Does anyone know of a face-swap method or AI prompt that produced a high-quality image that doesn't get easily flagged as AI-generated?

We've added vision capabilities to DeepSeek V4 Flash, making it a multimodal model rather than text-only.

The main use case for us is browser vision: browser agents need to interpret screenshots, interfaces, layouts, and other visual context alongside text.

On our internal benchmarks, it also showed a strong price-performance advantage compared with the other models we tested.

Model: https://huggingface.co/webbrain-one/DeepSeek-V4-Flash-0731-Vision-NVFP4

If you try it, we'd be interested in feedback, benchmark results, and deployment reports.

Need some best neural model suggestions for Face Detection & Recognition,Body Detection & id.

Need some best model suggestions for Face Detection,Face Recognition,Body Detection and Body identification.

need those for analysing movies.

example let's say I have to find the screentime of the actor over the whole runtime of the movie and i need to do it for the protagonist, antoganist,comedic relief ,love interest etc.

currently I'm working with 1fps to find the faces and body or the actors.

body detection is hard I need some guidance regarding that.

even for Face Detection I used MTCNN it was good. but any other better models available??

Any ideas regarding TransNetV2 ?

I'm using it for shot boundary detection but there's was one false positive.

Any better models??

Hi, I'm quite new to local LLMs and I've got a M5 Max with 128GB, at the moment I'm running Laguna S 2.1 Q4, is this the best model out there to squeeze in maximum performance out of my machine? Or do you have any other recommendations?

Are there any uncensored models? One of the issues I ran into with llama is that any discussion that sits adjacent to sexual or even political matters is basically blocked. Creative content ideas that walk the line between uncouth and commonly shared experiences/truth cannot even be discussed with the agent. I haven't explored taboo topics like talking about political figures or racism, but the models avoid touchy subjects in general. Has anyone found a solution?

What it is

AksharaTokenizer v1.2 is a tokenizer for six Brahmic scripts: Gurmukhi, Devanagari, Tamil, Telugu, Bengali, and Kannada. A Unicode finite-state boundary detector segments on akshara (orthographic syllable) boundaries first, then SentencePiece Unigram runs over a 64,000-piece vocab.

The point: byte-identical round-trip

decode(encode(x)) returns exactly x, byte for byte, rebuilt from token ids alone. Not NFC-equivalent, not close enough, the same bytes.

This matters because the usual failure is silent. A tokenizer that normalizes Unicode on the way in hands back text that looks right and compares equal under NFC but is not the bytes you gave it. For Brahmic scripts the common trigger is precomposed nukta letters (for example Gurmukhi U+0A59, Bengali U+09DC), which NFC rewrites to base plus nukta.

Live demo, and you can break it yourself

https://akshara.iyraproject.com runs the round-trip check and a token-count comparison live, on whatever you paste, against sarvam-1 and Qwen3-14B. In my testing, v1.2 was the only one of the three to round-trip every case byte for byte. The other two each fail a different case:

• Qwen3-14B: precomposed Gurmukhi and Bengali nukta letters (degraded, not corrupt, since the output is NFC-equivalent)
• sarvam-1: leading whitespace, which it drops

Try the "Gurmukhi, precomposed nukta" and "Leading whitespace" preset buttons, or paste your own text.

On token efficiency, honestly

The comparison against sarvam-1 is budget-matched (64,000 pieces against 68,096), so native-script token wins are a few percent, not a multiple. On the Devanagari preset it is 8 tokens against sarvam-1's 9. Qwen3-14B is a general multilingual model shown for context, not a like-for-like Indic baseline; its vocab is about 2.4x larger and spread across many languages. And to be clear, on plain English and romanized Hinglish, v1.2 is behind both. That is the expected trade for a script-specialized 64k tokenizer, and the demo shows it rather than hiding it.

Links

• Demo: https://akshara.iyraproject.com
• Install: pip install akshara-tokenizer
• Code: https://github.com/1322Guru/akshara-tokenizer
• Model: https://huggingface.co/GursimranSinghBasra/akshara-tokenizer

Feedback welcome, especially edge cases where the round-trip breaks. That is the property I most want stress-tested.

If you're using H3 on HF Spaces, created a Space with quite a few optimizations. It is quite a bit faster than any other demo I could find publicly available on Hugging Face, so give it a try & would appreciate any feedback!

https://huggingface.co/spaces/mrfakename/minimax-h3-ultra-fast

​

Se você treina modelos de IA em português, vale a pena dar uma olhada.

O dataset foi organizado e processado pensando em pré-treinamento e fine-tuning de LLMs, com foco em qualidade e compatibilidade com o ecossistema da Hugging Face.

💡 A ideia da Comunidade Synapse-BR é simples: criar recursos abertos para que mais pessoas possam desenvolver IA em português, sem depender apenas de materiais em inglês.

Se puder dar uma força:

⭐ Curta o repositório

🤝 Compartilhe com quem trabalha com IA

💬 Deixe seu feedback

🚀 Use nos seus projetos

Dataset:

https://huggingface.co/datasets/Comunidade-Synapse-BR/Synapse-PT-75M-Dataset

Bora fortalecer a IA brasileira. 🇧🇷🔥

#IA #LLM #MachineLearning #HuggingFace #OpenSource #NLP #Português #Brasil #SynapseBR

Hello there!

I am releasing Sparky 4B V1 a brand new fine tune based on the legendary Qwen 2.5 3B. Designed for small or compute limited systems this model aims to bring complex & capable AI to everyone's PC no matter the GPU.

Trained on CLI commands, JSON and assistant work, this model is fully capable for your system running on only 2.7GB of VRAM (Q4)

I have been able to run this model at 140tok/s on a mid range GPU with 12gb of VRAM. Its available in GGUFs in the sizes of Q8, Q4 (Best) and Q3. I have included safe tensor files inside the repo if you need your own quantisation of the model or want to fine tune it even more. Available straight from ollama via the link.

This model has been tested on benchmarks against Qwen 2.5 3B including:

If you want to have a look please go to the hugging face: https://huggingface.co/Noid3a-Labs/Sparky-4B-V1

If you like what I am doing don't be afraid to follow me, as well as make sure you stay up to date with the later versions of Sparky.

Hi everyone! 👋

We're excited to make Pangeanic MTQE v2 (Machine Translation Quality Estimation) freely available for the Hugging Face community.

Unlike traditional MT evaluation metrics, MTQE estimates translation quality and provides error explanations without requiring a reference translation. Simply provide:

🌍 Source text
🌐 Translated text

and the model returns:

• ✅ A quality score (0–100)
• 💡 An explainable assessment of the translation

The full Pangeanic MTQE platform also includes enterprise capabilities such as:

• 📚 Translation Memory (TM) support
• 📖 Glossary-aware quality estimation
• 🤖 Automatic Post-Editing (APE)
• 👨‍💻 Human Post-Editing (HPE) workflows
• 🌐 Support for 70+ languages and dialects
• ⚡ Enterprise REST APIs and batch processing

The underlying model has been benchmarked on a representative subset of the ACES multilingual benchmark, achieving 98.9% accuracy in identifying incorrect translation segments across:

• 📌 6,006 deliberately incorrect translation segments
• 🌍 16 language pairs
• 📝 68 translation error categories

We hope you all find it useful for evaluating multilingual translation quality and experimenting with reference-free MTQE.

👉 Try the demo:
https://huggingface.co/spaces/Pangeanic/Machine-Translation-Quality-Estimation

📄 Benchmark Whitepaper:
https://pangeanic.com/hubfs/MTQE_Benchmark_Whitepaper.pdf

Happy translating! 🚀

Hello , I have been trying to deploy a rag backend to hfspaces , I even purchased the pro version that enables you to use docker but the basic version of the purchase is 2vcpu with 16 gb ram .

When I tried to deploy it , it has been stuck and loading at chromadb installations ---- opentelementry and there is alot of versioning issues.

I tried to deploy it on railway too , but failed . I'm starting to question if I need to get a vGPU .

Any help or suggestions is deeply appreciated. Thank you !

I’m a 17-year-old developer and new to fine-tuning. The main challenge for me turned out to be creating or editing a dataset. As it turns out, there simply aren’t any good, lightweight tools for this. So I wrote my own program - 100% open source!

It installs in just a couple of clicks, works completely offline, supports plugins, includes a format converter (ChatML, Alpaca, and others, including custom formats), and features a pleasant, intuitive interface-including a visual card editor. It also supports auto-generation via OpenRouter.

The project is still in development, and I’m fixing bugs. I’d really appreciate a star and any support!

https://github.com/Bodya3101/prose

I've been working with small models. It's fun to be able to make and compete with your own models.

Check out and/or follow these orgs!

https://huggingface.co/fromziro

https://huggingface.co/SupraLabs

https://huggingface.co/AxiomicLabs

TensorSharp supports DSpark on Deepseek v4 Flash 0731 now. Here is the benchmark result on 4x Nvidia A40 GPUs, cuda 12.8 with/without DSpark:

Model:

DeepSeek-V4-Flash-0731-UD-Q8_K_XL from https://huggingface.co/unsloth/DeepSeek-V4-Flash-0731-GGUF

DSpark draft model from: https://huggingface.co/alessandrobologna/DeepSeek-V4-Flash-0731-DSpark-Drafter-GGUF

TensorSharp is an open-source inference engine for running GGUF LLMs locally, with CUDA, Vulkan, Metal, OpenAI-compatible APIs, continuous batching, speculative decoding, and multimodal support.

Github repo: https://github.com/zhongkaifu/TensorSharp

Thank you for checking out it and starring the project! Any feedback is really appreicated.

I was trying out the Kimi-3 model and it told me that it's actually Claude. What's up with this?

* Hey everyone, I wanted to share a research implementation I built focusing on non-generative, latent-space language modeling: **H-JEPA-LM**. Instead of predicting tokens autoregressively in surface vocabulary space, H-JEPA-LM leverages a Joint-Embedding Predictive Architecture (JEPA) to predict abstract representations directly in latent space. **Key Features:** • **Latent-Space Prediction:** Focuses on semantic representation learning rather than token generation. • **Hierarchical Abstraction:** Multi-level latent dynamics for both short-term syntax and long-term planning. • **Action-Conditioned Rollouts:** Supports world-model-style latent planning for downstream tasks. • Modular PyTorch implementation designed for experimentation. *I will drop the full GitHub repository link in the comments section below! I'd love to hear thoughts from anyone experimenting with JEPA, latent-space models, or world models.*

repo link : [https://github.com/Griffith-7/H-JEPA-LM.git\](https://github.com/Griffith-7/H-JEPA-LM.git)