When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs
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louisbrulenaudet
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8 days ago
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934
🌐 Clinical Trials Dataset now available on Hugging Face! 🧬
I’ve just released a comprehensive, ML-ready dataset featuring 500,000+ clinical trial records sourced directly from ClinicalTrials.gov for biomedical NLP, healthcare analytics, and clinical research applications 🤗
I wanted to produce the most complete and up-to-date dump with all raw data partially flattened to simplify extraction, self-querying and processing.
Do you have any ideas about what we can do with it? Using descriptions to enhance specialized embedding models?
louisbrulenaudet/clinical-trials
I’ve just released a comprehensive, ML-ready dataset featuring 500,000+ clinical trial records sourced directly from ClinicalTrials.gov for biomedical NLP, healthcare analytics, and clinical research applications 🤗
I wanted to produce the most complete and up-to-date dump with all raw data partially flattened to simplify extraction, self-querying and processing.
Do you have any ideas about what we can do with it? Using descriptions to enhance specialized embedding models?
louisbrulenaudet/clinical-trials
muhammadravi251001
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11 days ago
hesamation
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15 days ago
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2438
this repo is gold! a collection of LLM apps with multi-agents, MCP, RAG and so much more.
the best way to learn is by building, and this repo provides the blueprint.
Repo: https://github.com/Shubhamsaboo/awesome-llm-apps
the best way to learn is by building, and this repo provides the blueprint.
Repo: https://github.com/Shubhamsaboo/awesome-llm-apps
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nielsr
yongzx
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yongzx
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MarziehFadaee
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ljvmiranda921
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hesamation
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I really like how this seven-stage pipeline was laid out in the Ultimate Guide to Fine-Tuning book.
It gives an overview, then goes into detail for each stage, even providing best practices.
It’s 115 pages on arxiv, definitely worth a read.
Check it out: https://arxiv.org/abs/2408.13296
It gives an overview, then goes into detail for each stage, even providing best practices.
It’s 115 pages on arxiv, definitely worth a read.
Check it out: https://arxiv.org/abs/2408.13296
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hesamation
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60+ Generative AI projects for your resume. grind this GitHub repo if you want to level up:
> LLM fine-tuning and applications
> advanced RAG apps
> Agentic AI projects
> MCP and A2A (new)
GitHub: https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/resources/60_ai_projects.md
> LLM fine-tuning and applications
> advanced RAG apps
> Agentic AI projects
> MCP and A2A (new)
GitHub: https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/resources/60_ai_projects.md
hesamation
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about 2 months ago
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3076
this book actually exists for free, “the little book of deep learning”. best to refresh your mind about DL basics:
> foundations of machine learning
> how models train
> common layers (dropout, pooling…)
> basic intro to LLMs
actually optimized for mobile.
Book: https://fleuret.org/public/lbdl.pdf
> foundations of machine learning
> how models train
> common layers (dropout, pooling…)
> basic intro to LLMs
actually optimized for mobile.
Book: https://fleuret.org/public/lbdl.pdf
marjpri
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