AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Paper • 2503.05731 • Published • 3
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#1 opened 2 months ago
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stephenb1334
chenhegu
authored 3
papers 7 months ago
multimodalart
posted an update 9 months ago
Post
33121
Want to iterate on a Hugging Face Space with an LLM?
Now you can easily convert any HF entire repo (Model, Dataset or Space) to a text file and feed it to a language model!
multimodalart/repo2txt
Now you can easily convert any HF entire repo (Model, Dataset or Space) to a text file and feed it to a language model!
multimodalart/repo2txt
- 3 replies
ehristoforu
posted an update 11 months ago
Post
2696
🚀Hello from the Project Fluently team!
✨ We are happy to share with you our new universal LLM models based on Qwen3 1.7B and 4B — powerful, multilingual and ready to solve a wide range of problems!
🛠️ We have conducted additional training and carefully merged them to achieve even better results and maximize the potential of the models.
🆓 And most importantly — the models are completely open and free under the Apache-2.0 license!
🔗 Links to repositories:
- FluentlyQwen3-4B: fluently/FluentlyQwen3-4B
- FluentlyQwen3-1.7B: fluently/FluentlyQwen3-1.7B
😍 We will be very glad to hear your feedback and impressions! Your opinion is very important to us!
✨ We are happy to share with you our new universal LLM models based on Qwen3 1.7B and 4B — powerful, multilingual and ready to solve a wide range of problems!
🛠️ We have conducted additional training and carefully merged them to achieve even better results and maximize the potential of the models.
🆓 And most importantly — the models are completely open and free under the Apache-2.0 license!
🔗 Links to repositories:
- FluentlyQwen3-4B: fluently/FluentlyQwen3-4B
- FluentlyQwen3-1.7B: fluently/FluentlyQwen3-1.7B
😍 We will be very glad to hear your feedback and impressions! Your opinion is very important to us!
Post
4358
Hey all
Finally it's happening. DeepGit lite is back now, running on cpu only devices. Just smartly search across Github and spin up conversational agents in the background and have grounded conversation with repositories
Try it out now!!!! zamal/DeepGit
Finally it's happening. DeepGit lite is back now, running on cpu only devices. Just smartly search across Github and spin up conversational agents in the background and have grounded conversation with repositories
Try it out now!!!! zamal/DeepGit
- 1 reply
multimodalart
posted an update about 1 year ago
Post
18513
Self-Forcing - a real-time video distilled model from Wan 2.1 by @adobe is out, and they open sourced it 🐐
I've built a live real time demo on Spaces 📹💨
multimodalart/self-forcing
I've built a live real time demo on Spaces 📹💨
multimodalart/self-forcing
- 6 replies
Post
1671
Say hallo to GermaNER 💪– a lightweight, high-accuracy NER model for German texts, powered by XLM-RoBERTa + LoRA adapters!
⚡ Fast, efficient, and open-source – perfect for tagging names, places & orgs in real-world German data.
Try it now on Hugging Face 👉 fau/GermaNER
⚡ Fast, efficient, and open-source – perfect for tagging names, places & orgs in real-world German data.
Try it now on Hugging Face 👉 fau/GermaNER
Post
4452
🚀 Videoxity is live on Hugging Face! 🎞️
A powerful, modular toolkit for intelligent video manipulation and scene editing.
With Videoxity, you can:
🖼️ Auto-caption keyframes with BLIP
🧠 Filter scenes using natural language (e.g. “remove dog scenes”)
✂️ Seamlessly trim videos with FFmpeg
📊 Generate frame-based summaries
Powered by Groq LLM + LangChain, OpenCV, BLIP, and SentenceTransformers, Videoxity bridges vision and language to give developers full control over video content.
🔧 Built for developers. Feedback welcome!
👉 Try it out here fau/videoxity
A powerful, modular toolkit for intelligent video manipulation and scene editing.
With Videoxity, you can:
🖼️ Auto-caption keyframes with BLIP
🧠 Filter scenes using natural language (e.g. “remove dog scenes”)
✂️ Seamlessly trim videos with FFmpeg
📊 Generate frame-based summaries
Powered by Groq LLM + LangChain, OpenCV, BLIP, and SentenceTransformers, Videoxity bridges vision and language to give developers full control over video content.
🔧 Built for developers. Feedback welcome!
👉 Try it out here fau/videoxity
asa1108
published a
model about 1 year ago
asa1108
updated a
model about 1 year ago
Post
2003
🚀 DeepGit Lite is live! 🔍✨
Hey folks!
Just launched DeepGit Lite — a lighter version of DeepGit with fewer components under the hood.
It won’t perform quite like the full powerhouse, but it’s great for a quick peek and first-hand feel! ⚙️👀
Give it a spin and tell us what you think!
👉 Try it here https://huggingface.co/spaces/zamal/DeepGit-lite
#opensource #DeepGit #gradio #githubresearch
Hey folks!
Just launched DeepGit Lite — a lighter version of DeepGit with fewer components under the hood.
It won’t perform quite like the full powerhouse, but it’s great for a quick peek and first-hand feel! ⚙️👀
Give it a spin and tell us what you think!
👉 Try it here https://huggingface.co/spaces/zamal/DeepGit-lite
#opensource #DeepGit #gradio #githubresearch
- 3 replies
Post
2627
DeepGit: Your GitHub Gold Digger! 💰🚀
Hey Hugging Face gang! Meet DeepGit—my open-source sidekick that rips through GitHub to snag repos that fit you. Done with dead-end searches? Me too. Built it with LangGraph and some dope tricks:
Embeddings grab the good stuff (HF magic, baby!)
Re-ranking nails the best picks
Snoops docs, code, and buzz in one slick flow
Drops a clean list of hidden gems 💎
Unearth that sneaky ML lib or Python gem—run python app.py or langgraph dev and boom! Peek it at https://github.com/zamalali/DeepGit. Fork it, tweak it, love it—Docker’s in, HF vibes are strong. Drop a 🌟 or a crazy idea—I’m pumped to jam with you all! 🪂
Hey Hugging Face gang! Meet DeepGit—my open-source sidekick that rips through GitHub to snag repos that fit you. Done with dead-end searches? Me too. Built it with LangGraph and some dope tricks:
Embeddings grab the good stuff (HF magic, baby!)
Re-ranking nails the best picks
Snoops docs, code, and buzz in one slick flow
Drops a clean list of hidden gems 💎
Unearth that sneaky ML lib or Python gem—run python app.py or langgraph dev and boom! Peek it at https://github.com/zamalali/DeepGit. Fork it, tweak it, love it—Docker’s in, HF vibes are strong. Drop a 🌟 or a crazy idea—I’m pumped to jam with you all! 🪂
Post
2057
🚀 ftBoost is LIVE – Stop Struggling with Fine-Tuning Data!
Alright folks, if you’re tired of manually crafting fine-tuning datasets, ftBoost is here to do the heavy lifting. One-click, LangChain-Groq-powered data augmentation that scales your training data in OpenAI, Gemini, Mistral, and LLaMA formats—automatically.
🔥 What’s inside?
✅ Smart Augmentations – Paraphrasing, back translation, synonym swapping & synthetic noise.
✅ No more JSONL headaches – Auto-formats everything for OpenAI, Gemini, Mistral & LLaMA.
✅ Custom tuning – Adjust similarity, diversity, and fluency in real-time.
✅ Upload, generate, download – That’s it.
⚡ If you’re fine-tuning LLMs, this will save you hours.
🚀 Try it now: 👉 zamal/Finetune-Boost
🌟 Give us a star on GitHub!
Let me know what you think & how it boosts your workflow! 🔥
Alright folks, if you’re tired of manually crafting fine-tuning datasets, ftBoost is here to do the heavy lifting. One-click, LangChain-Groq-powered data augmentation that scales your training data in OpenAI, Gemini, Mistral, and LLaMA formats—automatically.
🔥 What’s inside?
✅ Smart Augmentations – Paraphrasing, back translation, synonym swapping & synthetic noise.
✅ No more JSONL headaches – Auto-formats everything for OpenAI, Gemini, Mistral & LLaMA.
✅ Custom tuning – Adjust similarity, diversity, and fluency in real-time.
✅ Upload, generate, download – That’s it.
⚡ If you’re fine-tuning LLMs, this will save you hours.
🚀 Try it now: 👉 zamal/Finetune-Boost
🌟 Give us a star on GitHub!
Let me know what you think & how it boosts your workflow! 🔥
ehristoforu
posted an update over 1 year ago
Post
4483
Introducing our first standalone model – FluentlyLM Prinum
Introducing the first standalone model from Project Fluently LM! We worked on it for several months, used different approaches and eventually found the optimal one.
General characteristics:
- Model type: Causal language models (QwenForCausalLM, LM Transformer)
- Number of parameters: 32.5B
- Number of parameters (not embedded): 31.0B
- Number of layers: 64
- Context: 131,072 tokens
- Language(s) (NLP): English, French, Spanish, Russian, Chinese, Japanese, Persian (officially supported)
- License: MIT
Creation strategy:
The basis of the strategy is shown in Pic. 2.
We used Axolotl & Unsloth for SFT-finetuning with PEFT LoRA (rank=64, alpha=64) and Mergekit for SLERP and TIES mergers.
Evolution:
🏆 12th place in the Open LLM Leaderboard ( open-llm-leaderboard/open_llm_leaderboard) (21.02.2025)
Detailed results and comparisons are presented in Pic. 3.
Links:
- Model: https://huggingface.co/fluently-lm/FluentlyLM-Prinum
- GGUF version: mradermacher/FluentlyLM-Prinum-GGUF
- Demo on ZeroGPU: ehristoforu/FluentlyLM-Prinum-demo
Introducing the first standalone model from Project Fluently LM! We worked on it for several months, used different approaches and eventually found the optimal one.
General characteristics:
- Model type: Causal language models (QwenForCausalLM, LM Transformer)
- Number of parameters: 32.5B
- Number of parameters (not embedded): 31.0B
- Number of layers: 64
- Context: 131,072 tokens
- Language(s) (NLP): English, French, Spanish, Russian, Chinese, Japanese, Persian (officially supported)
- License: MIT
Creation strategy:
The basis of the strategy is shown in Pic. 2.
We used Axolotl & Unsloth for SFT-finetuning with PEFT LoRA (rank=64, alpha=64) and Mergekit for SLERP and TIES mergers.
Evolution:
🏆 12th place in the Open LLM Leaderboard ( open-llm-leaderboard/open_llm_leaderboard) (21.02.2025)
Detailed results and comparisons are presented in Pic. 3.
Links:
- Model: https://huggingface.co/fluently-lm/FluentlyLM-Prinum
- GGUF version: mradermacher/FluentlyLM-Prinum-GGUF
- Demo on ZeroGPU: ehristoforu/FluentlyLM-Prinum-demo
- 7 replies
Post
652
🚀 Try Out RAG Demo! 🚀
A Hugging Face Space where you can compare DeepSeek-R1 vs Llama-3 using Stuff RAG (Retrieval-Augmented Generation)!
🔍 Upload a PDF, ask questions, and see how both models perform in real-time!
Try out now:
zamal/Deepseek-R1-vs-LLama3
A Hugging Face Space where you can compare DeepSeek-R1 vs Llama-3 using Stuff RAG (Retrieval-Augmented Generation)!
🔍 Upload a PDF, ask questions, and see how both models perform in real-time!
Try out now:
zamal/Deepseek-R1-vs-LLama3
- 1 reply
ameerazam08
posted an update over 1 year ago
Post
1530
zamal/Multimodal-Chat-PDF
🚀 Introducing Chat PDF Multimodal 💬
Interact with your PDF documents like never before! 🤯
Extract text & images, then ask context-aware questions based on both. Powered by RAG techniques & multimodal LLMs. Perfect for studying, research & more! 📝👀
Try it out now!!!! ✍️
#LlavaNext #MultimodalAI #Transformers
🚀 Introducing Chat PDF Multimodal 💬
Interact with your PDF documents like never before! 🤯
Extract text & images, then ask context-aware questions based on both. Powered by RAG techniques & multimodal LLMs. Perfect for studying, research & more! 📝👀
Try it out now!!!! ✍️
#LlavaNext #MultimodalAI #Transformers
ehristoforu
posted an update over 1 year ago
Post
4681
✒️ Ultraset - all-in-one dataset for SFT training in Alpaca format.
fluently-sets/ultraset
❓ Ultraset is a comprehensive dataset for training Large Language Models (LLMs) using the SFT (instruction-based Fine-Tuning) method. This dataset consists of over 785 thousand entries in eight languages, including English, Russian, French, Italian, Spanish, German, Chinese, and Korean.
🤯 Ultraset solves the problem faced by users when selecting an appropriate dataset for LLM training. It combines various types of data required to enhance the model's skills in areas such as text writing and editing, mathematics, coding, biology, medicine, finance, and multilingualism.
🤗 For effective use of the dataset, it is recommended to utilize only the "instruction," "input," and "output" columns and train the model for 1-3 epochs. The dataset does not include DPO or Instruct data, making it suitable for training various types of LLM models.
❇️ Ultraset is an excellent tool to improve your language model's skills in diverse knowledge areas.
fluently-sets/ultraset
❓ Ultraset is a comprehensive dataset for training Large Language Models (LLMs) using the SFT (instruction-based Fine-Tuning) method. This dataset consists of over 785 thousand entries in eight languages, including English, Russian, French, Italian, Spanish, German, Chinese, and Korean.
🤯 Ultraset solves the problem faced by users when selecting an appropriate dataset for LLM training. It combines various types of data required to enhance the model's skills in areas such as text writing and editing, mathematics, coding, biology, medicine, finance, and multilingualism.
🤗 For effective use of the dataset, it is recommended to utilize only the "instruction," "input," and "output" columns and train the model for 1-3 epochs. The dataset does not include DPO or Instruct data, making it suitable for training various types of LLM models.
❇️ Ultraset is an excellent tool to improve your language model's skills in diverse knowledge areas.