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jaithree
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reacted to jiteshkprasad's post with 🔥 1 day ago
Laghu: a 152M chat model trained from scratch for $146 of pretraining compute, released fully open. It thinks before it answers (on/off), holds multi-turn conversations and chats, in 163 MB that runs in your browser. - Base model, new text (Sep-Oct 2026): 0.919 bits per byte vs SmolLM2-135M 0.941 and Falcon-H1-Tiny-90M-Base 0.979 - Chat: level with SmolLM2-135M-Instruct on chat quality and multi-turn, ahead on maths with thinking; behind Falcon-H1-Tiny-90M-Instruct on all three - Thinking on/off, 16K context; runs in the browser, Ollama (`ollama run hf.co/udgamlabs/Laghu-GGUF:Q8_0`), LM Studio, llama.cpp, MLX and transformers - Every surviving checkpoint with optimiser states, code, recipe, per-item evals, failures and cost How we made it: https://huggingface.co/blog/udgamlabs/laghu Collection: https://huggingface.co/collections/udgamlabs/laghu-6ad91b153ba8cdc4e40900bd Space: https://huggingface.co/spaces/udgamlabs/Laghu
liked a model 1 day ago
udgamlabs/Laghu-MLX
reacted to jiteshkprasad's post with 🔥 1 day ago
Laghu: a 152M chat model trained from scratch for $146 of pretraining compute, released fully open. It thinks before it answers (on/off), holds multi-turn conversations and chats, in 163 MB that runs in your browser. - Base model, new text (Sep-Oct 2026): 0.919 bits per byte vs SmolLM2-135M 0.941 and Falcon-H1-Tiny-90M-Base 0.979 - Chat: level with SmolLM2-135M-Instruct on chat quality and multi-turn, ahead on maths with thinking; behind Falcon-H1-Tiny-90M-Instruct on all three - Thinking on/off, 16K context; runs in the browser, Ollama (`ollama run hf.co/udgamlabs/Laghu-GGUF:Q8_0`), LM Studio, llama.cpp, MLX and transformers - Every surviving checkpoint with optimiser states, code, recipe, per-item evals, failures and cost How we made it: https://huggingface.co/blog/udgamlabs/laghu Collection: https://huggingface.co/collections/udgamlabs/laghu-6ad91b153ba8cdc4e40900bd Space: https://huggingface.co/spaces/udgamlabs/Laghu
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