NYCU-MLLab/Flow-Aligned-Distillation

Flow-Aligned Distillation checkpoints

This private artifact repository contains the six seed-44 FAD best-validation training checkpoints used for the 3B/8B, 15%/20%/25% operating points.

These files are custom PyTorch training checkpoints, not standalone Transformers from_pretrained directories. Load them with the FAD pipeline in the companion code repository:

https://github.com/hanklin9188/Flow-Aligned-Distillation

Checkpoints

Backbone	Nominal budget	Stored FFN banks	Seed	Best validation step	File
Llama-3.2-3B	15%	22	44	4,500	checkpoints/llama-3.2-3b/fad-15pct-k22-seed44.pt
Llama-3.2-3B	20%	19	44	4,500	checkpoints/llama-3.2-3b/fad-20pct-k19-seed44.pt
Llama-3.2-3B	25%	17	44	4,500	checkpoints/llama-3.2-3b/fad-25pct-k17-seed44.pt
Llama-3.1-8B	15%	25	44	7,500	checkpoints/llama-3.1-8b/fad-15pct-k25-seed44.pt
Llama-3.1-8B	20%	23	44	9,500	checkpoints/llama-3.1-8b/fad-20pct-k23-seed44.pt
Llama-3.1-8B	25%	20	44	not recorded	checkpoints/llama-3.1-8b/fad-25pct-k20-seed44.pt

The 8B 25% run retains its best-validation training checkpoint but does not have a completed shared_student.pt or deploy_bundle.pt export in the source archive. It is published here as a recoverable training checkpoint and should be exported with the companion pipeline before deployment.

Training contract

• objective: decision-token CE + FAD functional alignment
• private adapter: rank/alpha 128/128
• seed: 44
• checkpoint selection: minimum validation decision_ce
• 3B projection rank: 256
• 8B projection rank: 512

External requirements

The checkpoints do not redistribute Meta Llama base weights, merged teacher weights, tokenizer files, datasets, or licenses. Users must obtain the matching base model and teacher artifacts separately and comply with Meta's model terms.

SHA256SUMS is generated before upload and is the integrity authority for the six checkpoint files.