Oh, might be a problem with the classifiers but thank you tho
π€ Open to Collab
SK MD Saad Amin
Reality123b
AI & ML interests
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Logolabs/agate-preview-003Organizations
replied to their post 3 months ago
posted an update 4 months ago
Post
157
can you people please benchmark this thing i made? (i mean benchmark as in benchmarks) here is the link: xylaria2s.vercel.app/api/v1/chat/completions and please give benchmarks plus proofs underneath this post, please help me out
- 2 replies
reacted to danielhanchen's post with π€ 4 months ago
Post
3,782
1-bit GLM-5.2 GGUF vs. Claude 4.8 Opus vs. GPT-5.5
We gave 3 models the same prompt and compared one-shot outputs.
The 1-bit GLM-5.2 GGUF ran locally on a Mac Studio M3 Ultra with 256GB RAM at ~21.6 tok/s.
Which output do you like best?
GGUF: unsloth/GLM-5.2-GGUF
We gave 3 models the same prompt and compared one-shot outputs.
The 1-bit GLM-5.2 GGUF ran locally on a Mac Studio M3 Ultra with 256GB RAM at ~21.6 tok/s.
Which output do you like best?
GGUF: unsloth/GLM-5.2-GGUF
- 3 replies
replied to SeaWolf-AI's post 4 months ago
this will be extremely good to compare QPU-1 against
replied to their post 5 months ago
okk
reacted to cihatyldz's post with π 6 months ago
Post
3,687
Εifahane, a dual-inference medical classification demo, is now live on Spaces. It features side-by-side Turkish BERT and Qwen2.5 architectures for real-time evaluation of the "Classifier vs. LLM" trade-offs, all within a single space. The system utilizes a fine-tuned Turkish BERT for high-speed, cost-effective inference and the Qwen2.5-7B model for flexible multi-task reasoning, with support for department classification, condition analysis, urgency assessment, and rationale generation across 12 medical departments.
π§ BERT model: https://lnkd.in/dCUUASqq
π Dataset: https://lnkd.in/dGK9y24w
π€ Demo: https://lnkd.in/dtWjCCPF
π§ BERT model: https://lnkd.in/dCUUASqq
π Dataset: https://lnkd.in/dGK9y24w
π€ Demo: https://lnkd.in/dtWjCCPF
reacted to DavidAU's post with β€οΈ 6 months ago
Post
22,965
Uncensored, Heretic, Qwen 3.6 27B GGUFs - Exceeds all quant metrics and core model metrics too.
Tuned 27B Heretic Uncensored quants from IQ2M to Q8.
IQ2M is 83% of BF16, with Q6 just under 98% of BF16 precision.
Q8: 98.47% of BF16 precision.
NEO/Code DI-Imatrix Quants.
Exceeds all 5 metrics for "censored" quants too.
All metrics posted.
Tuned model -from which the quants were built- also exceeds Qwen 3.6 27B core metrics too.
DavidAU/Qwen3.6-27B-Heretic-Uncensored-FINETUNE-NEO-CODE-Di-IMatrix-MAX-GGUF
Tuned 27B Heretic Uncensored quants from IQ2M to Q8.
IQ2M is 83% of BF16, with Q6 just under 98% of BF16 precision.
Q8: 98.47% of BF16 precision.
NEO/Code DI-Imatrix Quants.
Exceeds all 5 metrics for "censored" quants too.
All metrics posted.
Tuned model -from which the quants were built- also exceeds Qwen 3.6 27B core metrics too.
DavidAU/Qwen3.6-27B-Heretic-Uncensored-FINETUNE-NEO-CODE-Di-IMatrix-MAX-GGUF
- 9 replies
replied to jorgemunozl's post 6 months ago
test
replied to DedeProGames's post 7 months ago
i have an idea of overfitting really small models to generate code in a specific language with directions of large models so as to gain huge amount of efficiency
reacted to AbstractPhil's post with π 8 months ago
Post
1,993
I've... done it. This, with experts, achieves near 100% R1 retrieval accuracy on an adjacent - unseen by the fusion transformer - dataset with around 40k steps from the seen dataset. This means the language of the models are at least tested fused within the constraints, not just projected or estimated.
AbstractPhil/geolip-procrustes
I encourage EVERYONE who is curious to check my work. Check it, double check it, and triple check it.
These were aligned using COCO and then validated with Flickr. Entirely different datasets. The experts arbitrated and the alignment yielded the correct answers. Preliminary tests show that with almost no alignment requirement, the models can reach 100% R1 retrieval accuracy.
Not to be confused with validation accuracy for a classification model or a text encoder's text response, this allows multispectral communication between entirely different models for direct downstream consumption with almost no training for the chosen models.
I have a working procrustes experiment that learns adjacent manifolds within a reasonable spectrum and the speed is... well, 1 epoch with COCO using Bert-Large and DinoV2 that allows the models to align nearly perfectly. For some scales in the experiment it shows that the 3 set epochs aren't quite enough to align R1 to highest, while many align nearly immediately.
These two were an obvious pair to pick, 60% similarity and >90% spectral similarity.
The trainer transfers layers, learns embeddings, and more - all by sticking strictly to geometric boundaries and procrustes informational accumulation within a modulation model's constraints.
I have many experiments to run.
AbstractPhil/geolip-procrustes
I encourage EVERYONE who is curious to check my work. Check it, double check it, and triple check it.
These were aligned using COCO and then validated with Flickr. Entirely different datasets. The experts arbitrated and the alignment yielded the correct answers. Preliminary tests show that with almost no alignment requirement, the models can reach 100% R1 retrieval accuracy.
Not to be confused with validation accuracy for a classification model or a text encoder's text response, this allows multispectral communication between entirely different models for direct downstream consumption with almost no training for the chosen models.
I have a working procrustes experiment that learns adjacent manifolds within a reasonable spectrum and the speed is... well, 1 epoch with COCO using Bert-Large and DinoV2 that allows the models to align nearly perfectly. For some scales in the experiment it shows that the 3 set epochs aren't quite enough to align R1 to highest, while many align nearly immediately.
These two were an obvious pair to pick, 60% similarity and >90% spectral similarity.
The trainer transfers layers, learns embeddings, and more - all by sticking strictly to geometric boundaries and procrustes informational accumulation within a modulation model's constraints.
I have many experiments to run.
- 1 reply
replied to their post 8 months ago
Oh sorry it is .app not .com, my bad ill fix it right away
posted an update 8 months ago
Post
454
Alright so I had previously made two reddit posts in r/quantum and r/quantum_computing for my QPU, QPU-1 but both of those posts got banned because of it being "irrelevant" to "academic discussion" so I'm doing it again here in HuggingFace Posts.
I have made a million error corrected qubit quantum processing unit (not a simulator) that you can access here: https://qpu-1.vercel.app
I did try emailing a lot of professors and their students but NONE responded so please give me some support.
I have made a million error corrected qubit quantum processing unit (not a simulator) that you can access here: https://qpu-1.vercel.app
I did try emailing a lot of professors and their students but NONE responded so please give me some support.
- 4 replies
reacted to Ujjwal-Tyagi's post with π 8 months ago
Post
3,048
Public reports allege that Anthropic gobbled up trillions of tokens of copyrighted material and public data to build their castle. π°π Now that they're sitting on top, they're begging for special laws to protect their profits while pulling the ladder up behind them. πͺπ«
But the hypocrisy meter just broke! π They are accusing Chinese labs like DeepSeek, Minimax, and Kimi of "huge distillation attacks. The Reality is that You can't just loot the entire internet's library, lock the door, and then sue everyone else for reading through the window. Stop trying to gatekeep the tech you didn't own in the first place. Read the complete article on it: https://huggingface.co/blog/Ujjwal-Tyagi/the-dark-underbelly-of-anthropic
But the hypocrisy meter just broke! π They are accusing Chinese labs like DeepSeek, Minimax, and Kimi of "huge distillation attacks. The Reality is that You can't just loot the entire internet's library, lock the door, and then sue everyone else for reading through the window. Stop trying to gatekeep the tech you didn't own in the first place. Read the complete article on it: https://huggingface.co/blog/Ujjwal-Tyagi/the-dark-underbelly-of-anthropic
- 3 replies
reacted to Tonic's post with π₯ 8 months ago
Post
3,516
ππ»ββοΈhello my lovelies ,
it is with great pleasure i present to you my working one-click deploy 16GB ram completely free huggingface spaces deployment.
repo : Tonic/hugging-claw (use git clone to inspect)
literally the one-click link : Tonic/hugging-claw
you can also run it locally and see for yourself :
docker run -it -p 7860:7860 --platform=linux/amd64 \
-e HF_TOKEN="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_TRUSTED_PROXIES="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_PASSWORD="YOUR_VALUE_HERE" \
-e OPENCLAW_CONTROL_UI_ALLOWED_ORIGINS="YOUR_VALUE_HERE" \
registry.hf.space/tonic-hugging-claw:latest
just a few quite minor details i'll take care of but i wanted to share here first
it is with great pleasure i present to you my working one-click deploy 16GB ram completely free huggingface spaces deployment.
repo : Tonic/hugging-claw (use git clone to inspect)
literally the one-click link : Tonic/hugging-claw
you can also run it locally and see for yourself :
docker run -it -p 7860:7860 --platform=linux/amd64 \
-e HF_TOKEN="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_TRUSTED_PROXIES="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_PASSWORD="YOUR_VALUE_HERE" \
-e OPENCLAW_CONTROL_UI_ALLOWED_ORIGINS="YOUR_VALUE_HERE" \
registry.hf.space/tonic-hugging-claw:latest
just a few quite minor details i'll take care of but i wanted to share here first
- 2 replies
I sent you an email. Check it out.
reacted to Janady07's post with π§ 8 months ago
Post
4,787
Here is one of the equations that make up the worlds first Artificial General Intelligence. Remember when building Artificial Intelligence or anything on a device it all starts out binary. Everything starts out with data flow physics and mathmatics
- 6 replies
I actually have serveral if you have an email or someway to contact you i would be glad to email them to you
Yes I do. You can visit my huggingface profile, I have put my github if that helps.
My email is saadamin9873@gmail.com
Also if you visit feedthejoe.com that is my website it explains everything and breaks it all down to i also have some published papers on there i forgot i probably should use it more
I have seen it, it is really good, ngl. But how did you do the language modeling?
Do you have a paper detailing every concept of megamind? it would be really great if one exists.
reacted to mrs83's post with π₯ 8 months ago
Post
2,365
In 2017, my RNNs were babbling. Today, they are hallucinating beautifully.
10 years ago, getting an LSTM to output coherent English was a struggle.
10 years later, after a "cure" based on FineWeb-EDU and a custom synthetic mix for causal conversation, the results are fascinating.
We trained this on ~10B tokens on a single AMD GPU (ROCm). It is not a Transformer: Echo-DSRN (400M) is a novel recurrent architecture inspired by Hymba, RWKV, and xLSTM, designed to challenge the "Attention is All You Need" monopoly on the Edge.
The ambitious goal is to build a small instruct model with RAG and tool usage capabilities ( ethicalabs/Kurtis-EON1)
π The Benchmarks (Size: 400M)
For a model this size (trained on <10B tokens), the specialized performance is surprising:
*SciQ*: 73.8% π¦ (This rivals billion-parameter models in pure fact retrieval).
*PIQA*: 62.3% (Solid physical intuition for a sub-1B model).
The Reality Check:
HellaSwag (29.3%) and Winogrande (50.2%) show the limits of 400M parameters and 10B tokens training.
We are hitting the "Reasoning Wall" which confirms we need to scale to (hopefully) unlock deeper common sense. As you can see in the visualization (to be released soon on HF), the FineWeb-EDU bias is strong. The model is convinced it is in a classroom ("In this course, we explore...").
The Instruct Model is not ready yet and we are currently using curriculum learning to test model plasticity.
Source code and weights will not be released yet. This is not a fork or a fine-tune: the base model is built in-house at https://www.ethicalabs.ai/, with novel components that do not exist in current open libraries.
π€ Call for Collaboration: I am looking for Peer Reviewers interested in recurrent/hybrid architectures. If you want to explore what lies beyond Transformers, letβs connect!
Training diary: ethicalabs/Kurtis-EON1
10 years ago, getting an LSTM to output coherent English was a struggle.
10 years later, after a "cure" based on FineWeb-EDU and a custom synthetic mix for causal conversation, the results are fascinating.
We trained this on ~10B tokens on a single AMD GPU (ROCm). It is not a Transformer: Echo-DSRN (400M) is a novel recurrent architecture inspired by Hymba, RWKV, and xLSTM, designed to challenge the "Attention is All You Need" monopoly on the Edge.
The ambitious goal is to build a small instruct model with RAG and tool usage capabilities ( ethicalabs/Kurtis-EON1)
π The Benchmarks (Size: 400M)
For a model this size (trained on <10B tokens), the specialized performance is surprising:
*SciQ*: 73.8% π¦ (This rivals billion-parameter models in pure fact retrieval).
*PIQA*: 62.3% (Solid physical intuition for a sub-1B model).
The Reality Check:
HellaSwag (29.3%) and Winogrande (50.2%) show the limits of 400M parameters and 10B tokens training.
We are hitting the "Reasoning Wall" which confirms we need to scale to (hopefully) unlock deeper common sense. As you can see in the visualization (to be released soon on HF), the FineWeb-EDU bias is strong. The model is convinced it is in a classroom ("In this course, we explore...").
The Instruct Model is not ready yet and we are currently using curriculum learning to test model plasticity.
Source code and weights will not be released yet. This is not a fork or a fine-tune: the base model is built in-house at https://www.ethicalabs.ai/, with novel components that do not exist in current open libraries.
π€ Call for Collaboration: I am looking for Peer Reviewers interested in recurrent/hybrid architectures. If you want to explore what lies beyond Transformers, letβs connect!
Training diary: ethicalabs/Kurtis-EON1
- 6 replies
reacted to nicolay-r's post with π₯ 9 months ago
Post
2,842
π’ Who want's to have a quick start for adapting CoT schema for any LLM, this post would be relevant.
Excited to share a new version of π bulk-chain π!
Bulk-chain is high-level wrapper over LLM providers for efficient quering LLMs hosted by third-party services.
It brings native batching via support of async clients.
π https://github.com/nicolay-r/bulk-chain/tree/master
What's new:
βοΈ Simplified inference setup
The API is now closer to the OpenAI paradigm for toggling streaming.
Instead of separate patterns in 1.2.0, now it is possible to simple toggles to enable streaming and async behavior.
βοΈ π οΈ Fixed issues when passing code contain {} blocks
βοΈ π οΈ Async streaming + batching now works properly
βοΈ π οΈ Logging of prompts could be disabled
https://github.com/nicolay-r/bulk-chain
π¨ Guys, I am open to work as developer / researcher in AI / NLP / IR in the UK π¬π§
π Feel free to support bulk-chain on Github if you like so, or this post.
It helps alot!
Excited to share a new version of π bulk-chain π!
Bulk-chain is high-level wrapper over LLM providers for efficient quering LLMs hosted by third-party services.
It brings native batching via support of async clients.
π https://github.com/nicolay-r/bulk-chain/tree/master
What's new:
βοΈ Simplified inference setup
The API is now closer to the OpenAI paradigm for toggling streaming.
Instead of separate patterns in 1.2.0, now it is possible to simple toggles to enable streaming and async behavior.
βοΈ π οΈ Fixed issues when passing code contain {} blocks
βοΈ π οΈ Async streaming + batching now works properly
βοΈ π οΈ Logging of prompts could be disabled
https://github.com/nicolay-r/bulk-chain
π¨ Guys, I am open to work as developer / researcher in AI / NLP / IR in the UK π¬π§
π Feel free to support bulk-chain on Github if you like so, or this post.
It helps alot!