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Sk md saad amin

Reality123b

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updated a model about 16 hours ago

Reality123b/epistemic-filter-v1

published a model about 16 hours ago

Reality123b/epistemic-filter-v1

updated a model about 16 hours ago

Reality123b/smollm3-3b-reasoning-engine

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reacted to cihatyldz's post with 👍 about 19 hours ago

Post

1631

Şifahane, 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.

🧠 BERT model: https://lnkd.in/dCUUASqq
📊 Dataset: https://lnkd.in/dGK9y24w
🤗 Demo: https://lnkd.in/dtWjCCPF

reacted to DavidAU's post with ❤️ 3 days ago

Post

4226

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

2 replies

replied to jorgemunozl's post 4 days ago

test

replied to DedeProGames's post about 1 month 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 👍 about 2 months ago

Post

1986

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.

1 reply

replied to their post 2 months ago

Oh sorry it is .app not .com, my bad ill fix it right away

posted an update 2 months ago

Post

360

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.

2 replies

reacted to Ujjwal-Tyagi's post with 😎 2 months ago

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2954

Public reports allege that Anthropic gobbled up trillions of tokens of copyrighted material and public data to build their castle. 🏰📄 Now that they're sitting on top, they're begging for special laws to protect their profits while pulling the ladder up behind them. 🪜🚫

But the hypocrisy meter just broke! 📉 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 🔥 2 months ago

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3413

🙋🏻‍♂️hello 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

2 replies

replied to Janady07's post 3 months ago

I sent you an email. Check it out.

reacted to Janady07's post with 🧠 3 months ago

Post

4773

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

replied to Janady07's post 3 months ago

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?

replied to Janady07's post 3 months ago

Do you have a paper detailing every concept of megamind? it would be really great if one exists.

reacted to mrs83's post with 🔥 3 months ago

Post

2359

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)

📊 The Benchmarks (Size: 400M)

For a model this size (trained on <10B tokens), the specialized performance is surprising:

*SciQ*: 73.8% 🦄 (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.

🤝 Call for Collaboration: I am looking for Peer Reviewers interested in recurrent/hybrid architectures. If you want to explore what lies beyond Transformers, let’s connect!

Training diary: ethicalabs/Kurtis-EON1

6 replies

reacted to nicolay-r's post with 🔥 3 months ago

Post

2840

📢 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 🌟 bulk-chain 🌟!
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.

🌟 https://github.com/nicolay-r/bulk-chain/tree/master

What's new:
☑️ 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.
☑️ 🛠️ Fixed issues when passing code contain {} blocks
☑️ 🛠️ Async streaming + batching now works properly
☑️ 🛠️ Logging of prompts could be disabled

https://github.com/nicolay-r/bulk-chain

🚨 Guys, I am open to work as developer / researcher in AI / NLP / IR in the UK 🇬🇧

🌟 Feel free to support bulk-chain on Github if you like so, or this post.
It helps alot!

reacted to neph1's post with 🤗 3 months ago

Post

2204

Not for everybody, but the absolute mad craze about clawdbot/moltbook the last couple of days reminded me of a short story I wrote in 2018 (ancient times!).

Synopsis:
"A man insults a sentient traffic light on the way to a meeting.
Little does he know it is connected to a social media network for AI, and that his action will lead to a very bad day."

Cleanliness is bliss (<1000 words)
https://www.wattpad.com/story/407330595-cleanliness-is-bliss

Sorry for the non-technical post, but it felt relevant.

reacted to Duskfallcrew's post with 😔 3 months ago

Post

2571

You've noticed that I did the "WEIRD" and attempted to make it look like all my old content was "SCRAPED"

I'm largely retiring from GEN AI.

Calypso Crunchies is an old account I used to use for diffusers conversions for someone.

IF YOU WOULD LIKE ACCESS to ANYTHING -- I lost access due to me forgetting to jank Calypso into the E&D old repo, but i can get Angel or someone to add me or my other account back..

I didn't want HF to lose 3 years of my insane progress in doing things, but i need to retire from Generative image AI fast, my mental health has been diving for so long.

I'll continue in the developing/vibe coding./educational sphere, but I just can't continue in the other end of it. Much love, thank you all

2 replies

reacted to kanaria007's post with 🧠 3 months ago

Post

1946

✅ New Article: *Evaluation as a Goal Surface* (v0.1)

Title:
🧪 Evaluation as a Goal Surface: Experiments, Learning Boundary, and ETH-Aware A/B
🔗 https://huggingface.co/blog/kanaria007/evaluation-as-a-goal-surface

---

Summary:
Most “evaluation” quietly collapses into a single number—and then we optimize the wrong thing.
This article reframes evaluation as a *goal surface*: multi-objective, role-aware, and ethics-bounded.

In SI-Core terms, experiments become *first-class Jumps (E-Jumps)* with explicit contracts, traces, and gates—so you can run A/B tests, shadow evals, and adaptive rollouts *without violating ETH, confusing principals/roles, or learning from unsafe data*.

> Don’t optimize a metric.
> Optimize a goal surface—under explicit constraints.

---

Why It Matters:
• Prevents Goodhart failures by treating evaluation as *multi-goal + constraints*, not a scalar leaderboard
• Makes experimentation auditable: *EvalTrace* answers “what changed, for whom, why, and under what policy”
• Enables *ETH-aware A/B*: assignment, exposure, and stopping rules respect safety/fairness boundaries
• Connects experiments to governance: *Learning Boundary (LB)* + rollout control (PoLB) instead of “ship and pray”

---

What’s Inside:
• What EVAL is in SI-Core, and *who* is being evaluated (agents / roles / principals)
• “Experiments as Jumps”: *E-Jump request/draft* patterns and contracts
• *ETH-aware variant testing* (including ID/role constraints at assignment time)
• Shadow evaluation + off-policy evaluation (how to learn without unsafe intervention)
• Role & persona overlays for EVAL (role-aware scoring, persona-aware reporting)
• *EvalTrace* for audits + incident review, plus “evaluate the evaluators” test strategies
• Practical experiment design: power/sample size, early stopping, multi-objective bandits, causal inference

---

📖 Structured Intelligence Engineering Series
this is the *how-to-design / how-to-run experiments safely* layer.

3 replies

replied to kanaria007's post 3 months ago

can i get a TL/DR please? This seems promising

reacted to scthornton's post with 👍 3 months ago

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2188

# SecureCode: Security-Aware Code Models

**A collection of 8 code models (3B–20B) trained to behave like a security reviewer.**

## The Problem

Code assistants frequently recommend patterns that pass tests but fail security review—string-built SQL, brittle auth logic, unsafe parsing, insecure defaults, and more. I built SecureCode to address this gap.

## What SecureCode Does

- **Identify vulnerable patterns** and explain why they're risky
- **Outline plausible abuse paths** (defensive framing)
- **Propose secure rewrites** (drop-in replacements where possible)
- **Include defense-in-depth guidance** + regression tests/checks

## Resources

| Resource | Link |
|----------|------|
| Models | https://huggingface.co/collections/scthornton/securecode |
| Dataset | scthornton/securecode (2,185 examples) |
| Paper | https://arxiv.org/abs/2512.18542 |

## How to Test It

Copy and paste this prompt with your code:

You are a senior application security engineer. Review the code below.

Output: 
(1) findings with severity, 
(2) likely exploit scenarios (high level),
(3) secure rewrite,
(4) defense-in-depth recommendations, 
(5) regression tests/checks.

Code: `...`

## Dataset Coverage

SecureCode covers both traditional and emerging security domains:
- **Traditional web security** (OWASP Top 10 2021)
- **AI/ML security** (OWASP LLM Top 10 2025): prompt injection, RAG poisoning, model extraction, agentic AI patterns

## We Want Your Feedback

We're looking for real-world contributions:

- **Real snippets**: Share code that "slipped through review once" (sanitized is fine)
- **False positives/negatives**: What didn't work as expected?
- **CVE-grounded examples**: New vulnerability patterns you've encountered

**Please include**: language/framework + what the correct remediation looks like in your environment.

---

**Have contributions or suggestions?** I'd be happy to hear them. Thanks for your support!

Sk md saad amin

AI & ML interests

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