Qwen2.5-7B-1M is an advanced compact model built on a state-of-the-art Transformer architecture, integrating key innovations such as: Rotary Positional Embeddings (RoPE) for efficient encoding of positional information, the SwiGLU activation function for nonlinear transformations, RMSNorm with pre-normalization for stable training, and QKV bias in the attention mechanism. The architecture includes 28 layers with Grouped Query Attention (GQA), which enables optimal KV-cache utilization and reduced computational costs.
The main feature of the model is its ability to process up to 1,010,000 tokens of input context! This is equivalent to processing 10 full-length novels, 150 hours of speech transcripts, or 30,000 lines of code within a single request. Dual Chunk Attention (DCA) divides sequences into chunks and redistributes relative positions, ensuring stable performance on ultra-long contexts. Integration with YaRN attention scaling further enhances focus on critical information even when processing extremely long sequences. As a result, the model demonstrates outstanding accuracy in information retrieval tasks from very large documents, achieving over 80% accuracy even with a one-million-token context.
Qwen2.5-7B-1M opens up new possibilities in document processing, automated analysis, and intelligent assistants. It is ideally suited for legal analysis of lengthy contracts and documentation, scientific research, software development involving analysis of large codebases, and building technical support systems with access to extensive knowledge bases. In education, the model can analyze entire textbooks and generate comprehensive learning materials, while in business analytics, it can process voluminous reports and extract key insights for informed decision-making.
| Model Name | Context | Type | GPU | Status | Link |
|---|
There are no public endpoints for this model yet.
An endpoint is a server with pre-installed software that automatically downloads neural network weights and launches an OpenAI-compatible API to handle incoming requests.
A private endpoint is created within the user's project, belongs exclusively to the user, and is billed per server configuration in accordance with the infrastructure pricing.
We recommend creating private endpoints when you need to:
Contact our dedicated neural networks support team at support@immers.cloud or send your request to the sales department at sale@immers.cloud.