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🔄 In a Training Loop

Stefan Schweter PRO

stefan-it
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Asford's profile picture lapsdoor's profile picture kuldeepjeengar's profile picture
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https://schweter.bayern

AI & ML interests

Flair + Flukes Library 💕, NER & PoS Tagging, LM Pretraining (mostly encoder-only & encoder-decoder), Historical Language Models, German Language Models, Bavarian NLP 🥨, xLSTM

Recent Activity

liked a model 2 days ago
fastino/GLiDE
liked a model 2 days ago
Cloudflare/clef-flash
reacted to jiteshkprasad's post with 🔥 3 days ago
Last week we released Gargi, a 384M English language model trained from scratch on about 17B tokens, roughly 1,000× less training data than Qwen2.5-0.5B (~18T tokens, as published by its authors). What we measured (same harness for every model: lm-evaluation-harness, 0-shot, greedy, each model's start token): - Base model vs Qwen2.5-0.5B: ARC-Easy 62.8 vs 64.7, ARC-Challenge 32.3 vs 32.4, WinoGrande 54.6 vs 56.1. - Chat model: thinking on or off; GSM8K 36.0 with thinking on. - Long inputs: needle-in-a-haystack recall 100% at 8K, 93% at 16K and 90% at 20K tokens, so use it up to about 16K. Where it falls short: knowledge and everyday common sense still scale with data. MMLU is 25.5 vs 47.3, and it trails on HellaSwag and PIQA. It is not a maths specialist, and it is not safety-tuned: put a moderation layer in front of any public use. It runs on a laptop: GGUF (Q8_0 at 410 MB, Q4_K_M, f16) and MLX builds. `ollama run hf.co/udgamlabs/Gargi-GGUF:Q8_0` Apache-2.0 · weights: https://huggingface.co/udgamlabs/Gargi try it: https://udgamlabs.com/models/gargi code: https://github.com/udgamlabs/gargi Named after Gargi Vachaknavi, the Vedic philosopher remembered for asking the hardest questions.
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