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Technical Report Open-sourcing soon Learn MoreChatDLM is a next-generation diffusion-based language model developed by Qafind Labs. With groundbreaking parallel decoding and KV cache optimizations, ChatDLM can generate over 2800 tokens per second, delivering rapid and coherent AI-powered conversations.
Discover what makes ChatDLM revolutionary
With over 2800 tokens per second, ChatDLM delivers responses in real-time, making conversations fluid and natural.
Precision control over text generation allows for highly customizable outputs tailored to specific requirements.
Seamlessly edit specific portions of generated content without regenerating the entire text.
Handle complex tasks with multiple requirements simultaneously, delivering precise solutions.
Exceptional performance in translation tasks, maintaining context and nuance across languages.
Optimized architecture reduces computational requirements, leading to lower operational costs.
Tokens Per Second
Lower Operational Cost
Specialized Use Cases
How ChatDLM compares to other language models
Data show that ChatDLM offers significant advantages in scenarios such as controllable generation, local inpainting, multi-constraint tasks, numeric countdowns, itinerary planning, Sudoku solving, translation, and more.
Precision control over generated content
Targeted modifications without full regeneration
Exceptional at structured problems like Sudoku
Our vision for the future of ChatDLM
Expanding ChatDLM's capabilities to understand and generate content across multiple modalities, including text, images, and potentially audio.
Further advancing our precision text generation capabilities, allowing for even more fine-grained control over style, tone, length, and content.
Fundamentally reimagining how language models can work, pushing beyond current paradigms to create truly next-generation AI systems.
Everything you need to know about ChatDLM
A DLM is a large-language model that fuses diffusion processes with autoregressive decoding. While diffusion techniques were originally devised for image and video synthesis, DLM applies them to text: starting from a forward diffusion and reverse-noise initialization, it iteratively refines the output into high-quality content—much like sketching a rough draft and then polishing it step by step.
DLM demonstrates clear strengths in use cases such as controllable generation, local inpainting (partial re-writes), multi-constraint tasks, numeric countdowns, itinerary planning, Sudoku solving, translation, and more.
By combining block-wise parallel diffusion generation with efficient autoregressive knowledge extraction, DLM not only reproduces text quickly and accurately but also pushes both generation quality and speed to previously unattainable, production-ready levels.
A 131,072-token context window means the model can read and generate nearly 100,000 English words in a single pass.