Hugging Face's logo
Activity Feed Request to join this org

AI & ML interests

Local LLMs

Recent Activity
smcleod  new activity 12 days ago
LocalLLaMA/GuifreAI:What's the organisation & project relating to (I'm a r/localllama mod)
guifre103  updated a model 12 days ago
LocalLLaMA/GuifreAI
guifre103  published a model 12 days ago
LocalLLaMA/GuifreAI
View all activity
OzTianlu 
posted an update about 13 hours ago
view post
Post
78
We deleted the Embedding Layer -- INTRO Our Collins-Embedding-3M
NoesisLab/Collins-Embedding-3M
Most "small" models are just giant vocab tables in a trench coat. Collins-3M changes that. By using 2-Universal Hashing and Chernoff-bound noise suppression, we’ve collapsed the embedding space into a fixed O(1) hash-map.
* STSB: 0.7114 (Beating many 100M+ models)
* Size: 3M (Edge-ready, IoT-ready)
* Tech: Randomized Sign-Hashing + RoPE positional injection.
Built by NoesisLab
MaziyarPanahi 
posted an update 2 days ago
view post
Post
3626
DNA, mRNA, proteins, AI. I spent the last year going deep into computational biology as an ML engineer. This is Part I of what I found. 🧬

In 2024, AlphaFold won the Nobel Prize in Chemistry.

By 2026, the open-source community had built alternatives that outperform it.

That's the story I find most interesting about protein AI right now. Not just the science (which is incredible), but the speed at which open-source caught up. Multiple teams, independently, reproduced and then exceeded AlphaFold 3's accuracy with permissive licenses. The field went from prediction to generation: we're not just modeling known proteins anymore, we're designing new ones.

I spent months mapping this landscape for ML engineers. What the architectures actually are (spoiler: transformers and diffusion models), which tools to use for what, and which ones you can actually ship commercially.

New post on the Hugging Face blog: https://huggingface.co/blog/MaziyarPanahi/protein-ai-landscape

Hope you all enjoy! 🤗
  • 1 reply
·
prithivMLmods 
posted an update 3 days ago
view post
Post
3709
QIE-Object-Remover-Bbox Demo removes objects and artifacts from selected regions using bounding box grounding. Built on Qwen-Image-Edit-2509 with Rapid Diffusers acceleration, it delivers fast 4-step inference via the QIE-2509 adapter. 🤗🔥

🔗Demo Space: prithivMLmods/QIE-Object-Remover-Bbox
🔗Qwen-Image-Edit-Rapid-AIO: prithivMLmods/Qwen-Image-Edit-Rapid-AIO-V4
🔗Adapter-(LoRA): prithivMLmods/QIE-2509-Object-Remover-Bbox

🔗Collection: https://huggingface.co/collections/prithivMLmods/qwen-image-edit-layout-bbox

To learn more, visit the app page or the respective model pages.
  • 1 reply
·
OzTianlu 
posted an update 5 days ago
view post
Post
4739
🔥 UPGRADE in Kai: 30B Scaling! 🔥
NoesisLab/Kai-30B-Instruct
NoesisLab/Kai-30B-Instruct
We are incredibly excited to announce that the Kai-30B-Instruct model and its official Space are now LIVE! 🚀
If you've been following the journey from Kai-0.35B to Kai-3B, you know we're rethinking how models reason. Tired of verbose, slow Chain-of-Thought (CoT) outputs that flood your screen with self-talk? So are we.
Kai-30B-Instruct scales up our Adaptive Dual-Search Distillation (ADS) framework. By bridging classical A* heuristic search with continuous gradient descent , we use an information-theoretic log-barrier to physically prune high-entropy reasoning paths during training.
The result? Pure implicit reasoning. The model executes structured logic, arithmetic carries, and branch selections as a reflex in a single forward pass—no external scaffolding required.
At 3B, we observed a phase transition where the model achieved "logical crystallization". Now, at 30B, we are giving the ADS regularizer the massive representational capacity it needs to tackle higher-order symbolic abstractions and complex reasoning tasks.
🧪 Test Kai yourself in our new Space:
NoesisLab/Kai-30B-Instruct
📦 Model Weights:
NoesisLab/Kai-30B-Instruct
Bring your hardest math, logic, and coding benchmarks. We invite the community to stress-test the limits of the penalty wall! 🧱💥
  • 1 reply
·
OzTianlu 
posted an update 7 days ago
view post
Post
1701
Scaling UP in Kai! 🌊
NoesisLab/Kai-3B-Instruct

Introducing NoesisLab/Kai-3B-Instruct What happens when you force a 3B model to reason entirely in its latent space ?
Meet Kai-3B, our latest industrial-grade reasoning model fine-tuned using the Adaptive Dual Search (ADS) algorithm.
GSM8K (0-shot, Direct Answer): 39.27% 🤯 (Llama-2-7B is ~14.6%)
HumanEval (Pass@1): 39.02% 💻 (Overtakes Gemma-2-2B's 30%)
MMLU (5-shot): 53.62% 📚 (Crushing the 50% barrier)
ARC-Challenge: 51.88%🎯
PIQA: 77.53%
HellaSwag: 69.53%
Kai-3B proves that reasoning density doesn't strictly require parameter bloat or verbose generation. It acts as a perfect, cold-blooded Agent action-engine—ideal for JSON routing, SWE-bench patch generation, and anywhere you need absolute structured certainty without token waste.
  • 2 replies
·
OzTianlu 
posted an update 9 days ago
view post
Post
1533
🛡️ Meet Spartacus-1B: Shattering the Memory Wall with True O(1) Inference! 🚀
NoesisLab/Spartacus-1B-Instruct
NoesisLab/ChatSpartacus
At NoesisLab, we've entirely ripped out Softmax Attention and replaced it with Causal Monoid State Compression.
Say hello to Spartacus-1B-Instruct (1.3B) 🗡️.
Instead of maintaining a massive, ever-growing list of past tokens, Spartacus compresses its entire causal history into a fixed-size state matrix per head. The result?
⚡ True O(1) Inference: Memory footprint and generation time per token remain absolutely constant, whether you are on token 10 or token 100,000.
🧠 Explicit Causality: We threw away RoPE and attention masks. The model learns when to forget using dynamic, content-aware vector decay.
🔥 Blazing Fast Training: Full hardware utilization via our custom Triton-accelerated JIT parallel prefix scan.
📊 Zero-Shot Benchmarks that Hit Hard:
O(1) architectures usually sacrifice zero-shot accuracy. Not Spartacus. It is punching way above its weight class, beating established sub-quadratic models (like Mamba-1.4B and RWKV-6-1.6B):
🏆 ARC-Challenge: 0.3063 (vs Mamba 0.284)
🏆 ARC-Easy: 0.5518
🏆 PIQA: 0.6915
prithivMLmods 
posted an update 9 days ago
view post
Post
2467
FireRed-Image-Edit-1.0 (Rapid) Fast Experimental Demo is Out! 🚀🤗

Demo: prithivMLmods/FireRed-Image-Edit-1.0-Fast

-> Paired the EditPlusPipeline with the Diffusers-compatible transformer weights of Rapid AIO from Qwen-Image-Edit. (experimental)
-> This fusion delivers more accurate instruction following, higher image quality, and consistent visual coherence @ 4-step fast inference.
-> Better maintains text styles with high fidelity, along with high-quality old photo restoration, enhancement, and best-in-class virtual try-on.
Ujjwal-Tyagi 
posted an update 10 days ago
view post
Post
2827
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
·
smcleod 
in LocalLLaMA/GuifreAI 12 days ago

What's the organisation & project relating to (I'm a r/localllama mod)
#1 opened 12 days ago by
smcleod
guifre103 
updated a model 12 days ago
guifre103 
published a model 12 days ago
prithivMLmods 
posted an update 13 days ago
MaziyarPanahi 
authored a paper 14 days ago
OzTianlu 
posted an update 16 days ago
view post
Post
3459
O(1) inference is the foundational design of Spartacus-1B-Instruct 🛡️ !

NoesisLab/Spartacus-1B-Instruct

We have successfully replaced the KV-cache bottleneck inherent in Softmax Attention with Causal Monoid State Compression. By defining the causal history as a monoid recurrence, , the entire prefix is lossily compressed into a fixed-size state matrix per head.

The technical core of this architecture relies on the associativity of the monoid operator:

Training: parallel prefix scan using Triton-accelerated JIT kernels to compute all prefix states simultaneously.
Inference: True sequential updates. Memory and time complexity per token are decoupled from sequence length.
Explicit Causality: We discard RoPE and attention masks. Causality is a first-class citizen, explicitly modeled through learned, content-dependent decay gates.

Current zero-shot benchmarks demonstrate that Spartacus-1B-Instruct (1.3B) is already outperforming established sub-quadratic models like Mamba-1.4B and RWKV-6-1.6B on ARC-Challenge (0.3063). Recent integration of structured Chain-of-Thought (CoT) data has further pushed reasoning accuracy to 75%.

The "Spartacus" era is about scaling intelligence, not the memory wall ♾️.
prithivMLmods 
posted an update 18 days ago
view post
Post
2576
Dropping the Qwen3 VL Series of Unredacted MAX-VL models. These models have undergone multi-stage training to minimize refusal rates through continuous abliterated optimization. You can find the models in BF16, FP8-Dynamic, and GGUF formats at the links below.🔥🚀

Unredacted MAX - VL:
➜ prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX
➜ prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX
➜ prithivMLmods/Qwen3-VL-8B-Instruct-Unredacted-MAX
➜ prithivMLmods/Qwen3-VL-8B-Thinking-Unredacted-MAX

Unredacted MAX - VL [FP8]
➜ prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-FP8
➜ prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-FP8
➜ prithivMLmods/Qwen3-VL-8B-Instruct-Unredacted-MAX-FP8
➜ prithivMLmods/Qwen3-VL-8B-Thinking-Unredacted-MAX-FP8

Unredacted MAX - VL [GGUF]
➜ prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF
➜ prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF
➜ prithivMLmods/Qwen3-VL-8B-Instruct-Unredacted-MAX-GGUF
➜ prithivMLmods/Qwen3-VL-8B-Thinking-Unredacted-MAX-GGUF

Unredacted MAX - VL [Collection]
➜ https://huggingface.co/collections/prithivMLmods/unredacted-max-vl-fp8
➜ https://huggingface.co/collections/prithivMLmods/unredacted-max-vl
➜ https://huggingface.co/collections/prithivMLmods/unredacted-max-vl-gguf

To learn more, visit the app page or the respective model pages.
Ujjwal-Tyagi 
posted an update 18 days ago
view post
Post
216
Qwen 3.5 Model is here! Supporting 1m context length by default, It is giving much good performance and competitive to Claude Opus 4.6, Qwen/Qwen3.5-397B-A17B, here it's GGUF: unsloth/Qwen3.5-397B-A17B-GGUF, Follow me and turn on the notification for the latest news!
Ujjwal-Tyagi 
posted an update 22 days ago
view post
Post
3009
GLM 5 is insane, it ranks #4 Globally!
·
OzTianlu 
posted an update 23 days ago
view post
Post
869
🚀 NanoHammer-1.5B-Instruct:
https://huggingface.co/NoesisLab/NanoHammer-1.5B-Instruct
We are excited to introduce NanoHammer, a novel architecture by NoesisLab designed for Causal State Compression and true Linear Inference Complexity.
🧠 The Core: Holographic State SpaceForget the growing KV Cache. NanoHammer leverages Holographic Rotary Embeddings to compress sequence history into a dynamic integral state.
Polynomial Compression: Instead of storing raw history, we "integrate" context into a complex number space , treating memory as a container of evolving polynomial coefficients.
Dynamic Evolution: The architecture features a custom StateUpdateCell that uses Euler method fixed-point iteration, allowing the model to perform implicit reasoning via differential state updates.
⚡ Why It Matters: Efficiency Meets Reasoning O(1) Inference Memory: State size remains constant regardless of sequence length.Causal Modeling: Explicitly models the causal flow of logic through time, perfect for "implicit reasoning" tasks without the verbosity of Chain-of-Thought.1.5B Lightweight Design: High performance, low resource footprint.
🛠 Model Card HighlightsType: nanohammer (Hybrid Causal-State Architecture)
License: Apache 2.0
Capabilities: Instruction following, Long-context handling
🔗 Try it on Hugging Face: https://huggingface.co/NoesisLab/NanoHammer-1.5B-Instruct
  • 1 reply
·
Parveshiiii 
posted an update 23 days ago
view post
Post
283
Introducing Seekify — a truly non‑rate‑limiting search library for Python

Tired of hitting rate limits when building search features? I’ve built Seekify, a lightweight Python library that lets you perform searches without the usual throttling headaches.

🔹 Key highlights

- Simple API — plug it in and start searching instantly

- No rate‑limiting restrictions

- Designed for developers who need reliable search in projects, scripts, or apps

📦 Available now on PyPI:

pip install seekify

👉 Check out the repo: https:/github.com/Parveshiiii/Seekify
I’d love feedback, contributions, and ideas for real‑world use cases. Let’s make search smoother together!
MaziyarPanahi 
posted an update 23 days ago
view post
Post
2198
Announcing: OpenMed Multilingual PII Detection Models

Today I am releasing 105 open-source models for Personally Identifiable Information (PII) detection in French, German, and Italian.

All Apache 2.0 licensed. Free for commercial use. No restrictions.

Performance:

- French: 97.97% F1 (top model)
- German: 97.61% F1 (top model)
- Italian: 97.28% F1 (top model)

All top-10 models per language exceed 96% F1

Coverage:

55+ PII entity types per language
Native ID formats: NSS (French), Sozialversicherungsnummer (German), Codice Fiscale (Italian)
Language-specific address, phone, and name patterns

Training Data:

French: 49,580 samples
German: 42,250 samples
Italian: 40,944 samples

Why Multilingual?

European healthcare operates in European languages. Clinical notes, patient records, and medical documents are generated in French, German, Italian, and other languages.

Effective de-identification requires:

- Native language understanding — not translation
- Local ID format recognition — each country has unique patterns
- Cultural context awareness — names, addresses, and formats vary
- These models deliver production-ready accuracy without requiring data to leave your infrastructure or language.

HIPAA & GDPR Compliance
Built for US and European privacy regulations:

- On-premise deployment: Process data locally with zero external dependencies
- Data sovereignty: No API calls, no cloud services, no cross-border transfers
- Air-gapped capable: Deploy in fully isolated environments if required
- Regulatory-grade accuracy: Supporting Expert Determination standards
- HIPAA and GDPR compliance across languages, without compliance gaps.

Use Cases
- Hospital EHR systems: Automated patient record de-identification
- Clinical research: Multilingual dataset preparation for studies
- Insurance companies: Claims processing across

https://huggingface.co/collections/OpenMed/multilingual-pii-and-de-identification
  • 1 reply
·