Minicpmo45 Matchtime Window v5 LoRA Continue204

MiniCPM-o 4.5 MatchTime Soccer Commentary LoRA

This repository contains the final PEFT LoRA adapter from a small video-only soccer commentary pilot. It adapts openbmb/MiniCPM-o-4_5 using 300 English video-text examples derived from MatchTime/SoccerNet.

Code, configurations, metrics, and compatibility notes: pikaqiu2333/minicpmo45-soccer-commentary-lora

Intended Use

Generate concise English soccer commentary for a 30-second broadcast clip. This adapter is an exploratory research artifact. It is not intended for real-time match reporting or factual use without verification.

Training

• Base revision: 4382fcae8a551b54d18f18462db974ff312aa7f3
• Unique training examples: 300
• Development examples: 100
• LoRA targets: q_proj,v_proj
• Trainable parameters: 3,833,856 (0.0441% of the loaded video-only path)
• Vision tower and multimodal projector frozen
• One pass over 96 examples, then one pass over 204 previously unseen examples
• Learning rate: 1e-5
• Effective batch size: 8
• Precision: bfloat16
• Hardware: Hugging Face Jobs, A100 80 GB

Evaluation

Evaluation used 100 frozen clips from 10 held-out matches, containing 112 reference events.

Metric	Base	LoRA-300
Micro event coverage	41.96%	44.64%
Contradiction rate	28.57%	24.11%
Net event score	13.39%	20.54%

Net event score assigns FULL=1, PARTIAL=0.5, MISS=0, and CONTRADICTION=-1. The LoRA-minus-Base match-cluster bootstrap 95% confidence interval was [+1.30, +12.50] percentage points. The ordinary event-coverage gain was directional; its confidence interval crossed zero.

Inference

The successful deterministic inference environment used transformers==4.51.0:

import torch
from peft import PeftModel
from transformers import AutoModel

base = AutoModel.from_pretrained(
    "openbmb/MiniCPM-o-4_5",
    revision="4382fcae8a551b54d18f18462db974ff312aa7f3",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    attn_implementation="sdpa",
    init_vision=True,
    init_audio=False,
    init_tts=False,
    low_cpu_mem_usage=True,
    device_map="cuda",
)
model = PeftModel.from_pretrained(
    base,
    "pkq233/minicpmo45-matchtime-window-v5-lora-continue204",
).merge_and_unload().eval()

Pass uniformly sampled PIL frames and the training prompt to model.chat with generate_audio=False, enable_thinking=False, and do_sample=False.

Limitations

• Only 300 training examples were used.
• The source annotations contain known timestamp and caption imperfections.
• Most evaluation labels were produced by a text-only reference-grounded judge; only two clips were subsequently adjudicated against video.
• This evaluates fixed-window caption generation, not proactive speaking time.
• SoccerNet videos and MatchTime captions are not redistributed in this model repository. Users must obtain them from the official sources and comply with their terms.

License and Attribution

MiniCPM-o 4.5 is distributed under Apache-2.0. This adapter is published with license: other because the upstream dataset repository does not provide a clear standalone license for redistribution. No source media or annotations are included.