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Mirror Qwen3.8-27B-DFlash2 (z-lab) with provenance header; Apache-2.0

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README.md ADDED
@@ -0,0 +1,167 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ library_name: transformers
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+ pipeline_tag: text-generation
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+ base_model:
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+ - Qwen/Qwen3.8-27B
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+ inference: false
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+ tags:
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+ - dflash2
10
+ - speculative-decoding
11
+ - block-diffusion
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+ - draft-model
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+ - sglang
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+ - vllm
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+ - mirror
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+ - veloGB10
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+ ---
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+
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+ # Qwen3.8-27B-DFlash2 (mirror)
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+
21
+ > **This repository is a mirror of [z-lab/Qwen3.8-27B-DFlash2](https://huggingface.co/z-lab/Qwen3.8-27B-DFlash2).**
22
+ > We mirror it here so it can be pulled from a stable location. The model, weights, and the
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+ > original model card below are **unmodified** from the upstream source — full credit to **z-lab / Inco AI**.
24
+ > This mirror is provided for the [veloGB10](https://github.com/sf-stav/veloGB10) engine and workflow.
25
+ >
26
+ > **License: Apache-2.0** (the upstream card declares `apache-2.0`; a copy of the license is
27
+ > included in this repository as `LICENSE`). The upstream [README](https://huggingface.co/z-lab/Qwen3.8-27B-DFlash2/raw/main/README.md)
28
+ > is reproduced in full below.
29
+
30
+ ---
31
+
32
+ # Qwen3.8-27B-DFlash2
33
+
34
+ [Blog](https://inco.ai/blog/dflash2/) | [GitHub](https://github.com/z-lab/dflash)
35
+
36
+ This repository contains the DFlash 2 draft model for
37
+ [`Qwen/Qwen3.8-27B`](https://huggingface.co/Qwen/Qwen3.8-27B).
38
+ It is not a standalone language model: it runs inside a speculative
39
+ decoding server and drafts tokens for the target model to verify. The checkpoint is also
40
+ mirrored at [`z-lab/Qwen3.8-27B-DFlash2`](https://huggingface.co/z-lab/Qwen3.8-27B-DFlash2).
41
+
42
+ DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts
43
+ a whole block of tokens in a single pass and keeps the top candidates at
44
+ every position. A lightweight selector then traces one coherent path through them.
45
+ Two-tap dynamic convolutions in the backbone keep the draft from decaying
46
+ toward the end of the block. Decoding is lossless: greedy output
47
+ matches the target model exactly, and sampling preserves its distribution.
48
+
49
+ <div align="center">
50
+ <img src="assets/dflash2-figure.png" alt="DFlash 2: parallel block drafting with a candidate path selector" width="100%">
51
+ </div>
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+
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+ ## Quick Start
54
+
55
+ Serve with [SGLang](https://github.com/sgl-project/sglang):
56
+
57
+ ```bash
58
+ pip install "sglang[all] @ git+https://github.com/sgl-project/sglang.git#subdirectory=python"
59
+
60
+ python -m sglang.launch_server \
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+ --model-path Qwen/Qwen3.8-27B \
62
+ --speculative-algorithm DFLASH \
63
+ --speculative-draft-model-path incoai/Qwen3.8-27B-DFlash2 \
64
+ --speculative-num-draft-tokens 8
65
+ ```
66
+
67
+ Or with [vLLM](https://github.com/vllm-project/vllm):
68
+
69
+ ```bash
70
+ pip install -U "vllm @ git+https://github.com/vllm-project/vllm.git@refs/pull/52816/head"
71
+
72
+ vllm serve Qwen/Qwen3.8-27B \
73
+ --speculative-config '{
74
+ "method": "dflash",
75
+ "model": "incoai/Qwen3.8-27B-DFlash2",
76
+ "num_speculative_tokens": 7
77
+ }'
78
+ ```
79
+
80
+ See the [blog post](https://inco.ai/blog/dflash2/) for other engines and more details.
81
+
82
+ ## Evaluation
83
+
84
+ - Runtime: SGLang on one NVIDIA H200, with FlashAttention 3 for target and draft attention
85
+ - Speculation block size: 8 (7 draft tokens per verification step)
86
+ - Sampling: Qwen3.8's officially recommended parameters (temperature 1.0, top-p 0.95, top-k 20), with `xhigh` reasoning effort
87
+ - Maximum new tokens: 4096
88
+ - Prompts: benchmark formatting from [`z-lab/dflash`](https://github.com/z-lab/dflash)
89
+
90
+ We compare autoregressive decoding, Qwen3.8's built-in seven-token MTP,
91
+ a community DSpark drafter
92
+ ([`RadixArk/Qwen3.8-27B-DSpark`](https://huggingface.co/RadixArk/Qwen3.8-27B-DSpark)),
93
+ and DFlash 2. All speculative methods propose seven draft tokens per
94
+ verification step.
95
+
96
+ ### Acceptance Length
97
+
98
+ Acceptance length is the per-request mean of completion tokens divided by verification steps.
99
+ Higher is better.
100
+
101
+ | Task | MTP | DSpark | DFlash 2 |
102
+ | :--- | ---: | ---: | ---: |
103
+ | GSM8K | 5.02 | 4.36 | **5.46** |
104
+ | MATH-500 | 4.72 | 3.92 | **5.28** |
105
+ | HumanEval | 3.91 | 3.30 | **4.39** |
106
+ | MBPP | 3.99 | 3.51 | **4.79** |
107
+ | MT-Bench | 3.74 | 3.01 | **4.10** |
108
+
109
+ ### Throughput
110
+
111
+ Throughput is total output tokens divided by end-to-end wall time.
112
+ Each cell shows `output tok/s (speedup vs. autoregressive)`.
113
+
114
+ #### Concurrency 1
115
+
116
+ | Task | Autoregressive | MTP | DSpark | DFlash 2 |
117
+ | :--- | ---: | ---: | ---: | ---: |
118
+ | GSM8K | 68.9 | 178.5 (2.59×) | 185.3 (2.69×) | **236.1 (3.43×)** |
119
+ | MATH-500 | 69.0 | 172.8 (2.51×) | 174.5 (2.53×) | **230.7 (3.34×)** |
120
+ | HumanEval | 69.0 | 151.9 (2.20×) | 159.9 (2.32×) | **214.6 (3.11×)** |
121
+ | MBPP | 69.0 | 153.1 (2.22×) | 163.3 (2.37×) | **226.9 (3.29×)** |
122
+ | MT-Bench | 68.9 | 134.9 (1.96×) | 137.6 (2.00×) | **184.0 (2.67×)** |
123
+
124
+ #### Concurrency 8
125
+
126
+ | Task | Autoregressive | MTP | DSpark | DFlash 2 |
127
+ | :--- | ---: | ---: | ---: | ---: |
128
+ | GSM8K | 467.2 | 1,022.1 (2.19×) | 1,040.8 (2.23×) | **1,328.7 (2.84×)** |
129
+ | MATH-500 | 480.0 | 1,023.5 (2.13×) | 1,025.8 (2.14×) | **1,368.3 (2.85×)** |
130
+ | HumanEval | 483.4 | 934.2 (1.93×) | 956.5 (1.98×) | **1,291.5 (2.67×)** |
131
+ | MBPP | 478.0 | 938.1 (1.96×) | 974.1 (2.04×) | **1,328.0 (2.78×)** |
132
+ | MT-Bench | 480.5 | 835.2 (1.74×) | 802.3 (1.67×) | **1,090.2 (2.27×)** |
133
+
134
+ #### Concurrency 32
135
+
136
+ | Task | Autoregressive | MTP | DSpark | DFlash 2 |
137
+ | :--- | ---: | ---: | ---: | ---: |
138
+ | GSM8K | 1,329.8 | 1,381.1 (1.04×) | 1,506.5 (1.13×) | **1,922.5 (1.45×)** |
139
+ | MATH-500 | 1,505.8 | 1,415.6 (0.94×) | 1,429.0 (0.95×) | **1,951.8 (1.30×)** |
140
+ | HumanEval | 1,546.5 | 1,296.8 (0.84×) | 1,330.1 (0.86×) | **1,799.0 (1.16×)** |
141
+ | MBPP | 1,507.7 | 1,314.9 (0.87×) | 1,361.3 (0.90×) | **1,886.8 (1.25×)** |
142
+ | MT-Bench | 1,507.4 | 1,159.7 (0.77×) | 1,115.5 (0.74×) | **1,525.3 (1.01×)** |
143
+
144
+ ## Citation
145
+
146
+ If you find DFlash 2 useful, please cite:
147
+
148
+ ```bibtex
149
+ @misc{inco2026dflash2,
150
+ title = {{DFlash 2: Keep Drafting Parallel}},
151
+ author = {{Inco AI}},
152
+ year = {2026},
153
+ month = {August},
154
+ url = {https://inco.ai/blog/dflash2/}
155
+ }
156
+ ```
157
+
158
+ Please also cite the original DFlash paper:
159
+
160
+ ```bibtex
161
+ @inproceedings{chen2026dflash,
162
+ title = {{DFlash: Block Diffusion for Flash Speculative Decoding}},
163
+ author = {Chen, Jian and Liang, Yesheng and Liu, Zhijian},
164
+ booktitle = {International Conference on Machine Learning (ICML)},
165
+ year = {2026}
166
+ }
167
+ ```
assets/dflash2-figure.png ADDED

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+ "architectures": [
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+ ],
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+ "attention_bias": false,
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+ "attention_dropout": 0.0,
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+ "is_causal": false,
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+ "conv_kernel_size": 2,
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+ "mask_token_id": 248070,
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+ "selector_rank": 256,
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+ "selector_top_k": 16,
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+ 5,
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+ 33,
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+ },
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+ "dtype": "bfloat16",
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+ "eos_token_id": 248044,
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+ "head_dim": 128,
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+ "hidden_act": "silu",
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+ "hidden_size": 5120,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 17408,
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+ "layer_types": [
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+ "sliding_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "sliding_attention",
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+ "sliding_attention"
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+ ],
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+ "max_position_embeddings": 262144,
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+ "max_window_layers": 5,
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+ "model_type": "qwen3",
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+ "num_attention_heads": 32,
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+ "num_hidden_layers": 5,
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+ "num_key_value_heads": 8,
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+ "num_target_layers": 64,
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+ "pad_token_id": 248044,
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+ "rms_norm_eps": 1e-06,
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+ "rope_parameters": {
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+ "rope_theta": 10000000,
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+ "rope_type": "default"
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