Model Training
Study the data, algorithms and infrastructure used to change model weights.
- Pretraining (2)
- Training Data (1)
- Post Training Rl (5)
- Fine Tuning (2)
- Training Infrastructure (1)
11 resources · page 1 of 1
LlamaFactory
by hiyouga (Yaowei Zheng) et al.
Compare adaptation methods within a shared training interface.
TRL - Transformers Reinforcement Learning
by Hugging Face
Find training interfaces for supervised and preference-based adaptation.
Reinforcement Learning from Human Feedback (book)
A conceptual path through reward modeling and post-training methods.
Controlling Reasoning Effort in LLMs (Ahead of AI)
A free Ahead of AI article on training controllable reasoning effort.
Unsloth
by Unsloth
Study local training and adaptation workflows for supported models.
nanochat
A compact training harness spanning the model-development lifecycle.
DeepSeek-R1: Incentivizing Reasoning via RL (arXiv 2501.12948)
by DeepSeek
Study the reported reinforcement-learning recipe behind DeepSeek-R1.
The Smol Training Playbook
A training account that includes failed experiments and engineering decisions.
The Ultra-Scale Playbook
Understand how parallelism changes memory use and training throughput.
The FineWeb Datasets
by Guilherme Penedo and coauthors, Hugging Face
Study how filtering and deduplication affect pretraining data.
verl (HybridFlow)
by verl project (originated at ByteDance Seed)
Explore how an RL training framework coordinates generation and optimization.