poolside-laguna-hackathon

laguna-xs2-dense-stage1

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python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained("poolside-laguna-hackathon/laguna-xs2-dense-stage1",
        trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="cuda")
tok = AutoTokenizer.from_pretrained("poolside-laguna-hackathon/laguna-xs2-dense-stage1", trust_remote_code=True)

Dense FFNs (intermediate 4608) are zero-padded to 8192 so the stock modeling_laguna.py loads it (numerically identical); exported reports ≈3.8B, true model ≈3.0B. last.pt (raw Stage-1 FFN weights) is also in this repo. Footprint: ≈6 GB bf16 vs ≈67 GB for the 33B MoE (≈11× less weight VRAM).

See the Stage-2 card for the full method, results, and next steps. Code: https://github.com/postscarcity-inc/laguna-xs.2-dense

Model provider

poolside-laguna-hackathon

Model tree

Base

poolside/Laguna-XS.2

Fine-tuned

this model

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