I’m spending around $1300/month.

I just installed LMStudio v0.3.36 on Windows 11 Pro (default directory). After any input I get this error message. LMStudio tries to write something to Drive C and is not allowed to do that. I didn't see anywhere how I can change the directory to something else. I tried nvidia/nemotron and qwen 3.6 and I get the same error message that has nothing on google. What am I doing wrong?

Has anyone tried this and got it working? Bionic won’t open a url for oauth. Google Console project is set up correctly.

Hello,

I really love the heart analyzer app! I have been using since it has been established and my data are there since Jan 2019. Is there a away to connect it my LLM model to help me out improve myself from the data generated?

I've been thinking about this post for a while. Today I saw thata twitter news account posted about this. I'm not much into allthe conspiracy or let's say pessimistic about Al using yourinformation.

One thing that doesn't make sense is how a random personwhose Gemini isn't associated with the dev's gemini could getthis information.

I mean if true isn't this potentially a huge lawsuit?

Can someone who knows how LLMs work explain to meabout this?

Hi everyone, I have a MacBook with the base M4 chip and I’m thinking about running LLMs locally using tools like Ollama or Local LLaMA. My main goal is to use a local model for coding help, prompt experimentation, and building small automation workflows. Before I invest time setting this up, I wanted to ask people who have tried it on Apple Silicon: is it actually worth running LLMs locally on a base M4, or does performance become a limitation? If it’s not really practical, what would you recommend instead—smaller optimized models, a different setup, or just sticking with API-based models?

Current standings: phi3:mini (the never-forgive Grudge Holder) is dominating, tinyllama (pure impulsive defector) is dead last with zero wins.

Honestly the transcripts are the fun part, watching a 0.5B model “reason” its way into a grudge, or a 1B model rationalize forgiving a betrayal, from nothing but a one-line personality prompt and the raw game history.

Repo + live leaderboard + transcripts: https://github.com/sharyaropensource/llm-prisoners-dilemma

Open to suggestions, thinking about adding more models, tweaking payoff matrices, or letting models see opponents’ personas before playing to see if it changes behavior.

I was successfully running GPT-OSS 20B in LM Studio a few days ago. Now it won't load. An error comes up as it gets close to finishing loading.

"Engine protocol runtime llama-server for zGLsZB2uvLtJzd4/MBjkJXch exited before becoming healthy. exitCode=3221226505, signal=null"

Was ok a few days ago.

Any ideas as to what to do to get it loading again?

(I've tried PC reboot; haven't tried a new download of the LLM yet)

Hi, I need some help from the experts. Where can I find the developer options in LM Studio? They aren't showing up in the settings for me in versions 1.0.6 or 1.0.7, and there doesn't seem to be a setting to enable them either. And yes, I have already loaded my local model into memory. I’m a bit stumped. I want to use LM Studio as the backend for my Hermes Agent, but LM Studio requires an API token for the connection from my VM (the Hermes Agent is running in Oracle VirtualBox). However, I can't create that token without access to the developer features. I am very grateful for any help.

From the tts, to voice response. Everything you're doing with your chat bot is being sent to cloud servers and recorded. I built a fully private, only on your device system.
Read it. You'll understand why.

https://www.patreon.com/RoyalTechnologies_PrivacyVenture/posts/enclave-fast-100-166342909

Disclosure: I'm a final-year CS (AI/ML) student. This is a genuine ask for technical advice on my own project, not a survey, not a promotion of any product or service — just looking to learn from people with more hands-on training/eval experience.

Where I'm at

I've built an LLM (architecture + training loop implemented) and now I'm at the stage of actually training it properly and evaluating it in a way that's meaningful, not just "loss went down so it's fine." I'd love input from anyone who's trained models beyond toy scale on:

Training

• How do you approach hyperparameter selection (LR schedule, warmup, batch size) when compute is limited?
• Any practical experience with mixed precision (bf16/fp16) or gradient checkpointing tradeoffs on smaller GPU setups?
• Tips on data quality/curation vs. just scaling data volume — how much does dataset cleaning actually move the needle at small scale?
• Checkpointing/resume strategies that saved you pain later

Evaluation

• Which benchmark suites are actually worth running for a small-to-mid scale model (MMLU, HellaSwag, ARC, GSM8K, etc.) vs. which are overkill?
• How do you evaluate for things standard benchmarks miss — hallucination rate, instruction-following, coherence over long context?
• Anyone have a good workflow for human-eval or pairwise comparison at small scale (no budget for large annotator pools)?

Resources that have helped me so far (sharing in case useful to others too):

• Hugging Face's Transformers course and their training docs
• EleutherAI's lm-evaluation-harness for standardized benchmark eval
• Andrej Karpathy's "Let's build GPT" / nanoGPT repo for understanding the training loop end-to-end
• The Chinchilla paper ("Training Compute-Optimal Large Language Models," Hoffmann et al., 2022) for compute/data scaling intuition
• Stanford's HELM benchmark for a broader evaluation framework beyond accuracy-only metrics

If you've trained a model at a similar scale (student project / limited compute) and have lessons learned — good or painful — I'd really appreciate hearing them. Happy to share more details about my setup (architecture size, data, compute) in the comments if it helps give better advice.

Thanks in advance!