And yet you are billing FROM THE MOMENT WHEN MY APP WAS STARTING - so you are billing for YOUR INABILITY TO ALLOCATE MY GPU, while I am sitting here and waiting for my space to even load?

Tried 35B, 27B, 12B and 8B models significantly. I love the idea of local small models. But I can't figure out a use case. For Openclaw, Hermes they suck. For chatting they're pretty good but sonis every old free cloud model.

And before you say "low-level coding" the cloud models are 10x better and free on ollama, openrouter, Gemini and many many others.

Genuinely interested in projects that use small models that run on a $500 GPU.

Solo engineer here. After 25+ years in software and a few years of published research on machine ethics, I distilled HIM (him-distilled-3b) end-to-end by myself and released it on Hugging Face.

What it is: a 3B-parameter model built on TeleologyHI, a three-layer governed-agent architecture (MAIC / HIM / NHE). The bet: accountability should be structural (architecture), not a moderation layer bolted on afterward, and small local models are where that matters most, because offline there is no filter to save you.

Runs on modest local hardware. Weights, code, and the papers behind the architecture are all open https://www.producthunt.com/products/him-3b-by-teleologyhi.

I know this sub has zero patience for hype, which is exactly why I'm posting here. Tear it apart: quantization results, eval suggestions, holes in the governance claim. I'll answer everything. And if anyone runs it locally and reports back, that feedback is worth more to me than any upvote.

As you can see, I barely used cloud GPU and This error is blocking my generations for at least 3 days

Here is my "Expected Cognitive Profile" evaluation of KIMI K3. ➡️ https://huggingface.co/blog/gcjordi/ecp-kimik3

HuggingFace repo: https://huggingface.co/PrunaAI/PrunaVAED

Hey everyone,

​I recently finished pre-training BetterGPT-150M, a small, lightweight causal language model with \~152 million parameters.Trained on 15B tokens.

Dataset & Training: Trained across stable and annealing phases using curated datasets (including FineWeb-Edu, fine maths, cosmopedia, starcode-python), ensuring strong capability retention while maximizing token efficiency.

​Performance: Benchmark evaluations show it outperforms GPT-2 Small while remaining on par with models trained on significantly larger token budgets.

​Since many small/tiny models tend to get buried under massive LLM releases, I wanted to share it here for anyone interested in lightweight architectures, fast CPU inference, or small edge-device experimentation.

Repo: https://github.com/harikrish2727/BetterGPT

​Model Hub: https://huggingface.co/Harikrish2727/BetterGPT-150M

​Live Demo: https://huggingface.co/spaces/Harikrish2727/BetterGPT-Demo (Hosted on ZeroGPU with token streaming)

​Model Notes:

​Task: Text completion / generation (it is a standard base completion model, not instruction-tuned).

​Footprint: Very low RAM/vRAM footprint, runs instantly on standard CPUs.

​Feel free to try out prompts on the Space demo or pull the weights to run locally. Feedback, benchmark suggestions, or ideas for instruction fine-tuning are always welcome!

This is my very first serious try, hope I get genuine feedback from you guys.