Dream-v0-Instruct-7B
A 7-billion-parameter instruction-tuned diffusion language model for text generation tasks.
Timeline (last 90 days)
The experiments were conducted on two models, LLaDA-8B-Instruct and Dream-v0-Instruct-7B, covering five benchmarks.
Compared with direct continuous-time sampling, SpecCTMC reduces the number of sequential model evaluations by 1.73 to 4.38 times while maintaining the reference sampling distribution.
LLaDA-8B adopts large-scale masked diffusion pretraining and approaches autoregressive models of the same scale on instruction-following tasks; Dream-v0-Instruct-7B explores the instruction fine-tuning path of diffusion models in dialogue scenarios.
All Facts (3)
The experiments were conducted on two models, LLaDA-8B-Instruct and Dream-v0-Instruct-7B, covering five benchmarks.
50%UnverifiedCompared with direct continuous-time sampling, SpecCTMC reduces the number of sequential model evaluations by 1.73 to 4.38 times while maintaining the reference sampling distribution.
50%UnverifiedLLaDA-8B adopts large-scale masked diffusion pretraining and approaches autoregressive models of the same scale on instruction-following tasks; Dream-v0-Instruct-7B explores the instruction fine-tuning path of diffusion models in dialogue scenarios.
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