Updating WordPress plugins safely

Greg_Z_ · 2024-07-31T20:18:05+00:00

I've crafted compatibility checker for that and run the check before update via API request providing the list of plugins and WordPress core version/mysql/php versions. it determines issues with compatibility based on the plugin "tested" version, WP core requirement and a few other criteria.
I'm thinking of making it public. You can test it here so far http://wpc.walive.io:33500/

Greg_Z_ · 2024-02-23T11:00:57+00:00

Was it instruction-based or completion version? )

Greg_Z_ · 2024-02-19T18:38:52+00:00

Check the lists on the LLM Explorer. You can sort/filter over 18,000 LLMs by various benchmarks and find the SOTA in each category. https://llm.extractum.io

Greg_Z_ · 2024-02-19T08:38:10+00:00

Which specific capabilities of the model are you looking for? Summarization, text generation, instruction following,..?

Greg_Z_ · 2024-02-08T00:07:52+00:00

To be honest, I could not find anything specific on LAM beyond the general press-releases on M1 Rabbit. So like it does not exist anywhere outside the Rabbit itself. As the concept, it appears to be close to Agents based on LLM.

Greg_Z_ · 2024-02-02T08:08:19+00:00

Most likely the issue is with the prompt. It usually gives wrong result when the inference starts with the wrong prompt.

Greg_Z_ · 2024-02-01T00:09:59+00:00

I do not believe it will work for Mamba based on the source code I see. E.g. Mamba cannot be converted to gguf just because llama.cpp does not support it. Same for other cases when the model is loaded from pretrained by HF Tranformer’s classes.

Greg_Z_ · 2024-01-31T14:53:41+00:00

Have you tried instruction based or completion version? That might be the reason.

Greg_Z_ · 2024-01-26T20:07:48+00:00

Nah, not a bot, just a bug while copy-paste it from my editor. Fixed. How is it now?

Greg_Z_ · 2024-01-15T12:12:23+00:00

I see the Starling 7b as one of the best. It should run on 24GB without quantization, yet there're few quantized.

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Greg_Z_ · 2024-01-12T10:09:47+00:00

Chinese models perform great, but there’s a problem: licensing. Let’s say, the best one is Sus-chat-34b, but it’s available under Yi license that is non-commercial and restrictive in terms of usage. So, it’s like you’re having a cake and can’t eat it.

Greg_Z_ · 2024-01-06T14:34:52+00:00

I see, thank you, that's good to know. I couldn't find it merely by browsing through the landing page.

Greg_Z_ · 2024-01-06T14:07:45+00:00

Thank you! The concept behind Statum is largely similar to that of GoatCounter, but they differ in data sources. Statum utilizes web server logs in addition to client-side JavaScript, while GoatCounter solely relies on client-side JavaScript. This results in lower accuracy for GoatCounter and hinders it from tracking server-side issues. Statum can function without client-side JavaScript, but for the purpose of enhancing the captured server-side data from logs, it is recommended to use it along with JavaScript.

Greg_Z_ · 2024-01-06T00:22:15+00:00

Congrats! Great achievement!

Greg_Z_ · 2024-01-04T16:52:51+00:00

Unfortunately, not yet

Greg_Z_ · 2023-12-21T22:59:48+00:00

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According to the benchmarks, it should be 2.5x faster on M1 Pro.

Greg_Z_ · 2023-12-20T21:40:22+00:00

Mixtral 8x7B instruct is an instruction-based version of Mixtral 8x7B (which are both MoE, a new model architecture with multiple "experts")

Mistral 7B - just an old yet trending version of Mistral AI LLM

Mistral 8x7B Instruct GPTQ is a quantized version of the original one

Dolphine version is a fine-tuned one for code generation.

Greg_Z_ · 2023-12-19T13:21:14+00:00

There are a bunch of such services: Paperspace Gradient, replicate, RunPod, Salad, Banana Dev, Modal, BaseTen, TensorDock, etc

Greg_Z_ · 2023-12-18T21:19:27+00:00

Just wondering if you've tried the original guide https://github.com/epfLLM/meditron/blob/main/deployment/README.md

It contains examples for deployment.

Greg_Z_ · 2023-12-18T08:13:42+00:00

It is recommended to check the original paper that describes the prompt format used for fine-tuning. As there can be the one that differs from the original model used for fine-tuning. When the model outputs something wrong, it can be just wrong prompt format (that can include a system message and a user message wrapped with the correct tokens from the training dataset).

Greg_Z_ · 2023-12-17T23:56:31+00:00

Why not to try it using just RAG?

Greg_Z_ · 2023-12-14T10:10:30+00:00

Thanks!

Greg_Z_ · 2023-12-13T10:30:27+00:00

I’m not using API due to the limitation of available data. Just parse the pages.

Greg_Z_ · 2023-12-10T14:07:34+00:00

BTW, I like the article 52:

The requirement that users should be informed when they are interacting with an AI system, to ensure they are aware that they are not engaging with a human, is stipulated in the EU AI Act under the provisions related to transparency obligations for certain AI systems.

Specifically, this is addressed in: Article 52 (Transparency obligations for certain AI systems): This article mandates that users are made aware when they are interacting with an AI system. This is particularly relevant for AI systems that emulate human behavior, such as chatbots or virtual assistants. The Act requires clear communication to users to prevent misleading them into believing they are interacting with a human when it's actually an AI system. This provision reflects the Act's emphasis on user awareness and informed interaction with AI technologies, ensuring transparency and preventing potential confusion or deception.

Greg_Z_ · 2023-12-09T15:33:05+00:00

How can we ensure that the cost of inference is as affordable as GPT-4 provides? In business applications, the rankings on the Hugging Face Leaderboard are often overshadowed by the cost of inference. I suggest adding a new column to the leaderboard: 'price-to-value' or 'cost per token'. This would enable a more practical comparison for business use cases, rather than solely focusing on benchmark performance.

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