wait first time I hear about it - absolute fan of Svelte I'll try it!
Victor Mustar PRO
victor
AI & ML interests
Building the UX of this website
Recent Activity
liked a dataset about 1 hour ago
datacurve/deep-swe updated a bucket about 5 hours ago
victor/ace-step-community updated a bucket about 6 hours ago
victor/pixal3d-communityOrganizations
reacted to rockypod's post with π₯ 6 days ago
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Still working on Svelte Coder. I will experiment with some other models so that I can get Svelte 5 trained better.
- 1 reply
posted an update 7 days ago
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Sharing how I built the LongCat-Video-Avatar 1.5 Space (+500k views on X) in one agent session. Gave a coding agent its own AI lab on ZeroGPU, framed the goal, walked away. It designed, deployed, tested against the live API, fixed, shipped.
Full recipe with the copy-paste prompt: https://huggingface.co/blog/victor/building-zerogpu-spaces-autonomously
Full recipe with the copy-paste prompt: https://huggingface.co/blog/victor/building-zerogpu-spaces-autonomously
- 1 reply
reacted to qgallouedec's post with π₯ 22 days ago
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Shipped hf-sandbox! π₯‘
π§ͺ Running an eval that executes model-generated C on a few thousand prompts? You probably don't want any of that on your laptop.
Just shipped hf-sandbox, a Modal-style sandbox API on top of Hugging Face Jobs. Spin up an isolated, ephemeral container, run untrusted code, get the result back. No Docker on your laptop, no infra to manage.
Just pip install hf-sandbox.
Early days (v0.1); feedback and issues very welcome:
π https://github.com/huggingface/hf-sandbox
π§ͺ Running an eval that executes model-generated C on a few thousand prompts? You probably don't want any of that on your laptop.
Just shipped hf-sandbox, a Modal-style sandbox API on top of Hugging Face Jobs. Spin up an isolated, ephemeral container, run untrusted code, get the result back. No Docker on your laptop, no infra to manage.
Just pip install hf-sandbox.
Early days (v0.1); feedback and issues very welcome:
π https://github.com/huggingface/hf-sandbox
- 1 reply
reacted to mipo57's post with π 23 days ago
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How do you train self-paying rl agents with jax? New colab that will set you up with Jaxpot is here: https://colab.research.google.com/drive/1-rm_Bh8CNaM861We97ZoicfgKxz0xOSi?usp=sharing
- 1 reply
reacted to Tonic's post with π€ about 1 month ago
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ππ»ββοΈ Hey there folks,
since everyone liked my previous announcement post ( https://huggingface.co/posts/Tonic/338509028435394 ) so much , i'm back with more high quality proceedural datasets in the Geospacial domain for SFT training !
Check this one out :
NuTonic/sat-bbox-metadata-sft-v1
the goal is to be able to train vision models on multiple images for remote sensing analysis with one shot .
hope you like it ! π
since everyone liked my previous announcement post ( https://huggingface.co/posts/Tonic/338509028435394 ) so much , i'm back with more high quality proceedural datasets in the Geospacial domain for SFT training !
Check this one out :
NuTonic/sat-bbox-metadata-sft-v1
the goal is to be able to train vision models on multiple images for remote sensing analysis with one shot .
hope you like it ! π
- 2 replies
replied to their post about 2 months ago
reacted to asigalov61's post with π₯ about 2 months ago
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π₯π΅ β πΉ π₯Check out my new large-scale MIDI + Lyrics dataset!!!
asigalov61/Lyrics-MIDI-Dataset
~179k MIDIs with corresponding Lyrics to play with!!! π€
If you liked the dataset, please β€οΈ
Any feedback and/or suggestions are also appreciated π€
asigalov61/Lyrics-MIDI-Dataset
~179k MIDIs with corresponding Lyrics to play with!!! π€
If you liked the dataset, please β€οΈ
Any feedback and/or suggestions are also appreciated π€
reacted to SeaWolf-AI's post with π₯ about 2 months ago
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𧬠Darwin-27B-Opus: 86.9% on GPQA Diamond β World #5, Zero Training
We are excited to share Darwin-27B-Opus, a 27B model that achieved 86.9% on GPQA Diamond β ranking #5 globally on the HuggingFace leaderboard β without a single gradient update.
How? Darwin breeds pretrained models through evolutionary FFN crossbreeding. The father (Qwen3.5-27B) provides the reasoning architecture; the mother (Claude 4.6 Opus Reasoning Distilled) contributes structured chain-of-thought knowledge. CMA-ES automatically discovers optimal per-layer blending ratios β no human tuning required.
The result surpasses the original Qwen3.5-27B (85.5%), GLM-5.1 (744B, 86.2%), and Qwen3.5-122B (86.6%). A 27B model outperforming 744B β with zero training, zero data, one GPU, ~2 hours.
We also confirmed hybrid vigor on Korean benchmarks: Darwin-27B-KR (2nd generation offspring) surpassed both parents on CLIcK, winning 7 out of 11 categories. The evolutionary optimizer independently assigned 93% of FFN from the Korean-specialized mother while preserving 93% of attention from the reasoning-specialized father β autonomously validating our core principle: FFN carries knowledge, Attention carries reasoning.
π Public release: 10 days β 300+ community derivatives, 120K+ downloads.
π Links:
Darwin-27B-Opus: FINAL-Bench/Darwin-27B-Opus
article: https://huggingface.co/blog/FINAL-Bench/darwin-gpqa
Darwin Family Collection: https://huggingface.co/collections/FINAL-Bench/darwin-family
If foundation models are raw ore, Darwin is the forge. We are just getting started. π₯
We are excited to share Darwin-27B-Opus, a 27B model that achieved 86.9% on GPQA Diamond β ranking #5 globally on the HuggingFace leaderboard β without a single gradient update.
How? Darwin breeds pretrained models through evolutionary FFN crossbreeding. The father (Qwen3.5-27B) provides the reasoning architecture; the mother (Claude 4.6 Opus Reasoning Distilled) contributes structured chain-of-thought knowledge. CMA-ES automatically discovers optimal per-layer blending ratios β no human tuning required.
The result surpasses the original Qwen3.5-27B (85.5%), GLM-5.1 (744B, 86.2%), and Qwen3.5-122B (86.6%). A 27B model outperforming 744B β with zero training, zero data, one GPU, ~2 hours.
We also confirmed hybrid vigor on Korean benchmarks: Darwin-27B-KR (2nd generation offspring) surpassed both parents on CLIcK, winning 7 out of 11 categories. The evolutionary optimizer independently assigned 93% of FFN from the Korean-specialized mother while preserving 93% of attention from the reasoning-specialized father β autonomously validating our core principle: FFN carries knowledge, Attention carries reasoning.
π Public release: 10 days β 300+ community derivatives, 120K+ downloads.
π Links:
Darwin-27B-Opus: FINAL-Bench/Darwin-27B-Opus
article: https://huggingface.co/blog/FINAL-Bench/darwin-gpqa
Darwin Family Collection: https://huggingface.co/collections/FINAL-Bench/darwin-family
If foundation models are raw ore, Darwin is the forge. We are just getting started. π₯
reacted to prithivMLmods's post with β€οΈ about 2 months ago
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A new comparator on Spaces showcases Standard FLUX.2 Decoder vs. FLUX.2 Small Decoder. The Small Decoder is ~1.4Γ faster, uses ~1.4Γ less VRAM, and maintains near-identical image quality. It has ~28M parameters with narrower channels [96, 192, 384, 384] vs. [128, 256, 512, 512], and the demo supports sequence generation by running both decoders simultaneously and comparing the results side by side.
π€ Comparator: https://huggingface.co/spaces/prithivMLmods/Flux.2-4B-Decoder-Comparator
π FLUX.2-small-decoder: black-forest-labs/FLUX.2-small-decoder
π GitHub: https://github.com/PRITHIVSAKTHIUR/Flux.2-4B-Encoder-Comparator
π Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
π€ > App built on the Gradio SDK. To learn more, visit the app page or the respective model pages.
π€ Comparator: https://huggingface.co/spaces/prithivMLmods/Flux.2-4B-Decoder-Comparator
π FLUX.2-small-decoder: black-forest-labs/FLUX.2-small-decoder
π GitHub: https://github.com/PRITHIVSAKTHIUR/Flux.2-4B-Encoder-Comparator
π Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
π€ > App built on the Gradio SDK. To learn more, visit the app page or the respective model pages.
posted an update about 2 months ago
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Want to share my enthusiasm for zai-org/GLM-5.1 here too π₯
I think we have it: our open source Claude Code = GLM-5.1 + Pi (https://pi.dev/) - Built a Three.js racing game to eval and it's extremely impressive. Thoughts:
- One-shot car physics with real drift mechanics (this is hard)
- My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen
- 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters
- All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio!
- Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed
You are going to hear about this model a lot in the next months - open source let's go - and thanks z-aiππ
I think we have it: our open source Claude Code = GLM-5.1 + Pi (https://pi.dev/) - Built a Three.js racing game to eval and it's extremely impressive. Thoughts:
- One-shot car physics with real drift mechanics (this is hard)
- My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen
- 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters
- All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio!
- Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed
You are going to hear about this model a lot in the next months - open source let's go - and thanks z-aiππ
- 5 replies
reacted to Juanxi's post with π₯ about 2 months ago
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π’ Awesome Multimodal Modeling
We introduce Awesome Multimodal Modeling, a curated repository tracing the architectural evolution of multimodal intelligenceβfrom foundational fusion to native omni-models.
πΉ Taxonomy & Evolution:
Traditional Multimodal Learning β Foundational work on representation, fusion, and alignment.
Multimodal LLMs (MLLMs) β Architectures connecting vision encoders to LLMs for understanding.
Unified Multimodal Models (UMMs) β Models unifying Understanding + Generation via Diffusion, Autoregressive, or Hybrid paradigms.
Native Multimodal Models (NMMs) β Models trained from scratch on all modalities; contrasts early vs. late fusion under scaling laws.
π‘ Key Distinction:
UMMs unify tasks via generation heads; NMMs enforce interleaving through joint pre-training.
π Explore & Contribute: https://github.com/OpenEnvision/Awesome-Multimodal-Modeling
We introduce Awesome Multimodal Modeling, a curated repository tracing the architectural evolution of multimodal intelligenceβfrom foundational fusion to native omni-models.
πΉ Taxonomy & Evolution:
Traditional Multimodal Learning β Foundational work on representation, fusion, and alignment.
Multimodal LLMs (MLLMs) β Architectures connecting vision encoders to LLMs for understanding.
Unified Multimodal Models (UMMs) β Models unifying Understanding + Generation via Diffusion, Autoregressive, or Hybrid paradigms.
Native Multimodal Models (NMMs) β Models trained from scratch on all modalities; contrasts early vs. late fusion under scaling laws.
π‘ Key Distinction:
UMMs unify tasks via generation heads; NMMs enforce interleaving through joint pre-training.
π Explore & Contribute: https://github.com/OpenEnvision/Awesome-Multimodal-Modeling
- 3 replies
reacted to qgallouedec's post with π₯ 2 months ago
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TRL v1.0 is out!
Hugging Face's TRL library is downloaded 3 million times a month. Over 130k models trained with it are public on the Hub, and major projects like @unsloth and @axolotl-ai-co build directly on top of it. v1.0 is the moment we acknowledged that responsibility explicitly, with a real stability contract.
The field hasn't settled. Building stable software in a domain that keeps invalidating its own assumptions is the actual problem we're solving. The answer is a design that can absorb the next shift without breaking what people rely on.
What's in v1.0:
Deep Hugging Face integration, low infrastructure burden
What's next: asynchronous GRPO, better scaling support, and making training legible enough that agents can inspect and steer it.
Read more: hf.co/blog/trl-v1
Hugging Face's TRL library is downloaded 3 million times a month. Over 130k models trained with it are public on the Hub, and major projects like @unsloth and @axolotl-ai-co build directly on top of it. v1.0 is the moment we acknowledged that responsibility explicitly, with a real stability contract.
The field hasn't settled. Building stable software in a domain that keeps invalidating its own assumptions is the actual problem we're solving. The answer is a design that can absorb the next shift without breaking what people rely on.
What's in v1.0:
Deep Hugging Face integration, low infrastructure burden
What's next: asynchronous GRPO, better scaling support, and making training legible enough that agents can inspect and steer it.
pip install --upgrade trlRead more: hf.co/blog/trl-v1
reacted to MikeDoes's post with π 2 months ago
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Ai4Privacy has been working on this for the past year. π
Today we're releasing the PII Masking 2M Series, the world's largest open source privacy masking dataset. (Again. ππ)
π’ 2M+ synthetic examples
π 32 locales across Europe
π·οΈ 98 entity types
π₯π¬π¦πΌπ 5 industry verticals: Health, Finance, Digital, Work, Location
β 1M+ entries freely available on Hugging Face
Every example is 100% synthetic. No real personal data. Built so you can train and evaluate PII detection models without the legal headaches. π
Thank you for 15,000,000+ downloads across our datasets, models, and libraries. This one's for you. β€οΈ
hashtag#privacy hashtag#ai hashtag#opensource hashtag#nlp hashtag#gdpr hashtag#pii hashtag#huggingface hashtag#machinelearning
Today we're releasing the PII Masking 2M Series, the world's largest open source privacy masking dataset. (Again. ππ)
π’ 2M+ synthetic examples
π 32 locales across Europe
π·οΈ 98 entity types
π₯π¬π¦πΌπ 5 industry verticals: Health, Finance, Digital, Work, Location
β 1M+ entries freely available on Hugging Face
Every example is 100% synthetic. No real personal data. Built so you can train and evaluate PII detection models without the legal headaches. π
Thank you for 15,000,000+ downloads across our datasets, models, and libraries. This one's for you. β€οΈ
hashtag#privacy hashtag#ai hashtag#opensource hashtag#nlp hashtag#gdpr hashtag#pii hashtag#huggingface hashtag#machinelearning
reacted to unmodeled-tyler's post with π 2 months ago
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Hey Hugging Face!
PRODUCT HUNT LINK: https://www.producthunt.com/products/quanta-intellect?utm_source=other&utm_medium=social
I've been sharing my new AI browser Vessel the last few days and I've gotten some great feedback/interest from a lot of you!
I'm excited to announce that Vessel Browser is now live on Product Hunt! If this is the first you've heard of it, check it out! Vessel is an open source AI browser built specifically for agents on Linux. It's not a fork of an existing browser, and it doesn't assume that the human is the primary operator.
If you've already tried Vessel Browser, feel free to leave a review on Product Hunt of what you thought - or if you'd prefer, send me an email directly or reach out on twitter if you have any questions about it. I'm perpetually online & happy to chat π
I'm committed to building the best open source AI browser out there, and Vessel is only going to improve as time goes on!
PRODUCT HUNT LINK: https://www.producthunt.com/products/quanta-intellect?utm_source=other&utm_medium=social
I've been sharing my new AI browser Vessel the last few days and I've gotten some great feedback/interest from a lot of you!
I'm excited to announce that Vessel Browser is now live on Product Hunt! If this is the first you've heard of it, check it out! Vessel is an open source AI browser built specifically for agents on Linux. It's not a fork of an existing browser, and it doesn't assume that the human is the primary operator.
If you've already tried Vessel Browser, feel free to leave a review on Product Hunt of what you thought - or if you'd prefer, send me an email directly or reach out on twitter if you have any questions about it. I'm perpetually online & happy to chat π
I'm committed to building the best open source AI browser out there, and Vessel is only going to improve as time goes on!
reacted to prabhatkr's post with π 2 months ago
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π Is Vector RAG Dead? Why We Built FastMemory to Beat PageIndex
If you've built a RAG pipeline for complex financial documents, you already know the painful truth: Standard vector search fails when things get complicated.
While tools like PageIndex and Mafin 2.5 provide great out-of-the-box PDF chat experiences, they hit structural bottlenecks the second you push them past basic queries.
We just published a comprehensive benchmark study comparing FastMemory against PageIndex across 5 advanced datasets. The results fundamentally change how we should think about document ingestion.
Read more: https://x.com/FastBuilderAI/status/2037404008978018493
If you've built a RAG pipeline for complex financial documents, you already know the painful truth: Standard vector search fails when things get complicated.
While tools like PageIndex and Mafin 2.5 provide great out-of-the-box PDF chat experiences, they hit structural bottlenecks the second you push them past basic queries.
We just published a comprehensive benchmark study comparing FastMemory against PageIndex across 5 advanced datasets. The results fundamentally change how we should think about document ingestion.
Read more: https://x.com/FastBuilderAI/status/2037404008978018493
- 2 replies
reacted to branikita's post with π₯ 2 months ago
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We have received the majority of components for our first small commercial batch of SO-ARM101 robotic arms. Feetech STS3250 servo drives, parallel gripper, and depth camera.
- 1 reply
reacted to prometechinc's post with π 2 months ago
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CicikuΕ v4-5B (POFUDUK Edition) is a next-generation compact language model engineered for high-efficiency reasoning, adaptive intelligence, and behavioral coherence. Built on the Gemma 4B IT foundation and enhanced through advanced LoRA optimization and selective layer reconstruction, this model delivers powerful performance without the overhead of massive parameter counts.
π Explore the model: pthinc/pofuduk_cicikus_v4_5B
π§ Why CicikuΕ?
In a world dominated by massive LLMs, CicikuΕ takes a different path:
β‘ Fast & Efficient β Designed for edge deployment and low-resource environments
π― High Reasoning Accuracy β Strong results across MMLU, GSM8K, HumanEval, and more
𧩠Behavior-Aware Intelligence β Powered by the Behavioral Consciousness Engine (BCE)
π Low Hallucination Rate β ~3% with built-in ethical filtering
π Multilingual Capable β Optimized for English and Turkish
π Explore the model: pthinc/pofuduk_cicikus_v4_5B
π§ Why CicikuΕ?
In a world dominated by massive LLMs, CicikuΕ takes a different path:
β‘ Fast & Efficient β Designed for edge deployment and low-resource environments
π― High Reasoning Accuracy β Strong results across MMLU, GSM8K, HumanEval, and more
𧩠Behavior-Aware Intelligence β Powered by the Behavioral Consciousness Engine (BCE)
π Low Hallucination Rate β ~3% with built-in ethical filtering
π Multilingual Capable β Optimized for English and Turkish
reacted to prithivMLmods's post with π₯ 2 months ago
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Flux-Klein-KV-Edit-Consistency demo is now available on Spaces. It preserves character identity and delivers high-quality, realistic results after edits. No need for any special prompts, just upload the image, type your prompt, and get the resulting image blazing fast.
π₯ Demo Space: prithivMLmods/flux-klein-kv-edit-consistency
π€ Model: black-forest-labs/FLUX.2-klein-9b-kv
π€ Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
π Gradio Server Mode: https://www.gradio.app/main/guides/server-mode
β Built with Headless Gradio, an alternative to using gr.Blocks for creating the frontend and triggering events, powered by FastAPI + Gradio. You can now design the frontend however you want, with continued support for APIs, MCP, and ZeroGPU.
β Gradio Server Mode is now available from gradio@v6.10.0.
To learn more, visit the app page or the respective model pages.
π₯ Demo Space: prithivMLmods/flux-klein-kv-edit-consistency
π€ Model: black-forest-labs/FLUX.2-klein-9b-kv
π€ Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
π Gradio Server Mode: https://www.gradio.app/main/guides/server-mode
β Built with Headless Gradio, an alternative to using gr.Blocks for creating the frontend and triggering events, powered by FastAPI + Gradio. You can now design the frontend however you want, with continued support for APIs, MCP, and ZeroGPU.
β Gradio Server Mode is now available from gradio@v6.10.0.
To learn more, visit the app page or the respective model pages.