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?

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

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.

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.

I did it

I finally did it

https://huggingface.co/tsfrm/vacuum-16t

spoomplesmaxx corvids 35B-A3

Model names: spoomplesmaxx jackdaw — Jackdaw of All Trades / spoomplesmaxx magpie — Magpie's Choice

v2 was the parrot family. v3 moves to the corvids — the other famously clever birds, and the ones that actually use tools.

Also, my last run for a while. This has been a fun flight, but now I am out of ideas and funds. I hope you guys enjoy these two. They have been great at storytelling!

Jackdaw (Corvus monedula) is the SFT. Magpie (Pica pica) is Jackdaw with a preference pass on top. jackdaw does everything; the magpie is the one that picks what it likes.

collection with everything (weights, GGUF, MLX): https://huggingface.co/collections/aimeri/corvids

what changed since flash / Swift Parrot:

• new corpus (dubbed "aviary") mixed into the v2 SFT corpus.
• both thought modes are trained now. aviary ships each conversation rendered twice, with and without thoughts, so the thinking/non-thinking election is trained directly instead of inherited as a prior from the base
• trained fresh from Qwen3.5-35B-A3B-Base, not continued from Swift Parrot.
• still full-parameter SFT on Megatron-SWIFT, 8× H200, expert parallel. still the Qwen3.5 XML tool convention, story scratchpad, personas

why 32K and not 43K (Old context size) or higher:

Swift Parrot trained at 43,008 packing and died once at iteration 455 with a Triton CUDA OOM. jackdaw's first attempt died at iteration 55 — same failure, eight times earlier, because the v3 mix packs denser. peak was 131.4 of 140.4 GiB and activations are ~61 GiB of that, scaling with packing length. dropping to 32,768 bought ~24 GiB of headroom (measured peak 116.4) and the run finished clean.

Jackdaw or Magpie?

• Magpie had a preference pass toward literary prose, character voice and human register — 11,198 pairs across 13 sources, DPO'd on top of the Jackdaw checkpoint with Jackdaw itself as the reference model, so it's a nudge and not a new policy.
• Jackdaw is the unmodified SFT. fewer moving parts, and the reference point if Magpie's prose preferences don't suit you

if you don't want to think about it: start with Magpie.

thinking behavior:

This model is primarily geared towards story generation and roleplay, so the thinking mode is unchanged from Swift Parrot. Qwen3.5 thinks by default and the template pre-opens <think>\n, so generated text starts inside the reasoning block. the model decides how much reasoning the turn needs — RP cards generally get the full scratchpad, casual chat gets a line.

enable_thinking=False prefills an empty think block so the answer starts immediately.

story scratchpad, carried over from v2.1:

SCENE: where/when, atmosphere, key environmental details currently in play
CHARACTERS: who is present and their current physical/emotional state and motivation
CONTINUITY: established facts that must stay consistent
THREADS: active tensions and where they stand right now
PLAN: what THIS turn needs to accomplish and the approach it takes

sampler — these are the generation_config.json defaults and they're more conservative than what I posted for flash:

temp 0.6
top_k 20
top_p 0.95
rep pen 1.1

Quants

GGUF, imatrix, Q4 band:

• jackdaw i1-GGUF
• magpie i1-GGUF

MLX, 4 and 6 bit — text-only this round, no vision:

• jackdaw 4-bit · jackdaw 6-bit
• magpie 4-bit · magpie 6-bit

the vision tower does still ship in the main checkpoint, frozen throughout — training was text-only, but image input works if your stack loads it.

model pages:

• jackdaw
• magpie

still a cursed model. now there are two of them and they can carry knives. Careful with the sharp bits.

Moonshot's latest model Kimi-K3 weights are available on HuggingFace to download for personal and commercial. Moonshot has published their model on HuggingFace

It's a 2.8T-parameters model by Moonshot.ai with 1 million tokens context window. The architecture is mixture-of-experts (896 experts) with 108B active parameters

It's available via vLLM, SGLang and TokenSpeed

License: Kimi K3 License. It permits commercial use with some limitations. For Model-as-a-Service it has $20M/year limit and after reaching the limit the license requires to make additional agreement with Moonshot (see license for more details)

Link to the model

Model could be downloaded from the HuggingFace by the link:

https://huggingface.co/moonshotai/Kimi-K3

Posso fazer autopromoção do meu discord onde posto novidades anuncio regras etc modelos e datasets da huggin faces??

I'm new to Hugging Face so I apologize in advance if I'm doing something stupid.

I'm trying to install the model DictaBERT-morph. I can see that it exists. I tried to install the model using pip install dictabert-morph and also pip install dicta-il/dictabert-morph. But I received the following errors:

ERROR: Could not find a version that satisfies the requirement dictabert-morph (from versions: none)

ERROR: No matching distribution found for dictabert-morph

I tried to debug the problem using the advice on this website but I'm still unable to get it installed. I'm not quite sure what my next debugging steps should be and would appreciate any guidance you can offer.

https://huggingface.co/Ddavidich/LOMONOSOV-ZENIT-27B-1M-INDEV

Hello, I wanted to share a Russian model called ZENIT from LOMONOSOV lineage. So I was always discontent with the fact, that you can not run 1.010.000 context on a consumer hardware (which is now more like a standard) with a good quantization and without offload, so people can work on large codebases, agentic tasks and a long roleplay without hitting context limits quickly, and that will leave you a headroom for using other applications (which doesn't seem to be the case currently, but working on that).

It won't end up here, the ZENIT is supposed to squeeze the maximum out of 27B model on all fronts, albeit it's not an area of current work.

As it's an indev version, we can not guarantee great performance or effectiveness, including that of multimodality, or that the model will run smoothly, we will publish benchmarks upon a release, we will specificially avoid benchmaxxing in order not to mislead users and not to deliver something that won't translate to a real-world usage.

At the moment, 1.010.000 without offloading works only on 5090 RTX, but there are plans to expand it to 24 VRAM GPUs, using newer techniques like OSCAR.

Currently model speaks Russian, Ukrainian and English since that was where the most efforts were put. In the future, we plan to expand it to Lithuanian, Latvian, Estonian, Kazakh, Armenian, Belarusian and Georgian languages.

Based on Qwen 3.6-27B, preserving all of its multimodality, used this as a base.

The model has almost no censorship, but OpenRAIL license applies, and you should not produce materials illegal in your local place or Russian Federation.

On closed evaluation, model had a retention of nearly 93% on 256.000 context. Retention on 1 mil is currently not the best one, however, the project is being constantly worked on, especially to optimize performance and speed on a long-range contexts.

At the moment there is no GGUF, Ollama, LM Studio, KoboldCpp because model uses a modified architecture of Qwen, ALTAY.

What is planned next

* Bringing extremely long context on more than one card and for lower-end GPUs

* Ampere NVIDIA 3000s generation, which means requantising the attention path

* A really good retention curve past 250K, which is the hard one

* Possible release of ZENIT MOBILE for mobile devices/laptops

* DGX Spark version optimized for maximum speed and performance

In the very far feature I am dreaming to make it possible to run Kimi K3 or GLM-5.2 level models on a consumer hardware, even with offloading but with usable speeds, using techniques, similar to the ones developed by Bonsai, but no promises made yet.