deepseek-ai/DeepSeek-OCR
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DJ Sri Vigneshwar PRO
Sri-Vigneshwar-DJ
AI & ML interests
Co-Founder, CTO @Hawky.ai - Creative Intelligence for Performance Marketing
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20 days ago
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I found it excellent and very well done.
One of the best explanations of embedding I've ever read. Well done, @hesamation !
Had to share this: hesamation/primer-llm-embedding
One of the best explanations of embedding I've ever read. Well done, @hesamation !
Had to share this: hesamation/primer-llm-embedding
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Finally, I uploaded the model I developed for my master’s thesis! Given a financial event, it provides explained predictions based on a dataset of past news and central bank speeches.
Try it out here:
SelmaNajih001/StockPredictionExplanation
(Just restart the space and wait a minute)
The dataset used for RAG can be found here:
SelmaNajih001/FinancialNewsAndCentralBanksSpeeches-Summary-Rag
While the dataset used for the training is:
SelmaNajih001/FinancialClassification
I also wrote an article to explain how I've done the training. You can find it here https://huggingface.co/blog/SelmaNajih001/explainable-financial-predictions
Try it out here:
SelmaNajih001/StockPredictionExplanation
(Just restart the space and wait a minute)
The dataset used for RAG can be found here:
SelmaNajih001/FinancialNewsAndCentralBanksSpeeches-Summary-Rag
While the dataset used for the training is:
SelmaNajih001/FinancialClassification
I also wrote an article to explain how I've done the training. You can find it here https://huggingface.co/blog/SelmaNajih001/explainable-financial-predictions
- 2 replies
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20 days ago
Selma. Fine-tuning is a great way to start, but for enterprise use cases the real leverage comes after we “crack” the problem. Once the business workflow and KPIs are clear... a domain‑specific pretrained model (say 3B–7B) is viable and impactful. Budget‑wise, pretraining in that range can be around ~$400k (very very approximate estyimation), but the point is after validating the use case and TAM, a domain‑specific pretrained model becomes a big multiplier for retrieval, reasoning, and integration across the stack, far beyond what incremental fine‑tuning alone can deliver... I'm think find enterprise usecase and train (like B2B SaaS)
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20 days ago
I think domain-specific use cases are many... in every vertical there are more nuances as we try to crack deeper-level problems. I believe it’s worth investing time in experimentation to start with, then identify the TAM for that, and decide whether to train further or not. What do you think about this???
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Have built the new Image Studio with the Gemini Image Gen models for the following multiple tasks:
⭐ Gemini-Image-Studio: prithivMLmods/Gemini-Image-Studio (Latest)
🤞 Old-App: prithivMLmods/Nano-Banana-AIO
🥊 GitHub: https://github.com/prithivsakthiur/gemini-image-studio-hf
To proceed, you need to add your Gemini API key. Your API key is stored only for the duration of your session and will be lost when you reload or exit the page. It will not be shared or exposed anywhere.
imagen-4.0-fast-generate-001 model for Image Generation (Text-to-Image) and Multi-Image Editing (Image-to-Image), and Draw-to-Image powered by gemini-2.5-flash-image (aka Nano Banana).⭐ Gemini-Image-Studio: prithivMLmods/Gemini-Image-Studio (Latest)
🤞 Old-App: prithivMLmods/Nano-Banana-AIO
🥊 GitHub: https://github.com/prithivsakthiur/gemini-image-studio-hf
To proceed, you need to add your Gemini API key. Your API key is stored only for the duration of your session and will be lost when you reload or exit the page. It will not be shared or exposed anywhere.
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Do you think domain-specific embedding fine-tuners are needed?
I've been working with embeddings for marketing use cases and noticed something: most embeddings don't get marketing concepts very well. They're trained in general-purpose ways.
The Issue I'm Seeing
When I search marketing content with general embeddings:
"organic growth" returns farming articles
"conversion funnel" matches industrial equipment
"brand lift" doesn't connect to campaign effectiveness
Marketing jargon like CAC, ROAS, CTR aren't properly understood
My Question
Do you think domain-specific embeddings are needed for marketing?
Some thoughts:
Marketing has its own vocabulary and concept relationships
General models trained on Wikipedia/web crawl miss these nuances
But is fine-tuning worth the effort vs just using more retrieval tricks?
Quick Example
I fine-tuned all-mpnet-base-v2 on ~1000 marketing concept pairs and saw 15-20% better retrieval accuracy. But I'm curious:
Has anyone else tried this for marketing or other domains?
When do you think domain-specific embeddings are actually necessary vs overkill?
Are there better approaches I'm missing?
https://huggingface.co/blog/Sri-Vigneshwar-DJ/why-your-marketing-rag-system-needs-domain-specifi
I've been working with embeddings for marketing use cases and noticed something: most embeddings don't get marketing concepts very well. They're trained in general-purpose ways.
The Issue I'm Seeing
When I search marketing content with general embeddings:
"organic growth" returns farming articles
"conversion funnel" matches industrial equipment
"brand lift" doesn't connect to campaign effectiveness
Marketing jargon like CAC, ROAS, CTR aren't properly understood
My Question
Do you think domain-specific embeddings are needed for marketing?
Some thoughts:
Marketing has its own vocabulary and concept relationships
General models trained on Wikipedia/web crawl miss these nuances
But is fine-tuning worth the effort vs just using more retrieval tricks?
Quick Example
I fine-tuned all-mpnet-base-v2 on ~1000 marketing concept pairs and saw 15-20% better retrieval accuracy. But I'm curious:
Has anyone else tried this for marketing or other domains?
When do you think domain-specific embeddings are actually necessary vs overkill?
Are there better approaches I'm missing?
https://huggingface.co/blog/Sri-Vigneshwar-DJ/why-your-marketing-rag-system-needs-domain-specifi
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- 6 replies
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22 days ago
Article
Why Your Marketing RAG System Needs Domain-Specific Embeddings 🎯
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22 days ago
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How to run a regression using Hugging Face — An example with financial news to predict stock returns
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