suryadv/strive-kubric-checkpoints

STRIVE Kubric checkpoints

This repository contains the three merged checkpoints used for the STRIVE Kubric baseline experiments, plus six intermediate LoRA adapters for Baselines 2 and 3. The corresponding code, commands, evaluation artifacts, and detailed provenance are in the STRIVE reproducibility repository.

All three checkpoints are fine-tuned and merged derivatives of Qwen/Qwen3-VL-4B-Instruct. They retain the base model's Apache-2.0 license.

Checkpoints

Subfolder	Step	Size	Purpose
baseline1-detector-5500	5500	8.28 GiB	Single-frame object detector used as Baseline 2 stage 1.
baseline2-4000	4000	8.28 GiB	Streaming staged mapper used as Baseline 2 stage 2.
baseline3-4000	4000	8.28 GiB	Streaming end-to-end scene parser used as Baseline 3.

Baseline 2 is staged: use baseline1-detector-5500 for stage-one detection and baseline2-4000 for stage-two mapping. Baseline 3 uses baseline3-4000 directly.

Intermediate LoRA adapters

These PEFT adapters preserve the retained pre-final checkpoints without duplicating the base model or training-only optimizer state.

Subfolder	Step	Purpose
baseline2-adapter-3250	3250	Baseline 2 stage-two mapper/tracker
baseline2-adapter-3500	3500	Baseline 2 stage-two mapper/tracker
baseline2-adapter-3750	3750	Baseline 2 stage-two mapper/tracker
baseline3-adapter-3250	3250	Baseline 3 end-to-end scene parser
baseline3-adapter-3500	3500	Baseline 3 end-to-end scene parser
baseline3-adapter-3750	3750	Baseline 3 end-to-end scene parser

Each adapter targets Qwen/Qwen3-VL-4B-Instruct and includes its PEFT configuration, adapter safetensors, tokenizer, chat template, and processor configuration. Optimizer shards, scheduler state, RNG state, and other training-resume artifacts are intentionally excluded.

Download

Download one checkpoint without fetching the other two:

from huggingface_hub import snapshot_download

repo_id = "suryadv/strive-kubric-checkpoints"
subfolder = "baseline3-4000"
snapshot = snapshot_download(
    repo_id,
    allow_patterns=[f"{subfolder}/*"],
)
model_path = f"{snapshot}/{subfolder}"
print(model_path)

The returned model_path can be passed to Transformers, vLLM, or the STRIVE inference launchers as a normal local merged-model directory. Transformers also accepts the repository ID together with subfolder=<checkpoint-name>.

Integrity and provenance

CHECKPOINT_MANIFEST.json records the exact byte size and SHA-256 digest of every uploaded file. Each subfolder contains the full merged checkpoint: two safetensors shards, their index, model/generation configuration, tokenizer, processor configuration, chat template, and vocabulary files. Training-only optimizer state, caches, datasets, and evaluation outputs are +intentionally not duplicated here. The intermediate adapter folders contain only +the reusable LoRA and inference metadata described above.

The training and evaluation setup is documented at the reproducibility tag strive-repro-2026-07-18.