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Qwen is Alibaba’s family of large language models. The “7B” in Qwen-7B refers to the model’s size; the “2.4T” in its 2023 release table refers to the number of pretrained tokens, not parameters. From its first open-weight releases in 2023, the family expanded into different sizes and specialist coding and mathematics models.
What Qwen is—and what “7B to 2.4T” means
Qwen is a model family developed by Alibaba’s Qwen team. Alibaba says its first open-weight Qwen and Qwen Chat models were published in August 2023. In the original Qwen release table, “Qwen-7B” names a model-size class, while “2.4T” appears in a separate column for the number of pretrained tokens.
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Those figures describe different things: parameters are learned values in a model, while training tokens are pieces of data processed during training. The 2.4T figure is not evidence of a 2.4-trillion-parameter Qwen model. The early release table also listed Qwen-14B and Qwen-72B as trained on 3.0T tokens.
How the Qwen family developed
2023: the first public models
The Qwen team’s original announcement dates Qwen-7B to August 3, 2023, Qwen-14B to September 25, and Qwen-1.8B and Qwen-72B to November 30. The announcement described the family as multilingual, with particular strengths in English and Chinese, and highlighted function calling, a code interpreter and an agent framework. These were the team’s descriptions of its models and tools, not independent evaluation results.
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June 2024: Qwen2 adds sizes and a mixture-of-experts model
On June 7, 2024, the Qwen team announced Qwen2 pretrained and instruction-tuned models at five nominal sizes: 0.5B, 1.5B, 7B, 57B-A14B and 72B. Its table gives the corresponding parameter counts as 0.49B, 1.54B, 7.07B, 57.41B and 72.71B.
The 57B-A14B designation identifies a mixture-of-experts model: 57.41B is its total parameter count, while A14B refers to the activated parameters. Those numbers are not directly interchangeable with the parameter count of a dense model. The team said Qwen2 models were trained on 27 additional languages beyond English and Chinese, and that all sizes used Group Query Attention.
The Qwen2 announcement reported context support up to 128K tokens for Qwen2-7B-Instruct and Qwen2-72B-Instruct. It also described different licenses across named models: the 0.5B, 1.5B, 7B and 57B-A14B models moved to Apache 2.0, while Qwen2-72B retained the Qianwen License. Check the specific model repository for applicable terms before reuse; a family-wide license assumption is unsafe.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11September 2024: Qwen2.5 branches into specialist models
Qwen2.5 expanded the family beyond general language models with dedicated Qwen2.5-Coder and Qwen2.5-Math lines. The Qwen team said the Coder line was trained on 5.5 trillion code-related tokens. For Math, it described methods including chain-of-thought, program-of-thought and tool-integrated reasoning. These are descriptions from the model team rather than a guarantee that every variant will perform equally well on a particular task.
The Qwen2.5 announcement also discussed Qwen2.5-72B as a 72B-parameter dense decoder-only language model and reported comparisons against other models. Such results should be read as the team’s benchmark claims for the specified model and setup, not as timeless rankings across all tasks. The announcement also described hosted API offerings, including Qwen-Plus and Qwen-Turbo through Model Studio.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What a 7B label does—and does not—tell you
A size label is only one specification. The Qwen2.5-7B-Instruct model card lists 7.61B total parameters and 6.53B non-embedding parameters, alongside 28 layers. Its listed full context capability is 131,072 tokens, with generation of up to 8,192 tokens. The card separately says the current configuration is set to 32,768 tokens and describes YaRN scaling for longer inputs. In other words, a model’s stated context capability and the length enabled by a particular configuration may differ.
The card recommends deployment with vLLM, but a parameter count or context figure alone cannot determine whether a model will run well on a given machine. Memory use, speed and practical limits also depend on precision, runtime and deployment setup. The original release page’s historical memory estimates should not be treated as current hardware recommendations.
Quick Recap
How to compare Qwen variants
- Check the model design. Compare total and activated parameters separately for mixture-of-experts models; a 57B-A14B model does not use all 57B parameters for every token.
- Match the specialization to the task. Identify the exact general, coder, math, vision or other variant rather than assuming all models under the Qwen name have the same capabilities.
- Read context and output limits together. Distinguish advertised or full context from the repository’s configured default, any extension method required, and the generation limit.
- Verify distribution and license. Weight availability and terms can differ by repository and variant. Confirm the exact model’s current license before adapting or redistributing it.
- Choose the deployment route. Open weights can be run through a compatible local or hosted setup; APIs such as those described by Alibaba offer a hosted route. The trade-offs depend on the provider, runtime, hardware and workload.
- Interpret benchmark claims narrowly. Tie any reported result to its model, benchmark and test configuration. A vendor’s comparison is not a universal ranking.
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