John6666 (John Smith)

reacted to di-zhang-fdu's post with 👀 18 minutes ago

Post

From Pointers to Footnotes: Why Agents will be “Reference-First”

https://medium.com/@di-zhang-fdu/from-pointers-to-footnotes-why-agents-will-be-reference-first-88d0e9730e37

reacted to YerbaPage's post with 🔥 18 minutes ago

Post

12

🔥 SWE-Pruner can save up to 40% of your Claude Code cost without sacrificing performance. Try it out!

📚 SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents (2601.16746)
💻 https://github.com/Ayanami1314/swe-pruner

Drawing inspiration from how human programmers “selectively skim” source code during development and debugging, SWE-Pruner performs task-aware adaptive pruning for long contexts.

reacted to MikeDoes's post with 👀 18 minutes ago

Post

24

What happens when an LLM "forgets" your data? A new paper reports it might not be gone for good.

The "Janus Interface" paper details a new attack that could recover forgotten PII through fine-tuning APIs. This is a solution-oriented paper because it highlights a problem that needs fixing.

Testing such a high-stakes attack requires equally high-stakes data. The Ai4Privacy 300k dataset was a key part of their evaluation, providing a testbed for extracting sensitive Social Security Numbers. Our dataset, with its synthetic structured SSN data, helped the researchers at Indiana University, Stanford & CISPA, and others demonstrate that their attack works on more than just emails. It could affect highly sensitive personal identifiers.

We're excited to see our open-source dataset used in such cutting-edge security research. It's a win for the community when researchers can use our resources to stress-test the safety of modern AI systems. This work is a direct and explicit call for stronger protections on fine-tuning interfaces.

🔗 This is why open data for security research is so important. Check out the full paper: https://arxiv.org/pdf/2310.15469

🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/

reacted to scthornton's post with 👍 19 minutes ago

Post

17

SecureCode: security-aware code models (3B–20B), trained for review + remediation

I’ve been frustrated by how often code assistants recommend patterns that pass tests but fail security review (e.g., string-built SQL, brittle auth logic, unsafe parsing, insecure defaults, etc.). So I built **SecureCode**: a collection of **8 code models (3B → 20B)** trained to behave more like a security reviewer.

What you should expect from SecureCode:

- identify likely vuln patterns and explain *why* they’re risky
- outline plausible abuse paths (defensive framing)
- propose a secure rewrite (drop-in where possible)
- include defense-in-depth guidance + regression tests/checks

Links:

- **Models:** https://huggingface.co/collections/scthornton/securecode
- **Dataset:** scthornton/securecode-v2
- **Paper:** https://arxiv.org/html/2512.18542v1 SecureCode v2.0: A Production-Grade Dataset for Training Security-Aware Code Generation Models (2512.18542)

**How to test it (copy/paste prompt):**

> 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: `...`

**I’m looking for real-world feedback**

- Your “this slipped through review once” snippets (sanitized is fine)
- False positives / false negatives you observe
- Contributions of new CVE-grounded examples

If you drop a snippet, please include language/framework + what the *correct* remediation looks like in your environment. If you have any contributions or suggestions for the dataset, I'd be happy to hear them. I have some new features and enhancements planned for v3 that are already underway, but for now, I'm focused on testing as many use cases as possible. Appreciate you all!

reacted to imnotkitty's post with 🔥 19 minutes ago

Post

21

👀Just published a first-look at Tencent HY-Image-v3.0-I2I!

Tested its multi-image fusion and single-reference consistency. The results on complex prompts are quite impressive.

What’s the most creative image task you’d give it?

👉 Read the full analysis: https://huggingface.co/blog/imnotkitty/tencent-hy-image-v30-i2i

1 reply

·

reacted to Benedictat's post with 🔥 19 minutes ago

Post

414

Tencent HunyuanImage 3.0-Instruct is seriously impressive

skyrocketed to 2nd place globally on the LMArena leaderboard, only trailing Google Nano-banana Pro.

What excites me most is its newly launched image editing and multi-image fusion capabilities

its semantic understanding is rock-solid this Instruct-following capability basically enables one-sentence end-to-end workflows, delivering a dimensionality-reducing boost in efficiency.

Frankly, it nails the pain points of frontline creators: old photo restoration, text modification, even extracting people from multiple images to create group shots. Previously, tweaking the fusion quality took tons of effort, but now the out-of-the-box realism and emotional expression are top-tier zero cheap AI artifacts

👉 Repo: https://hunyuan.tencent.com/chat/HunyuanDefault?from=modelSquare&modelId=Hunyuan-Image-3.0-Instruct

technical report：https://arxiv.org/abs/2509.23951

1 reply

·

reacted to kelsend's post with 🔥 19 minutes ago

Post

374

I'm absolutely stunned by the aesthetics of HunyuanImage-3.0
The visual effects of this model are simply beyond imagination it’s every bit as good as NanoBanana, no compromise at all.
I fine-tuned my micro-scene prompts by adding text overlays and background effects, and its adaptability is truly breathtaking. With just one prompt, you can generate scene posters for any movie or novel.
Every detail, from scene design to text style and atmospheric effects, perfectly aligns with the tone of the original material.
No forced elements, just seamless, film-grade visual effects that exactly match what I envisioned.

👉 Repo: https://hunyuan.tencent.com/chat/HunyuanDefault?from=modelSquare&modelId=Hunyuan-Image-3.0-Instruct

reacted to wangbuer999's post with 🔥 20 minutes ago

Post

333

HunyuanImage 3.0-Instruct just dropped

fresh -sourceImage 3.0model! Spent 20 mins testing it on a Messi + retro scrambler fusion case

Ran on diffusers v0.26.3 + CUDA 12.1 | 8B MoE params (1.3B activated) | zero VRAM issues

strength=0.9 Messi #10 kit/tattoo sharp, moto’s rusted metal texture blurred (classic open-source pain)
strength=0.7 Moto/cobblestone background crisp, Messi’s jersey details faded completely

strength=0.75 + prompt "Blend seamlessly, keep all original details": both subject & background sharp
No ControlNet, no manual masking the model’s chain-of-thought reasoning parses image+prompt first
Already outperforms Qwen-Image-Edit 2511 (GSB eval +25.7% on single-image edits) | 100% open-source

👉 Repo: https://hunyuan.tencent.com/chat/HunyuanDefault?from=modelSquare&modelId=Hunyuan-Image-3.0-Instruct

technical report：https://arxiv.org/abs/2509.23951

Anyone else struggled with strength tweaks for fusion? This fixed it for my Messi+moto case did it work as well for yours?

reacted to Reubencf's post with 🔥 20 minutes ago

Post

35

📢 New release! World_events Dataset now available featuring global events spanning 2023 through 2025
🌍 https://huggingface.co/collections/Reubencf/world-events

🚀 2026 dataset dropping soon

1 reply

·

reacted to pnawrot's post with 🔥 20 minutes ago

Post

94

We’ve just released Qwen3-8B-DMS-8x fine-tuned for 8x KV cache compression. It maintains dense model accuracy on demanding tasks like AIME24, and is perfect for inference-time scaling. The code on HF works out-of-the-box.

With DMS we fine-tune models end-to-end via distillation; this works much better than “token importance” proxies found in usual eviction methods. It’s state-of-art for KV eviction tailored for fast inference: adds negligible amount of parameters and computation to each KV head, and requires as little as 1K fine-tuning steps to reach 8x compression. It speeds-up both prefill and generation phase of Transformer LLMs, and can be combined with Sparse Attention methods such as DSA.

🎓Paper - https://neurips.cc/virtual/2025/loc/san-diego/poster/119605
💾 Checkpoint - nvidia/Qwen3-8B-DMS-8x
📢 Article - https://ed.ac.uk/news/shrinking-ai-memory-boosts-accuracy

reacted to nyuuzyou's post with 🔥 21 minutes ago

Post

91

🌐 NNTP Discussion Archives - 387M Messages from Public Newsgroups - nyuuzyou/nntp-text-387m

Here's something different from the code datasets: 20+ years of public discussion archives from NNTP newsgroups. Clean Parquet format, but this time it's conversations instead of code.

Key Stats:
- 386,629,949 messages from 159,345 newsgroups
- 191 GB compressed Parquet storage
- Spans 2002-2026
- Multilingual: English, German, French, Italian, Dutch, Polish, Russian, and others
- Email addresses redacted for privacy

The data is messy in the way real discussions are messy. Spam wasn't filtered out - you get the advertisements, the arguments, the off-topic threads, all of it. If you want sanitized text, this isn't it. If you want to see how people actually talked online before Discord and Reddit took over, here you go.

Processing kept it simple: convert everything to UTF-8, remove exact duplicates, strip binary attachments, redact emails. Legacy character encodings were a nightmare - had to handle Windows-1252, ISO-8859 variants, KOI8-R, Shift-JIS, GBK, and others just to get readable text. At least it was fun to do, and I think the result turned out pretty well. I hope someone else will also be able to have fun or gain something useful from this project.

reacted to branikita's post with 🔥 21 minutes ago

Post

1064

Robonine (Educational Robotics) completed a structural optimization of our 6-DOF robotic manipulator after a structural optimization study. By increasing structural rigidity through topology optimization and design refinement, we reduced end-effector deflection by over 60% (from ~1.05 mm to ~0.41 mm) and improved motion stability. The final configuration delivers higher precision and reliability for industrial applications.

Article: https://robonine.com/increasing-the-structural-rigidity-of-the-manipulator/

1 reply

·

reacted to kanaria007's post with 🧠 21 minutes ago

Post

219

✅ New Article: *Genius Replay Protocol* (v0.1)

Title:
🧠 Genius Replay Protocol: Capturing and Replaying High-Value Jumps
🔗 https://huggingface.co/blog/kanaria007/genius-replay-protocol

---

Summary:
“Genius” isn’t magic—it’s a **high-value Jump (or short Jump sequence)** that reliably produces outsized GCS gains with strong robustness and reuse potential.
This article defines the **Genius Replay Protocol (GRP)**: a safe way to **capture, validate, store, and replay** those exceptional moves as reusable macro-intelligence—without turning them into copy-pasted folklore.

At the core is a **GeniusTrace** bundle: *ContextSignature* (where it applies) + *JumpSequence* (what was done) + *EvalSummary* (why it worked) + *EthicsTrace* (why it’s allowed).

> Don’t replay outcomes.
> Replay **structure**, under today’s constraints.

---

Why It Matters:
• Turns “it worked once” into a **reproducible asset**
• Prevents unsafe cargo-culting by replaying **structure**, not brittle outputs
• Ensures every replay is re-checked against **current ETH / goal surfaces / OBS requirements**
• Enables a pipeline: **automatic discovery → robustness checks → promotion**, not anecdote collection

---

What’s Inside:
• A concrete definition of “Genius” (GCS outliers + robustness + generalizability)
• The GeniusTrace schema: ContextSignature / JumpRecord / EvalSummary / EthicsTrace
• Three replay modes: **Exact / Structural / Suggestion** (human-facing)
• A **Replay Safety Checker**: context distance, under-observation, ETH compatibility, drift risk
• Operational patterns: policy bootstraps, incident recovery, cross-domain structural motifs
• Governance: re-validation cadence, fairness/privacy constraints, sharing + semantic redaction

---

📖 Structured Intelligence Engineering Series

reacted to AbstractPhil's post with 🔥 21 minutes ago

Post

256

Meet FluxLailah; AbstractPhil/tiny-flux-deep; 220m Flux variant currently pretraining at BF16. She is experimental, does not produce solid images yet - and yet she is producing. There is both an EMA and a raw weights pair producing different images. The EMA is particularly interesting at times.
Lailah uses flan-t5-base, clip-vit-l-14, and BlackForestLabs Flux1s VAE.
SEQ limit 128, images 512x512 for now. Lailah's early form is based on three variants. TinyFlux's weights were carefully planted into a deeper structure and trained yet again - dubbed TinyFlux-Deep. This variant has 15 dual-stream blocks and 25 single-stream blocks, nearly identical weight code as Flux with a similar attention mechanism - but intentionally deviant and compacted with careful consideration to scaling and purpose of mechanisms.
She went through quite a few growing pains with her earlier attention mechanism which required a reimagining today and careful consideration of the consequences, and now I present to you the preliminary look into Lailah.
The preliminary training is still heavily under way, the mechanisms are still being augmented, and her stability is currently being measured. The potential for fidelity, depth, and quality are still in measure - so I will be shifting attention and pivoting utility based on the needs over time.

2 replies

·

reacted to raincandy-u's post with 🔥 22 minutes ago

Post

4273

🤗 Just released Rain-100M, an experimental ~97M-parameter Qwen3-style language model trained from random initialization.

Repo: raincandy-u/Rain-100M

Data: HuggingFaceFW/fineweb-edu, ~3B tokens, English only

Tokenizer: custom 16k BPE, context length 4096

Architecture: 12 Transformer layers, hidden size 768, 12 heads, MLP 2048, SiLU, bf16

Rain-100M is a raw base model (not instruction-tuned or safety-aligned), aimed at small-scale research, debugging training pipelines, and CPU/edge experiments. If you run evaluations, finetunes, or visualizations with it, I would be very interested in your results!

2 replies

·

reacted to consome2's post with ❤️ 1 day ago

Post

4787

We’ve released two conversational speech datasets from oto on Hugging Face 🤗
Both are based on real, casual, full-duplex conversations, but with slightly different focuses.

Dataset 1: Processed / curated subset
otoearth/otoSpeech-full-duplex-processed-141h
* Full-duplex, spontaneous multi-speaker conversations
* Participants filtered for high audio quality
* PII removal and audio enhancement applied
* Designed for training and benchmarking S2S or dialogue models

Dataset 2: Larger raw(er) release
otoearth/otoSpeech-full-duplex-280h
* Same collection pipeline, with broader coverage
* More diversity in speakers, accents, and conversation styles
* Useful for analysis, filtering, or custom preprocessing experiments

We intentionally split the release to support different research workflows:
clean and ready-to-use vs. more exploratory and research-oriented use.

The datasets are currently private, but we’re happy to approve access requests — feel free to request access if you’re interested.

If you’re working on speech-to-speech (S2S) models or are curious about full-duplex conversational data, we’d love to discuss and exchange ideas together.

Feedback and ideas are very welcome!

2 replies

·

reacted to kanaria007's post with 🧠 2 days ago

Post

1818

✅ New Article: *Jumps as Atomic Moves* (v0.1)

Title:
🧠 Jumps: Atomic Moves in Structured Intelligence (and How to Make Them Safe)
🔗 https://huggingface.co/blog/kanaria007/jumps-atomic-moves-in-si

---

Summary:
In SI-Core, a *Jump* is the smallest *effectful* unit of reasoning+action: a move that consumes observations, proposes/chooses an action, and (optionally) commits results + memory updates.

This article makes Jumps operational: *what a Jump must declare*, how it is gated (OBS/ETH/RML), how it produces auditable traces, and how to keep it safe under uncertainty—without collapsing into “just prompt chaining.”

> If you can’t name the Jump, you can’t audit it.
> If you can’t gate it, you can’t ship it.

---

Why It Matters:
• Stops hidden behavior: every effectful move becomes *declared + inspectable*
• Prevents “jumping in the dark” via *OBS gating + sandbox-only paths*
• Makes policy enforceable: ETH overlay can *allow/modify/block/escalate* per Jump type
• Improves rollback reality: map Jump effects to *RML level*, not vibes
• Enables evaluation that matters: jump traces → *SCover / CAS / RIR / SCI* and failure taxonomy

---

What’s Inside:
• A practical Jump contract: inputs/required obs, scope, candidate generation, chooser policy, outputs, memory writes
• Gate sequence: *OBS → eval_pre → (sandbox) → ETH → commit → RML trace → ledger*
• Jump taxonomy: read-only / advisory / effectful / irreversible, and how to treat each
• Safety patterns: conservative defaults, human-in-loop, break-glass, and “publish_result=false” sandboxes
• Testing: golden traces, property tests, chaos drills, and “why this jump?” explainability hooks

---

📖 Structured Intelligence Engineering Series
this is the *how-to-implement / how-to-operate* layer for Jumps as atomic, auditable moves.

reacted to prithivMLmods's post with 🚀 2 days ago

Post

2760

Introducing QIE-2511-Zoom-Master for highlight-guided area zoom-in, enabling lossless zooming within a drawn square area, and QIE-2511-Object-Remover-v2 for precise object or highlight-guided area cleanup. These experimental adapters are trained based on QIE-2511. Find the adapters below.

🕹️QIE-2511-Zoom-Master : prithivMLmods/QIE-2511-Zoom-Master
🕹️QIE-2511-Object-Remover-v2: prithivMLmods/QIE-2511-Object-Remover-v2

🤗Demo: prithivMLmods/Qwen-Image-Edit-Object-Manipulator

📂Collection: https://huggingface.co/collections/prithivMLmods/qwen-image-edit-exps

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

1 reply

·

reacted to DavidAU's post with 🔥 3 days ago

Post

2394

Uncensored, Heretic GGUF quants of GLM 4.7 (30B-A3B) with correct Llamacpp and all updates ; NEO-CODE Imatrix W 16 bit OTs.

Also specialized quants (balanced for this model), and all quants are NEO-CODE Imatrix W 16 bit output tensor.

DavidAU/GLM-4.7-Flash-Uncensored-Heretic-NEO-CODE-Imatrix-MAX-GGUF

"Reg quants, non-heretic" :

Also 16 bit ot, NEO-CODE Imatrix and specialized:

DavidAU/GLM-4.7-Flash-NEO-CODE-Imatrix-MAX-GGUF

reacted to Juanxi's post with 👍 3 days ago

Post

2077

Recent Updates on ScalingOpt | Your Stars are Appreciated

We are pleased to announce several key updates to the ScalingOpt project:

Pyramid Visualization Structure
Following a suggestion from Yufei, we have introduced a pyramid-based visualization framework to systematically outline the layered architecture of Foundation Models—from foundational principles to infrastructure-level details. This addition is designed to assist teams in organizing and presenting related materials more clearly.

Integration of Optimizer Summaries by Yifeng
We extend a warm welcome to Yifeng (author of MARS), who has joined the project. He has contributed a comprehensive summary of over 100 optimizers, now available in ScalingOpt. This resource can be accessed via the “Optimization Summary Sheet” on the homepage or under the Optimizers page, featuring a reader-friendly interface that supports easy viewing, downloading, and citation.

Growing Community of Members
We continue to update and expand the list of active members. Researchers interested in Optimization & Efficient AI are encouraged to join and participate in discussions. Feedback and suggestions are also highly welcomed and will be reviewed and incorporated on an ongoing basis.

Tutorials in Progress
The tutorial development is actively underway. Currently, we have prepared over 300 slides and are refining and expanding the content in collaboration with contributors.

This community is driven purely by passion and a commitment to open knowledge sharing. Your support through starring the repository is greatly appreciated!

1 reply

·

John Smith PRO

AI & ML interests

Recent Activity

Organizations

John Smith PRO

AI & ML interests

Recent Activity

Organizations

John6666's activity