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DualTime-8B release

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.gitattributes ADDED
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README.md ADDED
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+ ---
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+ license: mit
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+ base_model: Qwen/Qwen3-VL-8B-Instruct
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+ library_name: peft
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+ tags: [time-series, multimodal, qwen3-vl, dinov3, lora]
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+ ---
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+
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+ # DualTime-8B
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+
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+ <p align="center">
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+ <a href="#"><img src="https://img.shields.io/badge/arXiv-coming%20soon-b31b1b.svg" alt="arXiv"/></a>
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+ <a href="https://github.com/azencot-group/dual-time"><img src="https://img.shields.io/badge/GitHub-Code-181717?logo=github" alt="Code"/></a>
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+ <a href="https://huggingface.co/datasets/SequentialLearning/DualTime-data"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Data-yellow" alt="Data"/></a>
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+ </p>
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+
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+ The reference checkpoint of *Twice Upon a Time: Complementary Representations
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+ for Time-Series Reasoning*: Qwen3-VL-8B-Instruct reading every time series
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+ twice, as a line plot through its own vision encoder and as a delay-embedding
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+ image through a second DINOv3 ViT-L/16 tower. Two adapters, applied in this
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+ order on top of the public weights:
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+
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+ | path | what |
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+ |---|---|
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+ | `stageA/best/` | Stage A prior: LoRA on the DINOv3 tower and the trained mergers |
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+ | `stageB/checkpoint-5714/` | Stage B language-model LoRA |
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+
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+ Base model: `Qwen/Qwen3-VL-8B-Instruct`. Second tower:
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+ `facebook/dinov3-vitl16-pretrain-lvd1689m` (not redistributed here).
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+
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+ ## Usage
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+
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+ Training, evaluation and the loading code are in the code repository; its
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+ evaluation section downloads these adapters and reproduces the paper's
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+ numbers:
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+
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+ ```bash
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+ bash scripts/download_checkpoints.sh # -> $QTSX_CKPT/DualTime-8B
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+ export A_CKPT=$QTSX_CKPT/DualTime-8B/stageA/best
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+ export B_CKPT=$QTSX_CKPT/DualTime-8B/stageB/checkpoint-5714
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+ KIND=all bash scripts/eval.sh
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+ ```
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+
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+ To load the model directly, clone the code repository and run from its
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+ root (the `src` package below is ours; `load_qwen3vl_dual` builds Qwen3-VL
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+ with the second DINOv3 tower attached, downloading
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+ `facebook/dinov3-vitl16-pretrain-lvd1689m`, a gated repository: accept its
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+ licence on the Hub and `hf auth login` first):
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+
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+ ```python
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+ from peft import PeftModel
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+ from src.model.model_patch import load_qwen3vl_dual # this project's code repository
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+
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+ model, processor = load_qwen3vl_dual("Qwen/Qwen3-VL-8B-Instruct") # bf16, sdpa attention
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+ model = model.to("cuda")
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+ model = PeftModel.from_pretrained(model, "stageA/best").merge_and_unload() # 1. Stage A prior
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+ model = PeftModel.from_pretrained(model, "stageB/checkpoint-5714").merge_and_unload() # 2. Stage B LoRA
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+ model.eval()
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+ ```
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+
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+ The order matters: the Stage B LoRA was trained on top of the merged Stage A
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+ prior. Prompts go through
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+ `src.datasets_utils.unified_collator.UnifiedDualCollator`, which renders each
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+ `<ts><ts/>` marker into the line plot and the delay image; the evaluators in
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+ the code repository do all of this.
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+
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+ ## Example
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+
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+ A synthetic series, a sawtooth wave (period 40) plus a square wave (period
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+ 128), asked a TimeSeriesExam-style multiple-choice question.
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+
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+ ```python
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+ import numpy as np, torch
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+ from scipy import signal
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+ from src.datasets_utils.unified_collator import UnifiedDualCollator
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+ from src.datasets_utils.prompts import SYSTEM, user_text # the multiple-choice prompt format the model was trained on
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+
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+ collator = UnifiedDualCollator(processor=processor, max_length=4096, chart_style="v2")
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+
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+ def ask(series, question, system=None, max_new_tokens=256):
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+ # "<ts><ts/>" marks where the series goes; the collator renders it as the line plot
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+ # for the Qwen ViT and the delay-embedding image for the DINOv3 tower.
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+ example = {"system": system, "user_text": "<ts><ts/>\n" + question, "target_text": "",
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+ "ts_list": [np.asarray(series, dtype=np.float32)], "ts_field_names": ["series"],
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+ "task": "demo", "meta": {}}
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+ inputs = collator([example], for_generation=True)
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+ inputs = {k: (v.to(model.device) if hasattr(v, "to") else v) for k, v in inputs.items()}
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+ with torch.no_grad():
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+ out = model.generate(**inputs, do_sample=False, max_new_tokens=max_new_tokens)
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+ return processor.tokenizer.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True).strip()
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+
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+ t = np.arange(512)
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+ series = signal.sawtooth(2 * np.pi * t / 40) + 2 * signal.square(2 * np.pi * t / 128)
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+
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+ question = "Which additive combination of patterns best describes the time series?"
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+ options = ["SineWave + SquareWave", "SawtoothWave + SquareWave", "SineWave + SawtoothWave"]
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+ print(ask(series, user_text(question, options), system=SYSTEM, max_new_tokens=8))
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+ ```
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+
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+ What the model sees, the line plot (Qwen ViT) and the delay-embedding image
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+ of the same series (DINOv3 tower), and its answer:
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+
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+ <table><tr>
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+ <td><img src="example_plot.png" alt="line plot" width="560"/></td>
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+ <td><img src="example_delay.png" alt="delay embedding" width="200"/></td>
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+ </tr></table>
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+
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+ ```
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+ B
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+ ```
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+
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+ ## Results
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+
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+ TSExam 0.899, TSRBench 53.2, ChatTS alignment A 0.864 / 0.805 and B
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+ 0.893 / 0.868 (categorical / numerical). The full tables are in the paper and
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+ the code repository.
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+ "lstrip": false,
120
+ "normalized": false,
121
+ "rstrip": false,
122
+ "single_word": false,
123
+ "special": false
124
+ },
125
+ "151658": {
126
+ "content": "</tool_call>",
127
+ "lstrip": false,
128
+ "normalized": false,
129
+ "rstrip": false,
130
+ "single_word": false,
131
+ "special": false
132
+ },
133
+ "151659": {
134
+ "content": "<|fim_prefix|>",
135
+ "lstrip": false,
136
+ "normalized": false,
137
+ "rstrip": false,
138
+ "single_word": false,
139
+ "special": false
140
+ },
141
+ "151660": {
142
+ "content": "<|fim_middle|>",
143
+ "lstrip": false,
144
+ "normalized": false,
145
+ "rstrip": false,
146
+ "single_word": false,
147
+ "special": false
148
+ },
149
+ "151661": {
150
+ "content": "<|fim_suffix|>",
151
+ "lstrip": false,
152
+ "normalized": false,
153
+ "rstrip": false,
154
+ "single_word": false,
155
+ "special": false
156
+ },
157
+ "151662": {
158
+ "content": "<|fim_pad|>",
159
+ "lstrip": false,
160
+ "normalized": false,
161
+ "rstrip": false,
162
+ "single_word": false,
163
+ "special": false
164
+ },
165
+ "151663": {
166
+ "content": "<|repo_name|>",
167
+ "lstrip": false,
168
+ "normalized": false,
169
+ "rstrip": false,
170
+ "single_word": false,
171
+ "special": false
172
+ },
173
+ "151664": {
174
+ "content": "<|file_sep|>",
175
+ "lstrip": false,
176
+ "normalized": false,
177
+ "rstrip": false,
178
+ "single_word": false,
179
+ "special": false
180
+ },
181
+ "151665": {
182
+ "content": "<tool_response>",
183
+ "lstrip": false,
184
+ "normalized": false,
185
+ "rstrip": false,
186
+ "single_word": false,
187
+ "special": false
188
+ },
189
+ "151666": {
190
+ "content": "</tool_response>",
191
+ "lstrip": false,
192
+ "normalized": false,
193
+ "rstrip": false,
194
+ "single_word": false,
195
+ "special": false
196
+ },
197
+ "151667": {
198
+ "content": "<think>",
199
+ "lstrip": false,
200
+ "normalized": false,
201
+ "rstrip": false,
202
+ "single_word": false,
203
+ "special": false
204
+ },
205
+ "151668": {
206
+ "content": "</think>",
207
+ "lstrip": false,
208
+ "normalized": false,
209
+ "rstrip": false,
210
+ "single_word": false,
211
+ "special": false
212
+ }
213
+ },
214
+ "additional_special_tokens": [
215
+ "<|im_start|>",
216
+ "<|im_end|>",
217
+ "<|object_ref_start|>",
218
+ "<|object_ref_end|>",
219
+ "<|box_start|>",
220
+ "<|box_end|>",
221
+ "<|quad_start|>",
222
+ "<|quad_end|>",
223
+ "<|vision_start|>",
224
+ "<|vision_end|>",
225
+ "<|vision_pad|>",
226
+ "<|image_pad|>",
227
+ "<|video_pad|>"
228
+ ],
229
+ "bos_token": null,
230
+ "clean_up_tokenization_spaces": false,
231
+ "eos_token": "<|im_end|>",
232
+ "errors": "replace",
233
+ "extra_special_tokens": {},
234
+ "model_max_length": 262144,
235
+ "pad_token": "<|endoftext|>",
236
+ "split_special_tokens": false,
237
+ "tokenizer_class": "Qwen2Tokenizer",
238
+ "unk_token": null
239
+ }
stageB/checkpoint-5714/vocab.json ADDED
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