yuyijiong/speculative_pipeline_decoding

Speculation head checkpoints

Important

This repo's checkpoints are v11 (default branch / main). Older v10 checkpoints remain on the v10 branch. Use the matching code release: v11 checkpoints require speculative_pipeline_decoding v11; v10 checkpoints load via old_version_v10/.

Pre-trained pipeline speculation head weights. Each .pt file is a single checkpoint produced by training; pair it with the same base model architecture it was trained on (see config["base_model_path"] inside the file).

For inference, evaluation, and training examples, see the official repo:
https://github.com/yuyijiong/speculative_pipeline_decoding

Filename format

Files are named:

{model}_s{num_stages}_l{num_spec_layers}.pt

Part	Meaning
{model}	Base model tag from training config (e.g. Qwen3.5-4B, Qwen3.5-9B)
s{...}	num_stages — pipeline depth (number of target-model stages)
l{...}	num_spec_layers — number of Transformer layers in the speculation module

Example: Qwen3.5-9B_s16_l2.pt → Qwen3.5-9B base, 16 stages, 2 spec layers.

Checkpoint contents

Each file is a PyTorch archive with two top-level keys:

{
    "state_dict": ...,  # weights of the speculation module
    "config": { ... },  # hyperparameters and metadata
}

config fields (always present)

Field	Description
base_model_path	Base model path recorded at training time (often a machine-local path; override at load time — see below)
hidden_size	Hidden size (matches base model)
vocab_size	Base model vocabulary size
draft_vocab_size	Draft head output size (full vocab or draft subset)
num_stages	Pipeline depth (same as s in filename)
num_spec_layers	Speculation module depth (same as l in filename)
version	Checkpoint format version (11)
num_aggr_types	Number of aggregation types m in the speculation module, determining the number of FC modules
aggr_feature_bound	HF hidden-state layer indices for aggregation anchors g_0..g_{m-1} (replaces v10's shallow_hidden_layer_indices)
trained_with_use_deepest	Whether training used deepest-layer features

config fields (optional)

Field	Description
model_type	Base model type recorded at training time (e.g. qwen3_5)
spec_init_from_base_layers	Base layers used to initialize the spec module (if any)
draft_token_ids	Draft vocabulary token ids (only when trained with a draft vocab subset)

Available v11 checkpoints

Base model	s (stages)	l (spec layers)	Filename
Qwen3.5-4B	4	4	Qwen3.5-4B_s4_l4.pt
Qwen3.5-4B	8	4	Qwen3.5-4B_s8_l4.pt
Qwen3.5-4B	16	2	Qwen3.5-4B_s16_l2.pt
Qwen3.5-9B	4	4	Qwen3.5-9B_s4_l4.pt
Qwen3.5-9B	8	4	Qwen3.5-9B_s8_l4.pt
Qwen3.5-9B	16	2	Qwen3.5-9B_s16_l2.pt

Loading checkpoints

config["base_model_path"] is often a local path from the training machine (e.g. /share/models/Qwen3.5-4B). On your machine, pass the correct Hugging Face id or local directory via --base_model_path; it overrides the path stored in the checkpoint:

python pipeline_inference.py \
  --spec_head_ckpt /path/to/Qwen3.5-4B_s4_l4.pt \
  --base_model_path Qwen/Qwen3.5-4B

python eval.py \
  --spec_head_ckpt /path/to/Qwen3.5-4B_s4_l4.pt \
  --base_model_path /your/local/Qwen3.5-4B \
  --data_dir eval_data \
  --output_dir ./eval_output

If --base_model_path is omitted, the value from config["base_model_path"] is used as-is.

More usage details: speculative_pipeline_decoding.

Citation

If you use this repo, please cite our paper:

@misc{yu2026speculativepipelinedecodinghigheraccruacy,
      title={Speculative Pipeline Decoding: Higher-Accruacy and Zero-Bubble Speculation via Pipeline Parallelism}, 
      author={Yijiong Yu and Huazheng Wang and Shuai Yuan and Ruilong Ren and Ji Pei},
      year={2026},
      eprint={2605.30852},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2605.30852}, 
}