Alibaba Launches Qwen-7B: A Revolutionary Open-Source Language Model
Alibaba has announced its latest open-source Large Language Model (LLM), dubbed Qwen-7B. Qwen-7B This marks their first venture into the sphere of publicly available language models, built on a robust framework of 7 billion parameters.
For context, Qwen-7B has been trained with a vast dataset of 2.2 trillion tokens, employing a context size of 2048 during training, which can be extended to a maximum of 8192 during practical use. In comparison, the Llama-2 model allows for a 4096 context size.
Benchmarks play a crucial role in evaluating model effectiveness, and Chinese developers claim that Qwen-7B outperforms Llama-2 in various areas. Notably, in the Human-Eval coding benchmark, Qwen-7B achieved a score of 24.4, significantly higher than Llama-2's 12.8. However, it's wise to interpret these figures cautiously, as some benchmarks do reveal that Qwen-7B excels not only over Llama-2's basic version but also its 13B alternative. Still, the gap narrows when comparing it to the more refined iterations of Llama-2. It’s worth noting that specific training techniques employed for Qwen-7B have not been publicly clarified.
In a move akin to LLaMa2-chat, Qwen has rolled out a chat-optimized variant called Qwen-7B-Chat, designed for enhanced user interaction and equipped with a variety of tools. APIs to enhance its responsiveness.
For those who appreciate the technical intricacies, it’s noteworthy that the architecture of Qwen-7B shares similarities with LLaMA, yet also showcases unique characteristics that set it apart:
- It employs untied embedding.
- It employs rotary positional embedding.
- Biases are generally omitted, with the exception of QKV within the attention mechanism.
- RMSNorm is favored over LayerNorm.
- Rather than the typical ReLU activation function, it implements SwiGLU.
- The integration of Flash attention accelerates the training process.
- The model consists of 32 layers, features an embedding dimension of 4096, and supports 32 attention heads.
On the licensing front, Qwen-7B is aligned with Llama-2, allowing for commercial use but with a cap on user numbers. Llama-2 limits this to 700 million active users monthly, while Qwen-7B’s threshold is set at 100 million.
For a thorough exploration, readers can access a technical report available on GitHub. Additionally, a Chinese version is provided for those interested in practical applications of the model's functionalities. a demonstration of Qwen-7B Key Insights About Large Language Models
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