pith:42WE65LJ
MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe
An 8B multimodal model outperforms GPT-4o-latest and Qwen2.5-VL 72B on OpenCompass while using far less memory and inference time.
arxiv:2509.18154 v1 · 2025-09-16 · cs.LG · cs.CV
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Claims
MiniCPM-V 4.5 surpasses GPT-4o-latest and Qwen2.5-VL 72B on OpenCompass while using 46.7% GPU memory and 8.7% inference time of Qwen2.5-VL 7B on VideoMME.
The reported benchmarks and efficiency measurements generalize beyond the specific evaluation suites and hardware configurations used in the experiments.
An 8B MLLM reaches state-of-the-art efficiency and performance under 30B by combining a unified 3D resampler, joint document-text training, and hybrid RL for reasoning modes.
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| First computed | 2026-05-17T23:38:50.810052Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/42WE65LJACTHJRNRQPEDHHNNVL \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: e6ac4f756900a674c5b183c8339dadaaf431922b66d39b98d7b5444ed9656249
Canonical record JSON
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