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siddharthmb/
2026.AP.cpt_ifp

Name: 2026.AP.cpt_ifp
Availability: InStock

safetensors

HuggingFaceFW/fineweb-edu

2501.02086

Qwen/Qwen2.5-3B

finetune

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2026.AP.cpt_ifp

Main IFPruning continued-pretraining (CPT) run: Qwen2.5-3B with a Qwen2.5-0.5B predictor backbone and MLP head producing per-layer FFN masks (SoftTopK, t_ffn=1536 of 11008, ~1B activated params), mask_mode=predictor, 3000 steps (~0.8B tokens), warmup_mask_steps=500, lrs llm 2e-5 / predictor 5e-5 / head 1e-3.

Part of a replication of Apple's Instruction-Following Pruning for Large Language Models (arXiv:2501.02086) — "AP" = apple-paper-replicate, package ifpruning in Sid-MB/mats_exploration under code/apple-paper-replicate/ (branch introspection-causal-test, merged to main at a76965b).

Architecture

LLM: Qwen2.5-3B (Qwen2ForCausalLM, 36 layers, d_ffn 11008)
Predictor backbone: Qwen2.5-0.5B (Qwen2Model) + 2-layer MLP head (head.pt) producing per-layer FFN importance scores [36, 11008]; per-row SoftTopK selects t_ffn=1536 of 11008 FFN units (~1B activated params). Dense-baseline runs train the same LLM without masking.

checkpoints/step_3000/pytorch_model_fsdp_0/ — FSDP2 SHARDED_STATE_DICT model weights (llm + predictor_backbone + head)
checkpoints/step_3000/optimizer_0/ — optimizer state (for exact training resumption)
checkpoints/step_3000/random_states_*.pkl, scheduler.bin — RNG/scheduler state
model/ — exported final model (llm/, predictor_backbone/, head.pt) from the FSDP export step

Performance

No evaluations were run on this checkpoint. The project reached "scaffold + smoke test + this training grid" before being paused (see code/mats_exploration/everything we learned.md); logs/eval/ is empty and no eval_results directory exists. The only training-quality signal is the loss curves in the wandb runs below.

Reproduction

From code/ in the mats_exploration repo (paths as of June 2026; IFP_ROOT=/nlp/scr/siddharth/apple-paper-replicate set in slurm/_common.sh):

sbatch apple-paper-replicate/slurm/train.sbatch apple-paper-replicate/configs/presets/cpt_ifp.yaml

which runs (8 GPUs, accelerate FSDP2 full-shard bf16, SHARDED_STATE_DICT):

bash

srun uv run accelerate launch --config_file apple-paper-replicate/configs/accelerate_fsdp8.yaml \
    -m ifpruning.train --config apple-paper-replicate/configs/presets/cpt_ifp.yaml \
    --ckpt-root $IFP_ROOT/ckpts --out-root $IFP_ROOT/models

Preset: configs/presets/cpt_ifp.yaml. Data: fineweb-edu sample-10BT, streaming, seq_len 4096. Seed 0. Slurm job 15878284 (jagupard37, preempted and resumed on jagupard39, 8 GPUs).

Weights & Biases

https://wandb.ai/siddharth-stanford/ifpruning-cpt/runs/10pj697a
https://wandb.ai/siddharth-stanford/ifpruning-cpt/runs/36r329pb (resume from step 1500)

Cluster paths (Stanford NLP)

Training log: /nlp/scr2/siddharth/code/mats_exploration/code/apple-paper-replicate/logs/train/cpt_ifp_15878284.out
Original checkpoint dir: /nlp/scr2/siddharth/apple-paper-replicate/ckpts/cpt_ifp (deleted after this upload was verified; this repo is now the only copy)
Code, configs, paper PDF, research notes: /nlp/scr2/siddharth/code/mats_exploration/code/apple-paper-replicate/
Research note with the full run grid: .../research-notes/2026-06-12_setup-and-smoke.md

siddharthmb/2026.AP.cpt_ifp