SpecForge: Open‑Source Framework Boosts Large‑Model Speculative Sampling by 2.18×

SpecForge, an open‑source training framework built on Eagle3, enables end‑to‑end speculative sampling for ultra‑large language models, integrates tightly with the SGLang inference engine, offers online and offline training modes, supports advanced parallelism strategies, and demonstrates up to 2.18× inference speedup on benchmark tests, with all code and pretrained drafts available on GitHub and Hugging Face.

AI PerformanceInference AccelerationSpeculative Sampling

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SpecForge: Open‑Source Framework Boosts Large‑Model Speculative Sampling by 2.18×

AntTech

Apr 1, 2025 · Artificial Intelligence

AReaL‑boba: Open‑Source Reinforcement Learning Training Framework v0.2 with SOTA Performance

The Ant Research Institute and Tsinghua University's Wu Yi team released AReaL‑boba 0.2, an open‑source reinforcement‑learning training framework that dramatically speeds up large‑scale model training, achieves state‑of‑the‑art mathematical reasoning results, and provides all code, data, and scripts for reproducible research.

AITraining Frameworklarge models

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AReaL‑boba: Open‑Source Reinforcement Learning Training Framework v0.2 with SOTA Performance

Baobao Algorithm Notes

Feb 12, 2025 · Artificial Intelligence

How X‑R1 Triggers Aha Moments in Low‑Cost RL Training of 0.5B LLMs

The X‑R1 open‑source framework demonstrates that a 0.5B language model can achieve rapid reasoning improvements and observable "Aha Moments" using reinforcement learning on a modest 4‑GPU setup, detailing its design, performance metrics, installation steps, and future roadmap.

AILLMTraining Framework

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How X‑R1 Triggers Aha Moments in Low‑Cost RL Training of 0.5B LLMs

NewBeeNLP

Sep 25, 2024 · Artificial Intelligence

From Zero to One: A Practical Guide to Pretraining Large Language Models

This comprehensive guide walks through every stage of LLM pretraining—from data sourcing, cleaning, and deduplication, to tokenizer design, model architecture choices, training framework selection, optimization tricks, and evaluation methods—offering actionable tips and pitfalls to avoid.

LLM PretrainingTraining Frameworkdata collection

0 likes · 32 min read

From Zero to One: A Practical Guide to Pretraining Large Language Models

Baobao Algorithm Notes

Sep 24, 2024 · Artificial Intelligence

From Zero to One: A Practical Guide to Pretraining Large Language Models

This comprehensive guide walks you through every stage of LLM pretraining—from data sourcing, cleaning, and deduplication to tokenizer design, model architecture choices, training framework selection, optimization tricks, and evaluation methods—highlighting common pitfalls and practical solutions for building robust models.

LLM PretrainingTokenizerTraining Framework

0 likes · 34 min read