gemini 2.0 flash was peak, went downhill after that.
alkinun
AtAndDev
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decentralize
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repliedto Banaxi-Tech's post about 11 hours ago
BananaMind is in the google situation before argon right now, right? reacted to mithulaartigala's post with 🔥 about 21 hours ago
We need to make software where people can start using decision models.
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replied to Banaxi-Tech's post about 11 hours ago
reacted to mithulaartigala's post with 🔥 about 21 hours ago
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We need to make software where people can start using decision models.
reacted to appvoid's post with 🔥 about 22 hours ago
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You know, I can't believe we are actually doing it, literally advancing the frontier at the edge! We've come a long way since the first models on open slm leaderboard, and, as many things that change, performance is one of the main things that has exploded.
7 years ago, the best we had was GPT-2. Today, we got 90m models beating smollm2! I can't barely see what a future with increasingly powerful small models will look like.
All I can say is that the best is yet to come. And will be agentic.
7 years ago, the best we had was GPT-2. Today, we got 90m models beating smollm2! I can't barely see what a future with increasingly powerful small models will look like.
All I can say is that the best is yet to come. And will be agentic.
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replied to Banaxi-Tech's post about 22 hours ago
im talking about gemini, gemma always cooks. gemini is shit always.
replied to Banaxi-Tech's post about 23 hours ago
google always has been bad, you arent :)
make a comeback!
reacted to Banaxi-Tech's post with 😔 about 23 hours ago
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BananaMind is in the google situation before argon right now, right?
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Text Generation • 501B • Updated • 5.96k • • 262
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reacted to jiteshkprasad's post with 🔥 4 days ago
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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.
Apache-2.0 ·
weights: 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.
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_0Apache-2.0 ·
weights: 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.
- 21 replies