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The requirements depend on the model size. All Qwen models require Python 3.8+ and PyTorch 1.12+ (PyTorch 2.0 recommended) for the provided code and Transformer integration. A GPU with CUDA 11.4+ is highly recommended for faster inference (and necessary for larger models). In terms of memory: smaller models are quite lightweight, while larger ones need high-end hardware:
Qwen-1.8B: Can run on consumer GPUs – inference can be done in 8-bit or 4-bit quantized mode using <2 GB of GPU VRAM (and about 3 GB for generating ~2048 tokens). It’s feasible to run on a modern laptop or CPU-only (with reduced speed) for experimentation. Fine-tuning Qwen-1.8B (e.g. with LoRA) requires ~6 GB GPU memory.
Qwen-7B: Typically needs around 8–16 GB GPU memory. For example, generating 2048 tokens with Qwen-7B in 4-bit mode uses ~8.2 GB VRAM. Full FP16 inference might require ~16 GB. It can be loaded on a single NVIDIA 3090/4090 or A6000.
Qwen-14B: Requires roughly 14–20 GB GPU memory. Int4 inference uses ~13 GB for 2K tokens. Plan for at least a 24 GB GPU for comfortable use (e.g. a RTX 6000 Ada or dual smaller GPUs with sharding).
Qwen-72B: This model is resource-intensive. For FP16/bfloat16 inference, ~144 GB of GPU memory is needed (e.g. 2×80GB GPUs). Running it in 4-bit quantized mode still requires ≈48 GB of VRAM. Multi-GPU setups (such as 8×A100 40GB) or GPU cloud instances are recommended. Qwen-72B also uses significantly more CPU RAM for the model weights (over 140 GB), so ensure your system has sufficient memory if offloading.
In summary, GPU with high memory is recommended for Qwen-14B and above. For Qwen-7B and smaller, one consumer-grade GPU is usually enough, and for Qwen-1.8B even CPU usage is possible (albeit slow). Always install the appropriate dependencies: transformers, accelerate, einops, torch, etc., as listed in the model docs. Using optimized inference libraries like Hugging Face Accelerate or DeepSpeed can help run larger Qwen models by sharding across GPUs.

Qwen models are fully compatible with Hugging Face Transformers, making it straightforward to load them in PyTorch. The official model repositories provide example code. For instance, to load Qwen-7B or Qwen-7B-Chat:
from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-7B-Chat", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True).eval()
The trust_remote_code=True flag is required because Qwen uses some custom modeling code (for the rotary embeddings, etc.) which Transformers will fetch from the Qwen repository. Once loaded, you can use standard generation methods (model.generate or the provided model.chat interface for chat models).
Beyond plain Transformers, Qwen models can be deployed with multiple inference frameworks:
ModelScope: Alibaba provides ModelScope support – Qwen models are available on ModelScope Hub and can be loaded via ModelScope APIs.
vLLM: For optimized text generation with continuous batching, vLLM supports Qwen (the Qwen team even provides guides for using vLLM with Qwen models for high-throughput inference).
DeepSpeed & Accelerate: These can be used to shard large Qwen models across GPUs or use CPU offloading. Qwen’s documentation includes guidance on using DeepSpeed for faster inference and lower memory usage.
Quantized Runtimes: Qwen-7B and 14B have been integrated into GGML/GGUF format (e.g., via projects like qwen.cpp similar to llama.cpp). The Qwen team released a tool called qwen.cpp for running Qwen-7B on CPU with quantization. Community efforts like Ollama also package Qwen variants (e.g., ollama run qwen:7b).
In summary, you can use Qwen with any framework that supports HuggingFace Transformers models. The primary reference implementation is PyTorch-based. Make sure to use the official Hugging Face model checkpoints (or ModelScope copies) to ensure compatibility.

The licensing for Qwen models varies by model and version:
Qwen-7B, 14B, 72B (Base & Chat) – Released under the Tongyi Qianwen License Agreement. This license allows free use, distribution, and modification of the model for research or non-commercial purposes. Commercial use requires contacting Alibaba for permission or an application process. In practice, this means you can experiment and build applications with these weights freely, but if you intend to monetize or deploy commercially, you need to obtain approval from Alibaba Cloud. This license was created by Alibaba specifically for these model releases. It is somewhat similar to licenses like LLaMA’s community license, in that it restricts unauthorized commercial use. The full text is available in the model repo’s “License Agreement” section.
Qwen-1.8B (Base & Chat) – Released under a slightly different Tongyi Qianwen Research License. This is even more restrictive for commercial usage: it is intended for research and non-commercial use only, and any commercial application explicitly requires written permission or a separate agreement with Alibaba. Essentially, treat Qwen-1.8B as research-only unless you’ve made arrangements with Alibaba Cloud. For academic and hobbyist use, it is free.
Qwen-VL and Qwen-VL-Chat – Use the Tongyi Qianwen License Agreement as well (the same terms as the text models: non-commercial by default). At the time of release, Qwen-VL was open-sourced for research with the expectation that commercial uses would go through Alibaba’s approval. Always double-check the specific model card’s “License” field; for example, Hugging Face lists Qwen-VL-Chat’s license as tongyi-qianwen-license (which signals the non-commercial clause).
Qwen2.5 Series (Omni, VL-32B, Coder, etc.) – Many of these newer models are under the Apache-2.0 License. Notably, Qwen2.5-Omni-7B and Qwen2.5-Coder models are Apache-2.0, which permits commercial use out-of-the-box. Alibaba shifted to Apache license for certain 2025 releases to encourage broader adoption. Always read the model card: for example, Qwen2.5-Coder-7B is clearly marked “License: apache-2.0” on Hugging Face. If you require a Qwen model for commercial products without a special agreement, consider using those Apache-licensed versions.
In summary, for the original Qwen models (1.8B, 7B, 14B, 72B, VL), you can freely use them for research, open-source projects, or internal R&D. But if you plan to integrate them into a paid service or product, you must seek permission from Alibaba (as per the Tongyi Qianwen license). The Qwen code (repository code, not weights) is under Apache-2.0, so any scripts or libraries provided are free to use in any context. It’s the model weights that carry the special license. When in doubt, consult the official Qwen GitHub and documentation for the latest licensing FAQs – and when needed, reach out to Alibaba Cloud for clarification or to obtain a commercial license.

Qwen models were designed as general-purpose AI assistants and language models with strengths in both Chinese and English. Some common use cases:
Chatbots and Virtual Assistants: Qwen-Chat models (e.g. Qwen-7B-Chat, Qwen-14B-Chat) excel at conversational dialogue, answering questions, and following user instructions in a chat format. They can be integrated into chat applications to provide automated customer support, tutoring, or personal assistant services (with the caveat of the license for commercial deployments).
Content Generation: These models can generate coherent text, making them useful for drafting emails, writing articles, summarizing documents, translating text, or creating creative content on demand. For example, Qwen has demonstrated strong performance in summarization and translation tasks during evaluation.
Coding Assistance: Qwen models were trained on a large volume of code (especially the chat versions and Qwen2.5-Coder), so they can write code, explain code, and help with debugging. Developers can use Qwen-7B-Chat or the specialized Qwen2.5-Coder models as AI pair programmers to suggest code snippets or algorithms in multiple programming languages.
Multimodal Applications: With Qwen-VL and Qwen-VL-Chat, you can build applications that require understanding images along with text. Use cases include an AI that can describe uploaded images, answer questions about an image’s content (visual Q&A), perform OCR on images, or help users analyze diagrams/screenshots. For example, Qwen-VL-Chat can take an image plus a question like “What is happening in this picture?” and produce a detailed answer. This opens possibilities for assistive tech, image search engines, or automating form data extraction.
Research and Benchmarking: Because Qwen models are open and powerful, researchers use them to study large LLM behavior, fine-tune them on domain-specific data, or benchmark them against other models. Qwen-72B in particular, being one of the largest open models, is used as a reference for cutting-edge LLM performance in academia.
Keep in mind that while Qwen models are high-performing, they are not immune to typical LLM limitations (e.g., potential to produce incorrect or nonsensical answers, sensitivity to prompt wording, etc.). Always evaluate a model’s outputs for your specific use case. If deploying publicly, implement appropriate filters or human review for sensitive tasks.

If you use Qwen models in a research project or application, it’s good practice to acknowledge the source. Alibaba has released an official technical report for Qwen (see arXiv:2309.16609) which you can cite in academic papers. In the Qwen model cards, they provide a citation BibTeX entry. For example, the Qwen-7B model card under Citation gives a reference format. Typically, you can cite it as:
Bai et al., “Qwen: A Family of Open Large Language Models (Tongyi Qianwen)”, Alibaba Cloud, 2023.
In open-source projects or applications, you can credit “Qwen by Alibaba Cloud” and link to the official Qwen repository or model card. Also, if you’re using the model under the official license, ensure you include any required attribution or text from the license agreement. For instance, the Tongyi Qianwen License might ask for including a copy of the license and an acknowledgment that the model came from Alibaba Cloud’s Qwen.
Additionally, if you fork the code or weights, maintain the provenance (don’t remove the original license files). On Hugging Face, the model pages (such as Qwen-7B’s card) include the license and contact info. Following those guidelines will ensure you’re respecting the creators’ terms while using this open model.