ChatDLM – A new generation of conversational generative large model launched by Qafind Labs

What is ChatDLM?

ChatDLM is a new generation dialogue generation large model launched by Qafind Labs, aimed at breaking through the traditional Transformer architecture’s bottlenecks in handling long contexts and improving inference efficiency. The model integrates Block Diffusion and Mixture of Experts (MoE) technologies, features 7 billion parameters, achieves an inference speed of up to 2800 tokens per second, and supports a context window of up to 131,072 tokens.

In performance evaluations, ChatDLM achieved 92.0% accuracy on the Humaneval (0-shot) test and 84.2% accuracy on the Fill-in-the-Middle test, demonstrating outstanding capabilities.

Key Features of ChatDLM

• Efficient Text Generation:
  ChatDLM features ultra-fast inference, generating over 2800 tokens per second, enabling real-time responses for more natural and fluent conversations. It supports extremely long context processing, up to 131,072 tokens, easily handling tasks like long document generation and conversation history tracking.

• Controllable Generation and Local Editing:
  ChatDLM allows precise control over text generation to meet specific output requirements. It can seamlessly edit specific parts of the generated content without the need to regenerate the entire text, significantly enhancing flexibility.

• Resource Efficiency:
  The optimized architecture of ChatDLM reduces computational demands, lowering operational costs by 30%, making it suitable for various professional applications.

• Dynamic Optimization and Domain Adaptation:
  ChatDLM employs dynamic early stopping and iterative step prediction mechanisms to minimize unnecessary computations while maintaining high accuracy. In vertical domains such as legal and medical fields, it can fine-tune expert weights to boost domain knowledge recall to 95.6%.

Technical Principles Behind ChatDLM

• Block Diffusion Technology:
  ChatDLM uses block diffusion to split input text into multiple semantic units (blocks). Each block undergoes independent spatial diffusion computation, and cross-block attention mechanisms are employed for global information interaction. This reduces complexity from traditional O(n²) to O(n log n), significantly improving computational efficiency.

• Mixture of Experts (MoE) Mechanism:
  ChatDLM is equipped with 32 to 64 expert modules, with only 2 experts activated per computation. Tasks are dynamically allocated by a Gating Network, reducing computation by 70% while maintaining model accuracy. MoE also enables domain-specific optimization, improving domain knowledge recall up to 95.6% through expert weight fine-tuning.

• Long Context Processing Solutions:
  To handle ultra-long contexts, ChatDLM adopts optimized Rotary Position Embedding (RoPE) and hierarchical caching strategies. RoPE enhances the model’s perception of long sequence positions, while the hierarchical caching strategy achieves a cache hit rate of up to 98.2% with 131,072-token inputs. Dynamic early stopping combined with iterative step prediction (averaging 12-25 steps to converge) reduces invalid computations by 40%.

• Inference Optimization:
  ChatDLM applies techniques like dynamic early stopping, BF16 mixed-precision training, and ZeRO sharding to enable seamless multi-GPU scaling, further enhancing operational efficiency and scalability.

• Parallel Decoding and Local Repair:
  By combining block diffusion and parallel decoding, ChatDLM can optimize multiple parts of the text simultaneously instead of generating sequentially like traditional models. This improves generation speed and allows targeted correction of specific text segments without re-generating the entire content.

Official Websites for ChatDLM

• International version: chatdlm.com

• Technical Report: https://www.chatdlm.com/about/report.html

Application Scenarios for ChatDLM

• Multi-turn Conversations and Dynamic Knowledge Base Loading:
  ChatDLM handles long-text dialogues, quickly understands user needs, and provides accurate answers, making it suitable for intelligent customer service systems in industries like finance and telecommunications, improving customer issue resolution rates to 92%.

• Real-time Emotion Monitoring and Knowledge Retrieval:
  During employee-customer interactions, ChatDLM can monitor emotions, speech rate, and sensitive terms in real time, dynamically retrieve knowledge, and push information to employees, enhancing service efficiency and accuracy.

• Long Document Creation and Editing:
  ChatDLM supports the generation of novel outlines and automatic plot expansion for works with tens of thousands of words, increasing creative efficiency by five times. It is also applicable to writing academic papers, producing brochures, and summarizing meeting notes.

• Academic Paper Review and Knowledge Graph Construction:
  ChatDLM helps students and researchers quickly review academic papers and build interdisciplinary knowledge graphs, reducing the time required for literature review generation by 80%.