Qwen2.5 Technical Report

Overview

The Qwen2.5 Technical Report details a series of large language models pre-trained on 18 trillion tokens, up from 7 trillion in prior versions, and refined through supervised fine-tuning with over 1 million samples. It documents improvements in common sense, expert knowledge, and reasoning capabilities achieved during both pre-training and post-training stages.

Best for

Best for
Researchers and developers evaluating large language model capabilities and training strategies

Use cases

• Assessing model performance and scalability for language tasks
• Comparing pre-training and post-training strategies across LLM families
• Guiding decisions on model selection for research or development projects

Notes

The Qwen2.5 Technical Report details a series of large language models pre-trained on 18 trillion tokens, up from 7 trillion in prior versions, and refined through supervised fine-tuning with over 1 million samples. It documents improvements in common sense, expert knowledge, and reasoning capabilities achieved during both pre-training and post-training stages.

Use cases

• Assessing model performance and scalability for language tasks
• Comparing pre-training and post-training strategies across LLM families
• Guiding decisions on model selection for research or development projects

Pros

• Provides extensive data on scaling from 7T to 18T tokens, showing clear improvements
• Covers both pre-training and post-training methodologies in detail
• Openly available as a community resource for benchmarking and education

Cons

• A technical report, not a deployable tool or framework
• Does not include inference benchmarks or deployment guidance
• Focuses on model architecture and training, not on practical usage or API access

Indexed from awesome-llm and enriched against its public facts.