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posted an update 1 day ago

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2908

Qwen3-14B Manim Expert LoRA

For "Build Small Hackathon", I built a Gradio app that turns any concept into a Manim explainer video.

This is powered by Qwen3-14B + Manim LoRA I trained on a synthetic 10k dataset I generated.

👉 Try it now: build-small-hackathon/anim-vid-ai

2 replies

SeaWolf-AI

posted an update 2 days ago

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4771

Darwin V9 — GPQA Diamond 90.9%, #1 on the leaderboard, with pure greedy decoding
Darwin-398B-JGOS reaches 90.9% (180/198) on GPQA Diamond, the PhD-level scientific reasoning benchmark, ranking #1 on the Hugging Face GPQA Diamond leaderboard. No self-consistency, no test-time compute scaling — this was achieved with a single greedy decode (temperature 0, single sample, max 16,384 tokens). The full eval config is published in the model card, so anyone can reproduce it. Raw reasoning, no score inflation.
The result comes from Darwin V9, a patented evolutionary model-development platform. Its core idea: it never trains a model from scratch.
Why Darwin V9 beats training from scratch

Cost & speed: no trillion-token pretraining run, no months of compute — a purpose-built, high-performance model is produced in a fraction of the time.
Reuse of proven intelligence: instead of re-learning every capability from a blank slate, it selects and combines only the strengths of already-trained, already-validated models, so results are stable and predictable.
Surgical transplantation: it identifies which neural region of which model holds which capability — at the FFN (Feed Forward Network) layer level — and grafts in only the segments that contribute to the target skill.

How it works: a large model (Qwen 3.5 397B) serves as the mother model (the substrate); several father models specialized in reasoning, coding, and language are analyzed layer-by-layer across their FFN regions; the segments that contribute to the target performance are extracted and transplanted into the mother model to produce a new child model. The result is a ~400B MoE that activates only ~17B parameters per token at inference — large-model capacity with efficient inference.
If training from scratch means rebuilding everything from a blank page, Darwin V9 means precisely recombining intelligence that has already been proven. GPQA Diamond #1 is the proof.
Model: FINAL-Bench/Darwin-398B-JGOS

mmhamdy

posted an update 1 day ago

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742

What if you could train a model on just 10 images instead of 60,000 and still get close to the same performance?

Traditional machine learning requires thousands, even millions, of data points to achieve high accuracy. But what if we could "distill" the entire dataset into just a few synthetic samples?

This is what Dataset Distillation offers. Unlike traditional knowledge distillation, we keep the model fixed and distill the knowledge contained in a massive training set into a tiny set of synthetic distilled images.

The goal is to train a model on this ultra-small set and achieve performance that almost matches what the same model would get when trained on the massive original dataset.

For example, training on only 10 distilled MNIST images (this is equivalent to a single image per class) yields 94% accuracy, compared to 99% when training on the full 60,000 images.

Interestingly, these distilled images look significantly different (as you can see in the image below) from natural images because they are optimized for model training rather than for matching the correct data distribution.

But that's not all.

Most importantly, this same method opens the door to a potent form of data poisoning. Because distilled images are specifically optimized for rapid learning, an attacker can create a tiny set of adversarial distilled images to cause a well-trained model to forget or misclassify a specific category.

What I find fascinating about dataset distillation is this: it mimics human-like learning by letting a model grasp a concept from a single example, but it does so using alien synthetic images that mean absolutely nothing to a human eye!

What about you? What are your thoughts on it?

1 reply

prithivMLmods

posted an update 2 days ago

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2464

Wan2.2-I2V-Fast with highly upscaled sequential frame sampling is now available as a Spaces demo, built using Wan2.2-I2V and FLUX.2-Klein. Try the demo using the links below.👇

➠ wan2.2-i2v-fast : prithivMLmods/wan2.2-i2v-fast
➠ github: https://github.com/prithivsakthiur/wan2.2-i2v-fast
➠ collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection

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

kanaria007

posted an update 1 day ago

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100

✅ Article highlight: *Institutional Memory & Forgetting for Learning Worlds* (art-60-172, v0.1)

TL;DR:
This article argues that if a living world becomes training data, memory becomes infrastructure.

Logs, dialogue, labels, releases, feature stores, and model weights can turn a world into something that cannot honestly forget. 172 makes deletion, redaction, exclusion, forgetting requests, SANITIZED/PUBLIC releases, and unlearning claims into receipted governance lifecycles.

Read:
kanaria007/agi-structural-intelligence-protocols

Why it matters:
• prevents learning worlds from becoming “unforgettable worlds”
• separates deletion, redaction, and future extraction exclusion
• makes right-to-be-forgotten requests caseable and appealable
• preserves canon facts without preserving every memory surface
• blocks public promises like “guaranteed deletion everywhere”

What’s inside:
• retention policy contracts for what may be kept, copied, trained on, or released
• corpus segment manifests and propagation indexes for known controlled copies
• forgetting request, adjudication, remedy, deletion, redaction, and exclusion receipts
• tombstone manifests and semantic preservation receipts for canon-safe forgetting
• use eligibility receipts for deciding whether a segment may train a future run
• release contracts, redaction maps, and irreversibility disclosures for SANITIZED/PUBLIC releases
• bounded unlearning contracts and post-unlearning verification receipts

Key idea:
Do not say:

*“we deleted it, so it is forgotten.”*

Say:

*“this subject was handled under this retention policy, propagation index, adjudication path, remedy contract, tombstone, semantic preservation receipt, extraction exclusion receipt, and bounded public claim.”*

Forgetting is not a button.

It is governance with receipts.

danielhanchen

posted an update 1 day ago

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1371

Google's new DiffusionGemma can now run at 2000+ tokens/sec! ⚡

We made local DiffusionGemma inference 1.8× faster.
Run it on 18GB RAM via Unsloth Studio.

GitHub: https://github.com/unslothai/unsloth
Guide: https://unsloth.ai/docs/models/diffusiongemma

2 replies

nevmenandr

posted an update 2 days ago

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669

🔥 New Russian Stylometry Dataset!

Russian Stylometric Dataset (RSD) — 322 texts from the 19th – early 20th centuries (16 million words), prepared for analysis in stylo (R) and machine learning (Python).

📚 What's inside?

Fiction, journalism, scientific texts, drama, poetry

Grouped by author, gender, age, genre, literary movements (Romanticism/Realism)

Character speech (Tolstoy, Gogol, Ostrovsky)

Generated texts (LSTM, GPT)

📊 Use cases: authorship attribution, clustering, classification, benchmarking methods.

🔓 Public domain + GPL-3.0 license.

👉 Learn more: https://github.com/nevmenandr/RSD

DOI: 10.5281/zenodo.20701309

Kasualdad

posted an update 2 days ago

Post

678

From Plain English to DuckDB SQL: Building LFEDS
🏫 I just shipped Local First Education Data Stack— a plain-English-to-SQL assistant for school district analytics — for the HF Build Small Hackathon.

The problem: school staff have useful data (attendance, grades, enrollment, discipline) but no fast, private way to ask questions. Most AI tools send that data to a cloud API. LFED doesn't.

What it does:
→ Type a question like "What's the average GPA for chronically absent students in 2023-2024?"
→ A fine-tuned Qwen2.5-Coder-14B model generates DuckDB SQL
→ A validation layer rejects anything that isn't a SELECT
→ Results come back as a summary, table, CSV download, and the SQL itself

Two flavors:
- Live Space demo: transformers + PEFT on HF ZeroGPU
- Local-first: llama.cpp + GGUF Q4_K_M on your own machine — no data leaves

The fine-tune:
- 27,859 synthetic NL→SQL pairs
- Unsloth QLoRA r=32 on Qwen2.5-Coder-14B
- Trained on Modal A10G

Hardest lessons were not model training:
1. Scope the model's job tightly — schema + few-shots + SELECT only.
2. Validate before executing. Always.
3. ZeroGPU is PyTorch-only; llama.cpp won't work there.
4. Gradio's scoped Svelte CSS beats generic selectors — inspect the live DOM.
5. modal deploy + fn.spawn() is fire-and-forget; modal run dies if your terminal drops.
6. Data artifacts matter as much as the model — Parquet seeds, dataset card, model card.

I also published the training dataset: 25,886 question→SQL pairs on the Hub.

Links:
Demo: https://youtu.be/cE0yp4qmFIA
- Live Space: build-small-hackathon/Kasualdad_LFED
- LoRA adapter: build-small-hackathon/lfed-qwen2.5-coder-14b-sql-lora
- GGUF: build-small-hackathon/lfed-qwen2.5-coder-14b-sql-gguf
- Dataset: build-small-hackathon/lfed-training-data

#BuildSmallHackathon #BackyardAI #HuggingFace #TextToSQL #DuckDB #LocalFirst #EdTech #Qwen #QLoRA #LLM

loay

posted an update 2 days ago

Post

617

I built EchoYard for the

build-small-hackathon : a tiny listen-and-repeat language practice app.

Pick a language, level, and voice style, listen to a short reference voice, record yourself, then get simple speaking feedback and a next practice step.

Built with

openbmb VoxCPM2 for multilingual reference audio and MiniCPM5-1B for friendly feedback.

Try it here: https://build-small-hackathon-echoyard.hf.space

Would love feedback, especially on the recording flow and how useful the speaking tips feel.

ykirpichev

posted an update 2 days ago

Post

652

Glass-Box Agent for Build Small Hackathon.

A tiny ReAct-style agent where the trace is the interface: click a thought, retry a branch, label weak/useful nodes, and export preference pairs for DPO/RL-style training.

Space: build-small-hackathon/glass-box-agent
Demo: included in the Space at assets/glass-box-agent-demo.mp4
Track: An Adventure in Thousand Token Wood

#BuildSmallHackathon #Gradio #SmallModels

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