Flint-DFlash2
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Flint-DFlash2-GGUF
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View details ↗GLM-5.3-DFlash2
incoaiThis repository contains the DFlash 2 draft model for `zai-org/GLM-5.3`. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution. DFlash 2 is also supported by vLLM v0.28.0 and later; see `incoai/GLM-5.3-NVFP4` for a vLLM serving example with the NVFP4-quantized target. See the blog post for more details.
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View details ↗GLM-5.3-Flash-DFlash2
incoaiThis repository contains the DFlash 2 draft model for `zai-org/GLM-5.3-Flash`. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution. Benchmark numbers for this checkpoint are being re-measured and will be published here once final.
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View details ↗GLM-5.3-NVFP4
incoaiThis repository contains an NVFP4-quantized version of Z.ai's `zai-org/GLM-5.3`. The weights and activations of the routed-expert linear layers are in NVFP4, and the KV cache is in FP8. The checkpoint uses the NVIDIA Model Optimizer format (`quant_method: modelopt`) and is served directly by SGLang and vLLM on NVIDIA Blackwell GPUs (SM100+). This checkpoint was produced with our in-house post-training quantization toolchain and exported in the NVIDIA Model Optimizer format. Only the weights and activations of the linear operators within the routed MoE experts are quantized to NVFP4, with static per-tensor activation scales. The KV cache is quantized to FP8 with static unit scales (`kv_cache_quant_algo: FP8`). The commands below enable speculative decoding with the DFlash 2 draft model (lossless, 7 draft tokens per verification step); remove the `--speculative-*` flags to serve without it.
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Muse-Glimmer-30B-DFlash2
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Ornith-1.5-122B-DFlash2
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View details ↗Qwen3.6-35B-A3B-DFlash2
incoaiThis repository contains the DFlash 2 draft model for `Qwen/Qwen3.6-35B-A3B`. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution. We compare autoregressive decoding, Qwen3.6's built-in MTP with seven speculative steps, and DFlash 2. Both speculative methods propose seven draft tokens per verification step.
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View details ↗Qwen3.8-27B-DFlash2
incoaiThis repository contains the DFlash 2 draft model for `Qwen/Qwen3.8-27B`. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. The checkpoint is also mirrored at `z-lab/Qwen3.8-27B-DFlash2`. DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution. See the blog post for other engines and more details.
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View details ↗Qwen3.8-27B-DFlash2-GGUF
incoaiThis repository contains GGUF conversions of `incoai/Qwen3.8-27B-DFlash2`, the DFlash 2 draft model for `Qwen/Qwen3.8-27B`. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. The checkpoints are also mirrored at `z-lab/Qwen3.8-27B-DFlash2-GGUF`. DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution. Build llama.cpp with DFlash 2 support (PR #27342):
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View details ↗This repository contains the DFlash 2 draft model for `Qwen/Qwen3.8-Flash-Next`. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution. Serve with SGLang. Flash-Next needs a small target-feature capture patch, included in this repository and validated at TP1, until support lands upstream:
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