An explainable and simplified version of OLMo model
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Updated
Mar 5, 2025 - Jupyter Notebook
An explainable and simplified version of OLMo model
TMLR 2026 | Mechanistic interpretability: attention-head binding (EB*) as a marker of concept emergence. 7 models, 5 architectures (Pythia 160M–2.8B, OLMo-1B, CRFM GPT-2, SmolLM3-3B, Qwen2.5-1.5B), 41 terms.
Cross-Family Convergence of Neural Network Weight Skeletons. Companion to Zenodo paper (10.5281/zenodo.19652706).
Provide an open-source implementation of the OLMo language model for efficient and accurate natural language processing tasks.
AI工具类(Midjourney / Notion / ChatGPT) 订阅教程类(Netflix / Spotify / Adobe) 虚拟卡支付类(Namecheap / OpenAI / Google)
RDKit-Guided Topological State Machine (TSM) for constrained SMILES generation with OLMo-7B. Solves BPE-tokenizer mismatch via RDKit-in-the-loop decoding. Achieved 100% validity on allenai/OLMo-7B-hf.
Pre-training a ~150M parameter code-specialized language model using OLMo 3 architecture (GQA, SWA, SwiGLU, RoPE) on PHP/JS/Python/C source code.
First open-source descriptor-augmented LLM for Neglected Tropical Disease drug discovery | OLMo-7B + QLoRA + DeepChem | Bioactivity & Toxicity prediction for Leishmaniasis, Chagas, Malaria, TB
Mechanistic interpretability research for studying how instruction tuning restructures the computational pipeline of language models
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