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posted an update 1 day ago

Post

1875

We're excited to release BananaMind 2 Micro, our smallest model yet.
It fits a compact architecture in only 2.9M parameters achieving the highest parameter efficiency on BananaMind Base Bench against comparable models.
It achieves comparable performance to GPT S2 5M and GPT S 5M at almost half the size while beating CMA 1M Mini.
BananaMind 2 Micro achieved the #1 spot on the Open SLM Leaderboard for the sub 3M category (not added yet but it achieves #1)
For the training we used Muon + the XSA Refresh Gate with a 5e-2 lr for Muon and 4e-3 for the 1D weights.
Its score on our efficiency measure is 0.326 getting the first place with Syn 2.6M on the second place scoring 0.291 and GPT S 5M at 0.235*
Check it out at BananaMind/BananaMind-2-Micro and follow us at:
@vovaRL
@DedeProGames
@Banaxi-Tech

BananaMind

Our new releases aren't stopping 🚀 August 13-14 BananaMind 2 Pro

7 replies

Nymbo

posted an update 1 day ago

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766

Anthropic gave me six months of Claude Max 20x through the Claude for Open Source program, granted based on my Hugging Face work. Thank you

Anthropic for supporting open source.

So far I've been pointing it at Markdown Minimap, an Obsidian plugin that adds a scrollable IDE-style minimap to your notes. This week I've been clearing a backlog of user-reported issues on it, with Claude often handling them end to end.

https://github.com/Nymbo/Markdown-Minimap — issues and PRs welcome.

onekq

posted an update 2 days ago

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2571

Looking forward to the Qwen 3.8 model drop, and congratulations on joining the trillion parameters club.

But my eyes are on the promised 27B model. Small (<50B) models decline on OpenRouter, because they are being run on local devices. If people see the family trees here on HF they will understand.

2 replies

danielhanchen

posted an update about 6 hours ago

Post

343

Introducing Unsloth Desktop 🦥
The first desktop app to run and train models locally.

• Open-source. Runs on Mac, Windows and Linux
• Supports MLX, diffusion image/video, audio, GGUF
• Connect Claude Code and Codex to local LLMs
• 50% more accurate, self-healing tool calls + sandboxed code exec
• Works for CPU + multiGPU setups - NVIDIA, AMD, Intel, Mac
• Train models 2× faster with 70% less VRAM
• Private web search, deep research, RAG, MCP and exports (NVFP4, GGUF)
• Use Unsloth’s OpenAI-compatible API and cloud models
• Securely deploy LLMs remotely and access anywhere

Unsloth Desktop is now available on http://unsloth.ai
and GitHub.

GitHub: https://github.com/unslothai/unsl
oth
Blog and Guide: https://unsloth.ai/docs/desktop

2 replies

LH-Tech-AI

posted an update about 7 hours ago

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335

Hey community and sponsors!

We are announcing the Supra3 family with four core SLM models:
- Supra3 Flash Lite: 25M parameters, ~60B pretraining tokens
- Supra3 Flash: 50M parameters, ~100B pretraining tokens
- Supra3 Pro: 75M parameters, ~150B pretraining tokens
- Supra3 Ultra: 100M parameters, ~200B pretraining tokens

For Supra3 Pro and Ultra, we search for sponsors who give us free access to compute like RTX 5090 32GB or so.

We estimate the total cost of the pro and ultra models at around $600.

For Supra3 Flash Lite and Flash, we do not need sponsors.

If anyone would apply for helping us, we would be really thankful and this person would get early access to new modele, insider information, credit and more!

Contact: here or on discord: lh_tech_ai

11 replies

OppaAI

posted an update 1 day ago

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1273

So many ideas, so little time and energy:

Another idea pops up today after spending the past weekend polishing the DAG Agentic workflow system of my AI Waifu system.
These past few days, as I was building workflows for my AI Waifu, the patterns and basic building blocks look very similar and probably can recycle for each workflow. So I propose a 5-layers Graph Engine to generalize all my workflows:
(I'm sure there are apps like that in the market, only the scale, complexity and price differs. But if f there is a generic app that can use local AI to let people construct workflows to run automated tasks, non-tech users or small companies can benefit from this.)

1) Planning - Users writes a detailed a spec of the workflow they want. Or even draws it in a n8n-like environment, defining each building block + args + queries, moving them around, arranging them to run in parallel/sequence, format the output, destination of the output, etc.

2) Orchestrating: AI system then interprets the spec, organizing the nodes into workflows, verified and tested by humans, then run the workflows and do the works. Reduce coding to the minimum for users.

3) Executing: Node is the basic building block of the graph and can be customizable, and sharable among different workflows; each node will call the registered tools or skills to do the work.

4) Tools and skills: exposed to AI system just like what we have right now.

5) Outputs: Generate reports of WIP and final results and users choose to get notified

With enough time and effort, and fund to invest, this and many other components of my AI system could potentially be a startup idea. Unfortunately I have neither the time, energy, fund, or talent to build productivity tools to benefit others, I can only build the components just good enough for my own use, and provide the source codes to other people to make the ideas scalable to production environment if they want.

dronefreak

posted an update about 12 hours ago

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439

🚀 Excited to open-source the GWHD Wheat Head Detection Model Zoo on Hugging Face.

This release includes:

- 🤖 YOLOv8, YOLOv11, YOLOv26 and RF-DETR object detection models trained on GWHD (Global Wheat Head Dataset), spanning nano through x-large variants across both architecture families.
- 🌾 Benchmarked on GWHD's dense, single-class wheat-head detection task — ~45 annotated heads per image on average, captured across multiple countries, genotypes, and growth stages, a genuinely hard small/dense-object setting.
- 📊 Detailed model cards with mAP/precision/recall, per-class breakdowns, PR/F1 curves and confusion matrices (YOLO), qualitative detection showcases, and full training configurations for reproducibility.

Headline numbers:
- 🏆 Best mAP@50: 74.25% (YOLOv11x), 34.92% mAP@50:95, 83.37% precision.
- ⚡ Best efficiency tradeoff: YOLOv26s hits 70.49% mAP@50 at just 22.8 GFLOPs (10.0M params) — within ~4 points of the top YOewer FLOPs (196.0 GFLOPs).

The goal is to make benchmarking and experimenting with agricultural computer vision easier by providing ready-to-use pretrained checkpoints, all trained and evaluated under one shared pipeline (DetectionBench: https://github.com/dronefreak/DetectionBench).
Full credit for the underlying dataset goes to Etienne David, Mario Serouart, Simon Madec, and the Global Wheat Head Detection 2020/2021) — this release is anunofficial, YOLO-ready reformatting of their work, not a new dataset.

If you're working on precision at detection, or just want areproducible detector benchmark, I hope these resources are useful.

📦 Dataset:
dronefreak/GWHD

🤖 Model Collection: dronefreak/gwhd-wheat-head-detection-model-zoo-6a7aea28b5431918cc46cec1

Feedback, bug reports, and contributions are always welcome.

3 replies

DavidAU

posted an update about 19 hours ago

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1861

Qwen3.6-40B-Fable-Fusion-6-Core-Deckard-Eleanor-Heretic-Uncensored

The fire of multiple Fable Fusion 711 cores in one, larger model.

This is the first 40B fine tune that reaches "closed source" (IE OpenAI, Claude) level of intelligence in both 8 bit and 4 bit.

This model is composed from multiple Qwen 27B Fable Fusion 711 cores (1700+ likes, 2.3 million+ downloads) - a record breaking model in terms of intelligence and raw power.

The "40B Eleanor" takes this to the next level with improvements in thinking tokens/ thinking block size (1/10 to 1/2 the size), thinking in general and output detail quality with deep analytics too.

- 1/10 to 1/2 the number of thinking tokens.
- Extreme depth of detail in generations, including long form, in depth analytics.
- STRONG creative abilities.
- Auto-variable reasoning: Model only reasons as much as the task requires.
- Strong general intelligence.
- It says what it means in less words, more clearly than any previous tuned model.
- It will go all in, in exacting detail when the situation calls for it.
- If it thinks something is wrong / wrong path it will say so too.

Regular and MTP Quants:

DavidAU/Qwen3.6-40B-Fable-Fusion-6-Core-Deckard-Eleanor-Heretic-Uncensored-NM-DAU-NEO-MAX-MTP-GGUF

1 reply

KlondikeDev

posted an update 3 days ago

Post

2100

Boris-2 coming soon!

The Models:

Boris-2-75M: Trained on 26B tokens -- estimated to start training on August 12th.
Boris-2-125M: Trained on 90B tokens -- Estimated to start training on August 20th.
Boris-2-250M: Trained on 60B tokens -- Estimated to start training on September 10th.

Why does 125M get more tokens than 250M?

Well, the straight answer is time. It saves time, while still allowing the 250M model to exceed the 125M model.

Furthermore, we are attempting a unique architecture and layering scheme to hopefully end up around the strength of SmolLM2-135M. Fingers crossed!

We hope to end up in the ballpark of AxiomicLabs/GPT-X2.5-135M or BananaMind/BananaMind-2-Pro-Preview

Following this, we will release the Pro, Instruct and Pro-Instruct variants. More info will be coming soon!

4 replies

wop

posted an update about 14 hours ago

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868

bench-labs/GCTokenizer-v1 , a multilingual tokenizer which does not require a training corpus

bench-labs developed **GCTokenizer-v1**, which is a multi-lingual tokenizer
Available in four sizes: 32K, 65K, 131K and 262K tokens "S, M, L, XL"
It utilizes an encoding scheme which allows it to handle characters in any language around the world

General (multi lingual)
Consensus (from multiple model tokenizers consensus)
Tokenizer

We included an implementation script too,
built like BPE- it can encode arbitrary text, most of the time, efficiently

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