Jean Louis

JLouisBiz

https://www.StartYourOwnGoldMine.com

AI & ML interests

- LLM for sales, marketing, promotion - LLM for Website Revision System - increasing quality of communication with customers - helping clients access information faster - saving people from financial troubles

Recent Activity

replied to unmodeled-tyler's post about 5 hours ago

NEW MODEL: https://huggingface.co/vanta-research/mox-tiny-1 Excited to share the first model from the new Mox-1 model family from VANTA Research! Mox-Tiny-1 is finetuned from Llama's 3.1 8B architecture specifically for thinking partnership and high-fidelity AI interaction. Training domains were carefully selected with this goal in mind, and all datasets used were synthetically generated for the Mox-1 models specifically. Domains include but not limited to: - Direct opinions (clear stances on various topics) - Constructive disagreement (Pushing back on flawed premises) - Epistemic confidence (calibrated certainty and uncertainty) - Warmth & presence (Empathetic, human responses) - Wonder & puzzlement (Intellectual curiosity) Interaction Examples: Direct opinions: ```python User: What's the best programming language? Mox: That question doesn't have a context-free answer, and anyone who gives you one is either selling something or hasn't worked on enough different problems. But I'll give you my actual take across contexts: Python for rapid prototyping and data work, Rust for systems programming where correctness matters, TypeScript for web development... ``` Warmth & Humor: ```python User: I've been debugging this issue for six hours. Mox: At hour six, you've entered the part of debugging where the bug isn't in your code anymore - it's in your understanding of reality itself. The semicolon starts looking suspicious. Show me what you're dealing with. Fresh eyes help, and mine haven't been staring at it since lunch. ``` Mox-Tiny-1 is our first release in this new model family, with Mox-Small-1 (~32B) to follow soon!

replied to sagar007's post about 5 hours ago

🚀 I built a Multimodal Vision-Language Model from scratch using Gemma-270M + CLIP! Just finished training my multimodal model on the full LLaVA-Instruct-150K dataset (157K samples) and wanted to share the results! 🔧 What I Built: A vision-language model that can understand images and answer questions about them, combining: - Google Gemma-3-270M (language) - OpenAI CLIP ViT-Large/14 (vision) - LoRA fine-tuning for efficiency 📊 Training Stats: - 157,712 training samples (full LLaVA dataset) - 3 epochs on A100 40GB - ~9 hours training time - Final loss: 1.333 training / 1.430 validation - Only 18.6M trainable params (3.4% of 539M total) 📈 https://huggingface.co/sagar007/multigemma Benchmark Results: - VQA Accuracy: 53.8% - Works great for: animal detection, room identification, scene understanding 🔗 **Try it yourself:** - 🤗 Model: https://huggingface.co/sagar007/multigemma - 🎮 Demo: https://huggingface.co/spaces/sagar007/Multimodal-Gemma - 💻 GitHub: https://github.com/sagar431/multimodal-gemma-270m Built with PyTorch Lightning + MLflow for experiment tracking. Full MLOps pipeline with CI/CD! Would love to hear your feedback! 🙏 #multimodal #gemma #clip #llava #vision-language #pytorch

reacted to MonsterMMORPG's post with 🤗 about 10 hours ago

Compared Quality and Speed Difference (with CUDA 13 & Sage Attention) of BF16 vs GGUF Q8 vs FP8 Scaled vs NVFP4 for Z Image Turbo, FLUX Dev, FLUX SRPO, FLUX Kontext, FLUX 2 - Full 4K step by step tutorial also published Full 4K tutorial : https://youtu.be/XDzspWgnzxI Check above full 4K tutorial to learn more and see uncompressed original quality and size images It was always wondered how much quality and speed difference exists between BF16, GGUF, FP8 Scaled and NVFP4 precisions. In this tutorial I have compared all these precision and quantization variants for both speed and quality. The results are pretty surprising. Moreover, we have developed and published NVFP4 model quant generator app and FP8 Scaled quant generator apps. The links of the apps are below if you want to use them. Furthermore, upgrading ComfyUI to CUDA 13 with properly compiled libraries is now very much recommended. We have observed some noticeable performance gains with CUDA 13. So for both SwarmUI and ComfyUI solo users, CUDA 13 ComfyUI is now recommended.

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I’ve built two Firefox extensions for my personal workflow:

1. **Quick Edit in Emacs**
I manage over 3,500 web pages locally. With this extension, I can now click anywhere on a webpage and instantly jump into Emacs to edit the exact page (or annotate any other page I'm working on).

2. **Describe Images (and soon Videos) on the Web**
Using the right-click menu, I can generate descriptions for images I come across online. These descriptions are stored and reused for my own image collections or web pages. I’m planning to add the same functionality for videos soon.

What makes this possible is running LLMs locally on my own machine — I’ve been experimenting with models like **Mistral Vibe** and others. This lets me automate description generation and text processing entirely offline, keeping everything fast, private, and fully under my control.

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