stefan-it/d-info-2005-names
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Stefan Schweter PRO
stefan-it
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 about 15 hours ago
Cloudflare/clef-flash reacted to jiteshkprasad's post with 🔥 about 19 hours 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. upvoted a paper about 23 hours ago
PACE: Periodic and Channel-Aligned Encoding of Four-Bit Recurrent StatesOrganizations
