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XLOverflow/
qwen3-eagle3-accrate

Name: qwen3-eagle3-accrate
Availability: InStock

AngelSlim/Qwen3-8B_eagle3

finetune

apache-2.0

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AccRate (α=1.0) — EAGLE3 Draft Model for Qwen3-8B

Our method. Per-step loss weighted by token acceptance rate: w_s = 1 + α(1 − β_s), α=1.0.

Part of a course project evaluating per-step weighted loss functions for training EAGLE3 draft models. Full pipeline and source: https://github.com/XLOverflow/anlp_course_project

Collection: Qwen3 EAGLE3 — Weighted Loss Variants

Training

Framework: SpecForge (our fork: https://github.com/XLOverflow/SpecForge)
Target model: Qwen/Qwen3-8B
Draft init: AngelSlim/Qwen3-8B_eagle3
Data: ShareGPT-style reasoning traces (see scripts/data/ in project repo)
Loss weight: w_s = 1 + α(1 − β_s), α=1.0
Initialized from: baseline-uniform/epoch_4_step_82000
Additional epochs: 1
β_s profiled offline via scripts/train/profile_beta.py

Evaluation (Qwen3-8B target)

Dataset	τ (accept. length)	Speedup	Accuracy
GSM8K	7.359	4.588×	95.15%
MATH500	7.326	4.606×	95.20%

Baselines for reference: Vanilla ≈ 1× speedup, EAGLE-orig ≈ 2× speedup.

Files

model.safetensors — draft model weights (~763 MB)
config.json — model config
Corresponds to: outputs/eagle3-accrate/epoch_0_step_17026 in the original training output

Optimizer state (~3 GB) is not uploaded — use the project repo's training scripts to resume from scratch if needed.

Usage

python

from huggingface_hub import snapshot_download
draft_path = snapshot_download(repo_id="XLOverflow/qwen3-eagle3-accrate")
# Then load with EAGLE's EaModel — see scripts/eval/eval_combined.py in the project repo.