dflash

Discover how DFlash speculative decoding achieves 3-6x speedup over traditional methods by using a block-diffusion head for parallel token generation with minimal memory. Learn more.

Discover 7 memory optimization strategies for DFlash deployment. Reduce memory usage and speed up inference on constrained hardware with these proven techniques.

Discover how DFlash integrates Qwen3 rotary embeddings using the Hugging Face transformers library. Learn how to ensure exact positional encoding matches for optimized draft models.

Maximize DFlash throughput by configuring speculative decoding. Optimize block size, cache reuse with default target layers, and use sliding window for large prompts to maintain peak token generation speed and prevent overflow.

Learn to train custom DFlash draft models by selecting target LLM layers, configuring architecture, and using hidden states and token predictions for effective supervision.

Troubleshoot MLX backend issues with DFlash. Resolve common failures like missing gated-delta support, version mismatches, and cache problems for seamless operation.

Explore DFlash's noise embedding mechanism, a temporary tensor that enhances draft model predictions by incorporating recent token embeddings, ensuring coherence with target model context.

Integrate DFlash with custom target models using this complete guide. Learn essential steps like matching hidden sizes and verifying target attributes for seamless implementation.

Benchmark DFlash performance: Discover how Transformers, SGLang, and vLLM backends compare in latency and throughput for single-GPU and concurrent workloads to optimize your inference.

Discover how DFlash manages thinking tokens in Qwen3 models. Learn about its tokenizer API integration and guard-rails for model compatibility.

Learn how to run DFlash benchmarks on custom datasets. Easily integrate your data by registering a new entry or providing a pre-cached JSONL file. Get started today!

Debug DFlash integration with vLLM using LOGURU_LEVEL DEBUG and inspect the _send_vllm payload in dflash/benchmark.py. Resolve connection and compatibility errors effectively.