense<https://huggingface.co/KoboldAI/fairseq-dense-13B> by Facebook (MIT-licence)
* LLaMa<https://ai.facebook.com/blog/large-language-model-llama-meta-ai/> by Facebook (custom license, leaked research use only)
* Bloom<https://huggingface.co/bigscience/bloom> by Bigscience (custom license<https://huggingface.co/spaces/bigscience/license>. open, non-commercial)
2.) Fine-tuning or align
* Example: Standford Alpaca is ChatGPT fine-tuned LLaMa
* Alpaca: A Strong, Replicable Instruction-Following Model<https://crfm.stanford.edu/2023/03/13/alpaca.html>
* Train and run Stanford Alpaca on your own machine<https://replicate.com/blog/replicate-alpaca>
* Github: Alpaca-LoRA: Low-Rank LLaMA Instruct-Tuning<https://github.com/tloen/alpaca-lora>
3.) 8,4,3 bit-quantization of model for reduced hardware requirements
* Running LLaMA 7B and 13B on a 64GB M2 MacBook Pro with llama.cpp<https://til.simonwillison.net/llms/llama-7b-m2>
* Github: bloomz.cpp<https://github.com/NouamaneTazi/bloomz.cpp> & llama.cpp<https://github.com/ggerganov/llama.cpp> (C++ only versions)
* Int-4 LLaMa is not enough - Int-3 and beyond<https://nolanoorg.substack.com/p/int-4-llama-is-not-enough-int-3-and>
* How is LLaMa.cpp possible?<https://finbarrtimbers.substack.com/p/how-is-llamacpp-possible>
4.) Easy-to-use interfaces
* Transformer.js<https://xenova.github.io/transformers.js/> (WebAssembly libraries to run LLM models in the browser)
* Dalai<https://github.com/cocktailpeanut/dalai> ( run LLaMA and Alpaca in own computer as Node.js web service)
* web-stable-diffusion<https://github.com/mlc-ai/web-stable-diffusion> (stable diffusion image generation in browser)
Br,
-- Kimmo Virtanen
On Mon, Mar 6, 2023 at 6:50?AM Steven Walling <steven.walling@gmail.com<mailto:steven.walling@gmail.com>> wrote:
On Sun, Mar 5, 2023 at 8:39 PM Luis (lu.is<http://lu.is>) <luis@lu.is<mailto:luis@lu.is>> wrote:
On Feb 22, 2023 at 9:28 AM -0800, Sage Ross <ragesoss+wikipedia@gmail.com<mailto:ragesoss%2Bwikipedia@gmail.com>>, wrote:
Luis,
OpenAI researchers have released some info about data sources that
trained GPT-3 (and hence ChatGPT): https://arxiv.org/abs/2005.14165
See section 2.2, starting on page 8 of the PDF.
The full text of English Wikipedia is one of five sources, the others
being CommonCrawl, a smaller subset of scraped websites based on
upvoted reddit links, and two unrevealed datasets of scanned books.
(I've read speculation that one of these datasets is basically the
Library Genesis archive.) Wikipedia is much smaller than the other
datasets, although they did weight it somewhat more heavily than any
other dataset. With the extra weighting, they say Wikipedia accounts
for 3% of the total training.
Thanks, Sage. Facebookâs recently-released LLaMa also shares some of their training sources, it turns out, with similar weighting for Wikipedia - only 4.5% of training text, but more heavily weighted than most other sources:
https://twitter.com/GuillaumeLample/status/1629151234597740550
Those stats are undercounting, since the top source (CommonCrawl) also itself includes Wikipedia as its third largest source.
https://commoncrawl.github.io/cc-crawl-statistics/plots/domains
<https://twitter.com/GuillaumeLample/status/1629151234597740550>
_______________________________________________
Wikimedia-l mailing list -- wikimedia-l@lists.wikimedia.org<mailto:wikimedia-l@lists.wikimedia.org>, guidelines at: https://meta.wikimedia.org/wiki/Mailing_lists/Guidelines and https://meta.wikimedia.org/wiki/Wikimedia-l
Public archives at https://lists.wikimedia.org/hyperkitty/list/wikimedia-l@lists.wikimedia.org/message/W3HAFQIMQWBZDTZL6EYZKFG3D2KL7XDL/
To unsubscribe send an email to wikimedia-l-leave@lists.wikimedia.org<mailto:wikimedia-l-leave@lists.wikimedia.org>
_______________________________________________
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Public archives at https://lists.wikimedia.org/hyperkitty/list/wikimedia-l@lists.wikimedia.org/message/6UKCJWOUR2KVTS7QZYKPMKQGONXZ72QR/
To unsubscribe send an email to wikimedia-l-leave@lists.wikimedia.org<mailto:wikimedia-l-leave@lists.wikimedia.org>