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Wayne Workman PRO

wayneworkman2012

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https://wayne.theworkmans.us/

AI & ML interests

LLMs

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repliedto their post about 4 hours ago

To all the inference providers behind the HuggingFace router - if you want me to use your services you will have to serve native-precision models. I'm not paying for FP8 when I can run in bf16 locally.

posted an update 2 days ago

repliedto their post 13 days ago

HuggingFace billing and credits questions Opening this post to get help, if anyone knows the answers. I have a balance of HF credits in my account that seem to have not changed since the turning of months (August to September). Last month, my period usage was showing $1,300ish and upon the 1st of the month, that dropped to zero. But my credits amount didn't change. Over the course of yesterday and today (1st and 2nd of September) I've used about 800 dollars in inference via the HF router and yesterday I could see that amount in my inference dashboard but not billing dashboard. And now today, that amount has disappeared from my inference dashboard and my period usage in the billing dashboard is showing a few cents. I'm super confused about how this works. I don't know why my inference usage costs keep disappearing. I don't know why my account credits are not decreasing. If any light can be shed on this, I'd really be appreciative.

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replied to their post about 4 hours ago

if I knew the served precision, It would save me the time of comparing the probabilities of a remote qwen 27B against my local native-precision 27B, and likewise for gemma4 31b. I am running qwen locally in native precision for the accuracy. The accuracy is what I'm after. I'd like to expand - using both my local GPU /w native precision 27B AND a remote qwen of the same precision. I don't want reduced precision for this work I'm doing.

It seems to me like they want our eyes to turn into hearts when we see "Qwen 27B" being served, but they aren't telling us they quantized it. I could run quantized qwen at twice the speed of native precision qwen, I don't want quantized.

posted an update 2 days ago

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5 replies

replied to their post 13 days ago

Can someone please explain to me what is wrong with my HF bill? Is inference free now? And if so, who can I thank?

replied to medmekk's post 24 days ago

I see you mentioned in the README using gpt and claude. Have you determined this is ok under anthropic's terms? I suppose it is because Anthropic offers no model training framework and therefore it's not competing ?? Not sure on this.

reacted to medmekk's post with ❤️ 24 days ago

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🚀 Introducing Halo 1.0

Today, we are open-sourcing Halo, the training framework we use to train every model at White Circle.

It comes with:
🧠 Full post-training stack: SFT, DPO/KTO/SMPO, reward modeling, GRPO, distillation
🤖 Async multi-turn RL with vLLM/SGLang rollouts and sandboxed tool use
⚡ ~2.8× TRL throughput on 8× B300 (EP+FSDPv2, FA4, fp8/fp4)
🤗 Dense HF models + 15 MoE families (Qwen, GLM, Mistral, DeepSeek-V4…)
🛠️ One halo command, prebuilt Docker images, and docs for humans and agents

💻 https://github.com/whitecircle/halo

Try it and tell us what you're training

1 reply

reacted to prithivMLmods's post with 👍 24 days ago

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VisionGuardrail EVO-2, a multimodal image-classification content-safety model based on Qwen/Qwen3.8-27B, is now available on the Hub!

Stricter image classification than before, with a dense 27-billion-parameter multimodal model, more precise reasoning, and improved captions for classifying visual media.

➠ Models: prithivMLmods/VisionGuardrail-Evo2-27B, prithivMLmods/VisionGuardrail-Evo2-27B-GGUF

➠ Collection: https://huggingface.co/collections/prithivMLmods/visionguardrail-evo2

➠ Previous Models: https://huggingface.co/collections/prithivMLmods/visionguardrail-collection

⤷ To learn more, visit the app page or the respective model pages.

replied to OppaAI's post 24 days ago

I read the chat - this is really entertaining. I need to check this out.

reacted to Datdanboi25's post with ❤️ 26 days ago

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THE SLM FRONTIER ADVANCES!

bench-labs/cagliostro-v3 just hit an Intelligence Index of 26.13 on the AxiomicLabs/Open_SLM_Leaderboard a 146M-param model trained completely from scratch on a single consumer GPU. That's 2nd place overall, and as far as I can tell, the most capable SLM trained on consumer hardware to date. Beating SmolLM-135m on 1/8th of the data is just silly levels of efficiency.

Big congrats to the @BenchLabs team and specifically @TobiasLogic !

6 replies

replied to Datdanboi25's post 26 days ago

Submit it to the SLM arena here: https://huggingface.co/spaces/CodeSoft/SLM-Arena

reacted to KlondikeDev's post with 👍 27 days ago

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Important Boris-2 news:

Boris-2 is 30B out of 200B tokens in, and it is severely behind its competitors in training.

We have determined the bug to be a configuration error. Boris-2 has been in training for ~1 week, and was projected to finish on November 3rd, 2026.

We are unfortunately going to restart training, with proper configuration.

The new projected finish date is ~15-18th of November.

We apologize for the delay.

17 replies

posted an update about 1 month ago

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Peacebell is released!

wayneworkman2012/peacebell-v1-291M
wayneworkman2012/peacebell-v1-291M-demo
wayneworkman2012/peacebell-v1-291M-demo-cpu
wayneworkman2012/peacebell-v1-148M

reacted to juiceb0xc0de's post with 🔥 about 1 month ago

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Dude, Where's My Update? I'll tell you where! ~97.6% of my BF16 parameter coordinates didn't move at all, and the ones that did overshot by ~1.33x.

It's nice to do research that doesn't end in disproving yourself once again and moving on to the next subject once in awhile.

Back to the topic, if you've ever wondered why most of your weights are basically ghosting you nearly every step when you store your weights at bf16, Dude, I Measured It.

https://huggingface.co/blog/juiceb0xc0de/intended-and-realized-updates-in-bf16-fine-tuning#dude-wheres-my-update

reacted to danielhanchen's post with 🚀 about 1 month ago

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6,874

Qwen3.8-27B Unsloth GGUF is now the #1 most-liked GGUF of all time! 🤗🦥

The model hit 10M downloads and 3.7K likes in just 24 days on Hugging Face - all thanks to you.

GGUF: unsloth/Qwen3.8-27B-GGUF
Guide: https://unsloth.ai/docs/models/qwen3.8

5 replies

reacted to GGUFGuy's post with 🤯 about 1 month ago

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wait why can i post

24 replies

reacted to anakin87's post with ❤️🔥 about 1 month ago

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3,504

I made a 1.1M ModernBERT encoder play Doom in real time on a CPU

Some time ago, VAGO Solutions released SauerkrautLM-Doom-MultiVec-1.3M, a tiny model trained to play Doom Defend the Center scenario from 31k human gameplay examples.

My first thought: cool! I love both Doom and Small Language Models.

Then another idea: I bet I can do better :-)

What I did?
- evaluated the original model and found it's better than reported
- changed a bit the architecture
- generated SFT data with a scripted oracle
- SFT + PPO refinement on consumer hardware

Got a smaller, faster and killer model
Can even fit a floppy with int8 quantization 💾

Watch it play/read the article: anakin87/tiny-doom-defender

reacted to KlondikeDev's post with ❤️ about 1 month ago

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The SLM Consortium has begun work on a safety dataset for Small Language Models, with the creation of the dataset being headed by @wayneworkman2012

The dataset will focus on refusals and redirects surrounding dangerous or extreme sexual content, designed to be shaped sized appropriately for SLMs, without significantly lowering benchmark performance.

More info will be out soon!

slmconsortium

3 replies

replied to kostakoff's post about 1 month ago

NVIDIA has openly supported open weights models. I am interested to see how the community evolves.

posted an update about 1 month ago

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HuggingFace billing and credits questions

Opening this post to get help, if anyone knows the answers. I have a balance of HF credits in my account that seem to have not changed since the turning of months (August to September). Last month, my period usage was showing $1,300ish and upon the 1st of the month, that dropped to zero. But my credits amount didn't change. Over the course of yesterday and today (1st and 2nd of September) I've used about 800 dollars in inference via the HF router and yesterday I could see that amount in my inference dashboard but not billing dashboard. And now today, that amount has disappeared from my inference dashboard and my period usage in the billing dashboard is showing a few cents.

I'm super confused about how this works. I don't know why my inference usage costs keep disappearing. I don't know why my account credits are not decreasing. If any light can be shed on this, I'd really be appreciative.

1 reply

replied to CodeSoft's post about 1 month ago

I think it's due to people asking questions that Bananamind is also good at. As I use the SLM Arena I'm finding there's some things that SmolLM can do that the others cannot do - and if I ask that question in repeated battles, SmolLM goes up in points consistently while others go down.

Wayne Workman PRO

AI & ML interests

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