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NOTICE

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+Weaver
+Copyright 2026 Mirai Labs
+
+This repository contains the Weaver checkpoint for DFlash-TfM.

README.md

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+---
+tags:
+- sglang
+- speculative-decoding
+- dflash
+- mirai
+library_name: sglang
+license: apache-2.0
+pipeline_tag: other
+---
+
+<div style="display:flex;align-items:center;justify-content:space-between;gap:20px;flex-wrap:wrap">
+  <div style="flex:1;min-width:260px">
+    <h1 style="margin:0 0 12px">Trees from Marginals: Weaver</h1>
+    <p style="margin:0">Weaver is a lightweight autoregressive Transformer that turns DFlash marginal predictions into proposal trees. With rollback-free Gated Delta Net tree verification, DFlash-TfM reaches 392.8 tokens/s/sequence on Qwen3.6-27B on a single B200: 4.37x over autoregressive decoding and 24.7% over tuned DFlash.</p>
+    <p style="margin:10px 0 0;display:flex;gap:8px;align-items:center;flex-wrap:wrap">
+      <a href="#"><img src="https://img.shields.io/badge/arXiv-coming%20soon-b31b1b?style=for-the-badge&logo=arxiv&logoColor=white" alt="arXiv coming soon"></a>
+      <a href="https://github.com/trymirai/sglang"><img src="https://img.shields.io/badge/GitHub-code-181717?style=for-the-badge&logo=github&logoColor=white" alt="GitHub code"></a>
+    </p>
+  </div>
+  <img src="https://assets.trymirai.com/images/logo/ml_small_logo.svg" alt="Mirai Labs" width="64" height="64" style="width:80px;height:80px;object-fit:contain;flex:0 0 auto">
+</div>
+
+<p align="center">
+  <img src="assets/tfm-throughput.png" alt="Per-dataset Qwen3.6-27B throughput comparison for autoregressive decoding, DFlash, DDTree, and DFlash-TfM." width="760">
+</p>
+
+### Method
+
+A factorized drafter emits marginal distributions for several future positions in a single forward pass. Because it ignores conditional dependencies between positions, the acceptance rate falls as draft length grows. DFlash-TfM addresses this by extracting the top-K marginal predictions from DFlash and using Weaver, a lightweight autoregressive draft model, to restore dependencies conditioned on the previously selected draft tokens.
+
+Weaver has 56.7M trainable parameters and operates exclusively over the DFlash candidate vocabulary, a tiny subset of the full vocabulary. It shares the verifier embedding and output projection matrices, but never projects to the full vocabulary: at each tree-expansion step, Weaver predicts residual logits over DFlash's top-512 candidates, adds them to the DFlash marginal logits, and normalizes over the candidate set.
+
+<p align="center">
+  <img src="assets/tfm-architecture.png" alt="DFlash-TfM uses DFlash marginals in parallel, then conditions tree proposals autoregressively with Weaver." width="760">
+</p>
+
+This repository contains the Weaver checkpoint for DFlash-TfM, short for DFlash - Tree From Marginals. Weaver is not a standalone language model: it is used during speculative decoding together with a Qwen3.6-27B target model, a Qwen3.6-27B DFlash drafter, and the Mirai Labs SGLang fork with the fused Gated Delta Net tree-verification kernels.
+
+### Results
+
+We evaluate on Qwen3.6-27B over chat, math, and code workloads: MTBench, ShareChat, GSM8K, MATH500, AIME25, HumanEval, MBPP, and LiveCodeBench. All runs use BF16 precision on a single B200 with batch size 1, temperature 1.0, reasoning enabled, maximum output length 4096, and the server cache flushed between requests.
+
+Throughput is computed as total generated tokens divided by wall-clock runtime, including prefill, scheduling, and decoding. Speedup is measured against autoregressive decoding under the same dataset, temperature, and reasoning setting. Macro Avg. is the unweighted average across datasets.
+
+| Method | Setting | Throughput | Speedup |
+| --- | --- | ---: | ---: |
+| Autoregressive | BF16 target only | 89.9 tok/s/seq | 1.00x |
+| DFlash | tuned chain baseline | 315.0 tok/s/seq | 3.50x |
+| DFlash-TfM + Weaver | tree budget 64 | 392.8 tok/s/seq | 4.37x |
+
+<p align="center">
+  <img src="assets/tfm-results-table.png" alt="Full DFlash-TfM table with speedup and accepted-token statistics across sampling and reasoning settings." width="900">
+</p>
+
+DFlash-TfM with Weaver is the fastest configuration on every task in this sweep. The gap comes from the superior acceptance probability: Weaver's trees lengthen the mean accepted draft by 77% relative to the chain DFlash baseline and by 32% relative to DDTree at the same tree size.
+
+### Using this checkpoint
+
+This repository contains the Weaver checkpoint used for DFlash-TfM:
+`weaver/qwen36_27b_weaver.pth`
+
+SHA-256: `71f540b143fb6bab14ba724c20e97a72ce198de103cfd228d31c3ce339227833`
+
+
+_This section will be updated once the reference implementation becomes available in the next few days._
+
+
+### Citation
+
+If you find our work helpful, feel free to give us a cite.
+
+```bibtex
+@misc{dflash-tfm,
+    title  = {{Trees from Marginals}: Autoregressive Drafting with Factorized Priors},
+    author = {Yuma Oda and Ryan Mathieu and Roman Knyazhitskiy and Artur Chakhvadze},
+    note   = {In collaboration with others at Mirai Labs},
+    month  = {July},
+    year   = {2026}
+}
+```

weaver/qwen36_27b_weaver.pth

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