Instructions to use TimeDuet/TimeDuet-Qwen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use TimeDuet/TimeDuet-Qwen with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Omni-7B") model = PeftModel.from_pretrained(base_model, "TimeDuet/TimeDuet-Qwen") - Notebooks
- Google Colab
- Kaggle
File size: 2,588 Bytes
fd62e2c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | {
"policy_id": "avrtg.r7.common.max1024.v1",
"status": "canonical_for_new_reevaluations",
"split": "R7 held-out test",
"expected_queries": 2202,
"data": {
"validation_sha256": "960e2a04502418f44ea23ca0a591609995379f1c636b7a6db80c017c4cf7237a",
"validation_query_instances": 2202
},
"prompt": {
"name": "canonical union-localization prompt",
"system": "You are an audio-visual temporal grounding model.",
"user_template": "<video>\nLocalize every time interval where the following event is present\nin the audio stream, the visual stream, or both:\n\n{query}\n\nReturn JSON inside <answer> tags. Use seconds relative to t=0 of the full video.\nReturn all matching intervals in chronological order, merging overlapping or contiguous intervals.\n\nRequired output format:\n<answer>{\"intervals\":[[start_second,end_second], ...]}</answer>"
},
"output_schema": "<answer>{\"intervals\":[[start,end], ...]}</answer>",
"generation": {
"do_sample": false,
"temperature": 0.0,
"top_p": null,
"top_k": null,
"num_beams": 1,
"max_new_tokens": 1024,
"termination": [
"model_native_eos",
"official_conversation_separator"
],
"answer_wrapper_early_stop": false
},
"audio": {
"scope": "full",
"local_format": "16-kHz mono",
"source": "same verified MP4"
},
"video": {
"scope": "full timeline",
"sampling_fps": 1.0,
"timestamps_sec": "0,1,2,...",
"experiment_frame_cap": null,
"experiment_pixel_cap": null,
"spatial_preprocessing": "official model-native processor"
},
"input": {
"video_sampling_fps": 1.0,
"experiment_frame_cap": null,
"experiment_pixel_cap": null,
"full_mp4_audio_required": true,
"use_audio_in_video": true,
"return_audio": false,
"talker_enabled": false,
"batch_size_per_gpu": 1,
"oom_policy": "Fail and record the row; do not silently reduce frames, pixels, audio, or output length."
},
"execution": {
"queries_per_gpu_at_once": 1,
"sharding": "deterministic and disjoint",
"resume": true,
"required_generation_audit": [
"generated_tokens",
"termination_reason",
"cap_hit",
"provider_finish_reason_if_available"
]
},
"parser": "strict/common canonical parser",
"metric_version": "avrtg.evidence-region.v2",
"failure_policy": "no reinference or manual repair; empty prediction and localization zero",
"integrity_requirements": {
"errors": 0,
"duplicates": 0,
"missing": 0,
"extra": 0,
"frame_mismatch": 0,
"empty_audio": 0
}
}
|