# Qwen1.5

Qwen1.5 is a family of large language models developed by Alibaba Cloud, released in early 2024, available in multiple parameter sizes and variants, including open-source and proprietary versions.

Qwen1.5 is a family of large language models developed by [Alibaba Cloud](https://www.wikiprompt.org/wiki/alibaba-cloud), released in early 2024. The family includes multiple parameter sizes ranging from 0.5 billion to 72 billion parameters, with both base and instruction-tuned variants. Qwen1.5 models are designed for a variety of natural language processing tasks, including text generation, translation, and question answering, and have appeared on public LLM and media leaderboards, with seven variants captured in benchmark snapshots.

The models are built on the [transformer](https://www.wikiprompt.org/wiki/transformer) architecture, a type of [neural-network](https://www.wikiprompt.org/wiki/neural-network) that has become standard in [large-language-model](https://www.wikiprompt.org/wiki/large-language-model) development. They are trained using [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) techniques, leveraging large-scale datasets and computational resources. Qwen1.5 is part of the broader [generative-ai](https://www.wikiprompt.org/wiki/generative-ai) trend, where models generate human-like text based on input prompts.

## Model Variants and Sizes

Qwen1.5 includes several parameter configurations: 0.5B, 1.8B, 4B, 7B, 14B, 32B, and 72B. Each size is available in base (pretrained) and chat (instruction-tuned) versions. The 72B variant is the largest and typically demonstrates higher performance on complex tasks, while smaller variants are optimized for efficiency and deployment on resource-constrained devices. The models are released under an open-source license, allowing researchers and developers to use and modify them, with some restrictions for commercial use in larger-scale applications.

## Training and Architecture

Qwen1.5 models employ a standard decoder-only transformer architecture, similar to other modern LLMs. They use [multi-head-attention](https://www.wikiprompt.org/wiki/multi-head-attention) mechanisms and [positional-encoding](https://www.wikiprompt.org/wiki/positional-encoding) to process sequential data. Training involves [adam-optimizer](https://www.wikiprompt.org/wiki/adam-optimizer) and [learning-rate-schedule](https://www.wikiprompt.org/wiki/learning-rate-schedule) techniques, with [gradient-clipping](https://www.wikiprompt.org/wiki/gradient-clipping) to stabilize training. The models are trained on a diverse corpus of text data, though specific details about the training dataset are not fully public. The instruction-tuned variants are fine-tuned using techniques like [rlaif](https://www.wikiprompt.org/wiki/rlaif) (reinforcement learning from AI feedback) and supervised fine-tuning to align with user intent.

## Performance and Benchmarks

Qwen1.5 models have been evaluated on various benchmarks, including language understanding, reasoning, and coding tasks. They have appeared on public leaderboards, such as the Open LLM Leaderboard, where they have ranked competitively among other open-source models. The 72B variant, in particular, has shown strong performance, often comparable to larger proprietary models. However, exact benchmark scores vary by task and are subject to change as new evaluations are conducted.

## Availability and Deployment

Qwen1.5 is available through multiple channels. The open-source versions can be downloaded from model repositories like Hugging Face, and they are integrated into [alibaba-cloud](https://www.wikiprompt.org/wiki/alibaba-cloud)'s services, including the DashScope API. The models can be deployed on various platforms, including [amazon-web-services](https://www.wikiprompt.org/wiki/amazon-web-services), [azure](https://www.wikiprompt.org/wiki/azure), and [google-cloud](https://www.wikiprompt.org/wiki/google-cloud), as well as on specialized hardware like [aws-trainium](https://www.wikiprompt.org/wiki/aws-trainium) and [groq](https://www.wikiprompt.org/wiki/groq) for inference acceleration. This flexibility makes Qwen1.5 accessible for both research and production use.

## Reception and Impact

Qwen1.5 has been well-received in the AI community for its performance-to-size ratio, particularly the smaller variants that offer competitive results with lower computational requirements. It has contributed to the proliferation of open-source LLMs, enabling broader access to advanced AI capabilities. The model family has also been used in academic research and industry applications, further cementing its role in the evolving landscape of [artificial-intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence).

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Source: https://www.wikiprompt.org/wiki/qwen1-5
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-13T18:57:00.458781+00:00