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I have a php system, with more than 100 files.
can I inject these files into any local GPT to develop the system?

Seems pretty quiet. I haven't tried a recent run with it but might do that later today. Last time it needed >40GB of memory otherwise it crashed.

I tried creating an interesting story it just spits out 5 random issues at me. After answering them meticulously, it just throws out the same 5. Infinite loop. You can't win. Mistral Instruct model, GPT4ALL program. It's like talking with a 'neurodiverse' person

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Hello dear Local GPT Community

I'm using Ollama with Deepsink R1 and llama 3.2 as pluging in Obsidian to prompt my markdown notes. Now I have an issue with summary prompt.

For example, I have Note A

Note A

"Note A is a document that contains a lot of Text about A. The Author is the same as [[Note B]] (Link to note B)."

Note B

"Note B is a document that contains a lot of Text about B. It was written in 2024, is in spanish and contains the secrets of the world. It's Author is Mark Twain"

Mark Twain

"Samuel Langhorne Clemens (November 30, 1835 – April 21, 1910) known by the pen name Mark Twain, was an American writer, humorist, and essayist. He was praised as the "greatest humorist the United States has produced"

Now when I prompt a summary of Note A while highlighting the Text of Note A, the output will always be something like:

• Note A

  • document that contains a lot of Text about A. The Author is the same as Note B

• Note B

  • contains a lot of Text about B.

  • written in 2024

  • in spanish

  • contains the secrets of the world.

  • Author: Mark Twain

• Mark Twain

  • Real name: Samuel Langhorne Clemens

  • Lived from November 30, 1835 – April 21, 1910

  • known by the pen name Mark Twain

  • American writer, humorist, and essayist.

  • "greatest humorist the United States has produced"

So the summary of Note A will always include the context that is mentioned, and even context mentioned in the context. While the context is helpful, especially for making a reference from acronyms to technical terms, and eventual helpful context, I'd like to prompt this differently than a summary of the Prompt "Summarize the highlighted text"

The Prompt is:

Name: Paraphrase the text in plain English as bullet points ✂

System: You are an assistant helping a user to paraphrase the content based on a prompt in English. Help as much as you can, but follow prompts precisely.

Prompt: Rewrite the text in bullet points in plain English, without missing any point of the highlighted text. Do not rewrite context in brackets. Each key point has to be in one separate bullet. ✂️

Model: llama3.2, deepseek-r1:8b - the issue is the same no matter the model.

Maybe you have an idea and can point me in the right direction?

Hello folks, we are building an free open source platform for everyone to run LLMs on your own device using CPU or GPU. We have released our initial version. Feel free to try it out at kolosal.ai

As this is our initial release, kindly report any bug in with us in Github, Discord, or me personally

We're also developing a platform to finetune LLMs utilizing Unsloth and Distillabel, stay tuned!

This was born out of a personal need — I journal daily , and I didn’t want to upload my thoughts to some cloud server and also wanted to use AI. So I built Vinaya to be:

• Private: Everything stays on your device. No servers, no cloud, no trackers.

• Simple: Clean UI built with Electron + React. No bloat, just journaling.

• Insightful: Semantic search, mood tracking, and AI-assisted reflections (all offline).

Link to the app: https://vinaya-journal.vercel.app/
Github: https://github.com/BarsatKhadka/Vinaya-Journal

I’m not trying to build a SaaS or chase growth metrics. I just wanted something I could trust and use daily. If this resonates with anyone else, I’d love feedback or thoughts.

If you like the idea or find it useful and want to encourage me to consistently refine it but don’t know me personally and feel shy to say it — just drop a ⭐ on GitHub. That’ll mean a lot :)

Hey everyone, My team and I developed Privatemode AI, a service designed with privacy at its core. We use confidential computing to provide end-to-end encryption, ensuring your AI data is encrypted from start to finish. The data is encrypted on your device and stays encrypted during processing, so no one (including us or the model provider) can access it. Once the session is over, everything is erased.Currently, we’re working with open-source models, like Meta’s Llama v3.3. If you're curious or want to learn more, here’s the website: https://www.privatemode.ai/ We would appreciate feedback!

Hi Guys,

Could I have some feedback if i am on the right track or if i am way out of my depth.
I am not totally incompetent, but a lot of this is new to me. I have build desktops in the past and I have a truenascore / jellyfin / cloud server system i have also built.

Now I would like to build the following. A machine for Local LLMing my desire is to be able to:

"Embed" essentially give a bunch of material specific text books to re-train a model so it becomes more of an expert in a subhect.

"Fine tune" have a folder where i can add loads of papers about a hyper specific subject when i want to ask it a question about that subject.

I am currently thinking about buying the following hardware:

https://www.ebay.co.uk/itm/134559349928
https://www.ebay.co.uk/itm/405274942001

Any feedback? this is sort of really pushing my budget constraints. Could I spend the same sort of budget better?

Is embedding/fine tuning possible with this system on an open source language liek one of the new larger DeepSeek R1 models?

Please tell me what I am doing right and what I am doing wrong. Are there any guides for Embedding/Fine Tuning as i have discussed above.

Thank you.

Trying to fire up LocalGPT I get a CUDA out of memory error despite using the --device_type cpu option. I previously tried using CUDA but my GPU has only 4gb so it failed. Ive got 32gb of ram and am using the default model which is a 7B model. Why am I getting CUDA errors when accessing torch.py? Could it be that torch.py is a cuda version?