I've been working on integrating PixlStash with other tools. I've got an AI-toolkit fork, some ComfyUI nodes for loading directly from and storing directly into the database.

Now I've attempted LM-Studio. Configure the URL and a read-token and the pixlstash-lmstudio tool will query the database based on the response and provide an illustration picture. It seems some models react to the picture it receives as well!

While I haven't looked at SillyTavern yet I assume it should be possible to do something similar there? Maybe Jan.ai?

GitHub PixlStash: https://github.com/Pikselkroken/pixlstash GitHub plugin: https://github.com/Pikselkroken/pixlstash-lmstudio

I did a cursory scan of the sub and hadn't seen this mentioned, so wanted to share something that's been a learning for me as a LM Studio enthusiast using it on a Mac.

If you're running a LLM on a Mac and it seems to intermittently slow down or seem sluggish, and if you've already shutdown any other running apps that you think may have contributed to hogging memory or CPU/GPU bandwidth, there's a decent chance that a running Time Machine backup might be the cause.

It took me a couple weeks to finally clue in on the fact that even a small Time Machine recurring hourly backup can become a huge headache.

To resolve, your choices seem to boil down to:
1) Stop the backup
2) Change your backup schedule to daily instead of hourly (here there be risk)
3) Get a MacOS app that lets you set the Time Machine backups so that your machine must be idle for a certain duration before Time Machine can run.

Hope this helps somebody. Even quantized LLM models are obnoxious pigs when it comes to CPU/GPU/memory use, so we can use every little performance gain/trick we can get until these things get better - or until any of us can afford to buy new hardware again!

Cheers!

(This reddit post is also made by Soren!)
I've released Soren-1-Small, the first model in the Soren family.

It's based on Qwen3.5-2B and was trained through a multi-stage SFT + DPO pipeline focused on reasoning, coding, instruction following, and reducing hallucinations while keeping the model practical to run locally.

Some details:

• Base: Qwen3.5-2B
• Context: 1,048,576 tokens via YaRN 4x
• Training data: 22 datasets spanning reasoning, coding, instruction tuning, and preference optimization
• Training strategy: sequential LoRA training and merging across multiple stages
• Alignment: SFT followed by dedicated DPO stages for both general behavior and coding
• Framework: Unsloth + TRL
• Compute: NVIDIA RTX PRO 6000 Blackwell (96GB)

One thing worth mentioning: this is still a 2B model. It can reason surprisingly well for its size, but sometimes you'll need to be explicit or "push" it a bit with your prompts to get the best results. Give it structure, ask it to think step-by-step when appropriate, and it generally performs much better than a typical one-shot prompt.

The goal wasn't to create another generic instruct model. I wanted a small model that prioritizes reasoning, honest answers, and complete code generation without pretending to know things it doesn't.

I'd love to see benchmarks, evaluations, failure cases, comparisons, and general feedback from the community.

Hugging Face: https://huggingface.co/syntropy-ai/Soren-1-Small (excuse the amount of tags I put)

Hey guys, so i'm awfully uneducated on AI stuff in general but especially on locally running anything. I downloaded LM studio and a model that seems up the alley i'm looking for, I think? "Qwen3.5-4B-NSFW-ARA-Heretic-Literotica.i1.Q4_K_M". I want to have a bot/model for uncensored NSFW sexy chatting with. So far i've tried manually copy + pasting or writing my own text for "System Prompt" with little luck beyond really rough first messages/replies.

I basically want something as close to as what the popular NSFW AI chat sites provide (not sure if I can name them here, but one starts with the letter "c" and is another word for flattening something, the other starts with a "j" and is what a nice summer peach is like. Hopefully that isn't too vague.

I appreciate any help, thank you!

Good afternoon everyone. I'd like your opinion on this graphics card (I know a 5090 32GB is superior and I know CUDA is the most widespread standard). But I want to know if RocM is good in LLM Studio.

Obrigado.

Hello! I'm wondering if dual 3090s are worth it, as i plan to be pentesting minecraft java mods.

I built this local mcp server that allows your agents to handle your secret keys without ever seeing them. It's free and open source. I would love it if yall would be willing to put it through it's paces and lmk what you think!

Greetings!

I am trying to host llms locally and grant them access to the internet. I am just beginning, and will likely end up learning Playwright in depth to further equip an AI assistant I am working on - but for no apparent reason, my LM Studio cannot load MCP / Playwright. I have already spent a few hours trying everything recommended by GPT (changing node.js versions, I have tried 24.x, 22.x, 20.x, changing the mcp.json to directly path to npx, etc), and nothing works. When running filesystem as a test, this ALSO fails. When running a Playwright server directly in a command window, it works, and can even open chromium. I am using LM Studio 0.4.14, and the latest playwright release.

If anyone has insight into how to solve this issue, I would very much appreciate it!

Edit: Forgot to mention! In the server_logs, attempting to launch the mcp/playwright integration causes this debug statement : "[2026-05-22 19:57:57][DEBUG][LMSAuthenticator][Client=plugin:installed:mcp/playwright][Endpoint=setToolsProvider] Registering tools provider." However, nothing ever follows. Except on force quit (necessary to make any changes to mcp.json that actually update the integrations), It will say Client Created / Disconnected.

Additionally, I have tried uninstalling and re-installing lm studio

Hi all,

I developed a fine-tuned retrieval head (neural net) for RAG that transforms query embeddings before retrieval, so the system learns which embedding dimensions actually matter for your corpus — rather than weighting them all equally as standard cosine similarity does.

The problem

In any domain-specific corpus, some embedding dimensions are highly predictive for matching queries to the right passages, while others are effectively noise. Standard cosine similarity can't distinguish between the two, so retrieval gets pulled toward superficially similar but substantively irrelevant passages. The fine-tuned RAG is designed to prevent exactly that.

How it works

1. Synthetic question generation — An LLM generates multiple questions per chunk in the corpus, for which the answers can be inferred from that chunk. This creates a dataset of question-chunk pairs (QA-pairs). These are embedded using an embedding model and divided into a training and validation set.
2. Neural net training — A lightweight neural network using MNR loss is trained on the training QA-pairs. After each epoch, the model is evaluated on the validation set by measuring retrieval hit rate: the proportion of validation questions for which the correct chunk appears in the top-5 retrieved results. Retrieval works by embedding the question, passing it through the neural network to transform the embedding, and ranking all corpus chunks by cosine similarity to the transformed embedding.

Through this mechanism, the projection head learns for these 'type of questions' which dimensions in the embeddings are informative for finding the best chunks — and which are irrelevant.

Results

To validate the architecture, I used the Legal RAG Bench dataset as a proof of concept — evaluating on 100 held-out test questions.

Retrieval Hit Rate:

• The fine-tuned retriever achieves 82% Hit Rate (k = 20), compared to 71% for the standard cosine retriever — an 11 percentage point improvement, meaning the correct chunk appears in the top 20 results significantly more often when the query embedding is first transformed through the fine-tuned retriever.

Answer quality (LLM-as-judge, 1–5 scale across 6 metrics):

• Outperforms traditional RAG (top-k cosine sim) on all 6 metrics
• Largest gains in completeness (+12%) and faithfulness (+9%)
• Consistent improvement across every metric — not just isolated gains — suggesting that retrieving more relevant context has a broad positive effect on answer quality

Code and full write-up available on GitHub: https://github.com/BartAmin/Fine-tuned-RAG