ontocord/pseudo_code_of_thought
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Ontocord.AI
AI & ML interests
We dedicate ourselves to bringing lawful and effective data to AI training so that everyone can benefit from human knowledge. Our focus is data-centric ML focused on performance and legal compliance, pre-training and safety for large multimodal foundation models.
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huu-ontocordÂ
updated
a
dataset
3 days ago
huu-ontocordÂ
published
a
dataset
3 days ago
huu-ontocordÂ
updated
a
dataset
6 days ago
huu-ontocordÂ
published
a
dataset
6 days ago
huu-ontocordÂ
updated
a
model
16 days ago
huu-ontocordÂ
published
a
model
16 days ago
huu-ontocordÂ
updated
a
model
16 days ago
huu-ontocordÂ
published
a
model
16 days ago
huu-ontocordÂ
updated
2
models
18 days ago
huu-ontocordÂ
published
a
model
18 days ago
Harsh1729Â
updated
a
model
20 days ago
Harsh1729Â
published
a
model
20 days ago
Taishi-N324Â
authored
3
papers
21 days ago
Balancing Speed and Stability: The Trade-offs of FP8 vs. BF16 Training in LLMs
Paper
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2411.08719
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Published
Why We Build Local Large Language Models: An Observational Analysis from 35 Japanese and Multilingual LLMs
Paper
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2412.14471
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Published
Wider or Deeper? Scaling LLM Inference-Time Compute with Adaptive Branching Tree Search
Paper
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2503.04412
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Published
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1
Harsh1729Â
updated
a
model
21 days ago
huu-ontocordÂ
authored
3
papers
25 days ago
RedPajama: an Open Dataset for Training Large Language Models
Paper
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2411.12372
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Published
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55
LLMs Lost in Translation: M-ALERT uncovers Cross-Linguistic Safety Gaps
Paper
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2412.15035
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Published
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4
Project Alexandria: Towards Freeing Scientific Knowledge from Copyright Burdens via LLMs
Paper
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2502.19413
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Published
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19