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TRL

4 articles · Page 1 of 1
MaGe Linux Operations
MaGe Linux Operations
Nov 7, 2026 · Artificial Intelligence

LoRA/QLoRA Fine-Tuning on a Single GPU: Practical Guide for 7B/13B Models

This comprehensive guide walks through efficient LoRA/QLoRA fine-tuning of 7B and 13B language models on a single 24GB GPU, covering memory budgeting, environment setup, data preparation, training configuration, evaluation strategies, model merging, deployment verification, and production best practices with concrete code examples and benchmarks.

GPUHuggingFaceLLM
0 likes · 78 min read
LoRA/QLoRA Fine-Tuning on a Single GPU: Practical Guide for 7B/13B Models
Fun with Large Models
Fun with Large Models
Jun 12, 2025 · Artificial Intelligence

Implement GRPO to Give LLMs Reasoning Ability with Qwen2.5‑0.5B

This article explains the GRPO reinforcement‑learning algorithm, shows its core idea of internal group competition without a separate evaluator model, and provides a complete, step‑by‑step code walkthrough—including environment setup, dataset preparation, reward‑function design, training configuration, and evaluation—using the Qwen2.5‑0.5B‑Instruct model on the GSM8K math dataset.

GRPOGSM8KQwen2.5
0 likes · 23 min read
Implement GRPO to Give LLMs Reasoning Ability with Qwen2.5‑0.5B
Baobao Algorithm Notes
Baobao Algorithm Notes
Mar 19, 2025 · Artificial Intelligence

Why Does GRPO Loss Start at Zero and Grow During OpenR1 Training?

The article explains why the GRPO loss in OpenR1 and trl starts at zero and then rises, detailing the underlying KL‑divergence formulation, the single‑step update mechanism, and how gradients are preserved despite a zero scalar loss, with code examples from the trl implementation.

GRPOLoss InitializationOpenR1
0 likes · 5 min read
Why Does GRPO Loss Start at Zero and Grow During OpenR1 Training?