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aicity2026-track3-7-8
jxb1st/aicity2026-track3-7-8
aicity2026-track3-7-8 is a machine learning model from jxb1st. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft.
LoRA adapters for our AI City 2026 Track 3 video-QA system. Code: https://github.com/jxb1st/aicity2026-track3-7-8
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Updated Aug 8, 2026
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At a glance
- Library
- peft
- Access
- Public
- Created
- Aug 8, 2026
- Updated
- Aug 8, 2026
- SHA
- 4640f4f2
Model card
From the Hugging Face model README
AI City Challenge 2026 - Track 3 (TAR / PSI / FETV) LoRA adapters
LoRA adapters for our AI City 2026 Track 3 video-QA system. Code: https://github.com/jxb1st/aicity2026-track3-7-8
| Subfolder | Base model | Track | Val |
|---|---|---|---|
psi_cosmos7b | nvidia/Cosmos-Reason1-7B | PSI (Track 8) | ~74 |
psi_qwen3vl32 | Qwen/Qwen3-VL-32B-Instruct | PSI (Track 8) | 70.5 |
psi_cosmos3nano | Cosmos3-Nano-VLM | PSI (Track 8) | 72.45 |
psi_unified_qwen3vl32 | Qwen/Qwen3-VL-32B-Instruct | Unified TAR+PSI | 66.23 |
tar_qwen3vl8b_generalist | Qwen/Qwen3-VL-8B-Instruct | TAR (Track 3) | pipeline ~0.6144 |
tar_qwen3vl8b_temporal | Qwen/Qwen3-VL-8B-Instruct | TAR (Track 3) | temporal specialist |
tar_qwen3vl8b_mcq | Qwen/Qwen3-VL-8B-Instruct | TAR (Track 3) | MCQ specialist |
fetv_qwen3vl32_pseudo | Qwen/Qwen3-VL-32B-Instruct | FETV (Track 7) | - |
The TAR specialists (tar_qwen3vl8b_*) stack on the merged generalist; see the repo tar/ README.
- Library
- peft
- Created
- Aug 8, 2026
- Updated
- Aug 8, 2026