backup 02_lr2e-4 pre-cleanup

.gitattributes

@@ -129,3 +129,5 @@ _DRYRUN3/02_single_ar/last/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 01_llada3e-4_qwen3e-4/01_qwen_body/periodic-step3000/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 01_llada3e-4_qwen3e-4/01_qwen_body/step100-val0.7227/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 01_llada3e-4_qwen3e-4/01_qwen_body/step50-val0.6665/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+02_lr2e-4/02_single_ar/step100-val1.0025/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+02_lr2e-4/02_single_ar/step50-val0.9883/tokenizer.json filter=lfs diff=lfs merge=lfs -text

02_lr2e-4/02_single_ar.log

@@ -0,0 +1,243 @@
+trainable params: 269,936,128 || all params: 1,580,276,736 || trainable%: 17.0816
+val set: 500 samples  eval_interval=50  eval_batches=20
+[qwen] step 20/3375 loss=1.4537 lr=4.00e-05 elapsed=15s
+[qwen] step 40/3375 loss=1.1372 lr=8.00e-05 elapsed=28s
+[qwen] step 50 val_loss=0.9883
+/home/jgai/miniconda3/envs/diffar/lib/python3.10/site-packages/peft/utils/save_and_load.py:300: UserWarning: Setting `save_embedding_layers` to `True` as the embedding layer has been resized during finetuning.
+  warnings.warn(
+[topk] +save step=50 val=0.9883 (top-1/2)
+[qwen] step 60/3375 loss=0.9140 lr=1.20e-04 elapsed=47s
+[qwen] step 80/3375 loss=0.8483 lr=1.60e-04 elapsed=60s
+[qwen] step 100/3375 loss=1.2447 lr=2.00e-04 elapsed=73s
+[qwen] step 100 val_loss=1.0025
+[topk] +save step=100 val=1.0025 (top-2/2)
+[qwen] step 120/3375 loss=0.8903 lr=2.00e-04 elapsed=92s
+[qwen] step 140/3375 loss=0.9677 lr=2.00e-04 elapsed=106s
+[qwen] step 150 val_loss=1.0269
+[qwen] step 160/3375 loss=0.8818 lr=2.00e-04 elapsed=123s
+[qwen] step 180/3375 loss=1.0244 lr=2.00e-04 elapsed=136s
+[qwen] step 200/3375 loss=1.1301 lr=2.00e-04 elapsed=149s
+[qwen] step 200 val_loss=1.0427
+[qwen] step 220/3375 loss=1.2421 lr=1.99e-04 elapsed=166s
+[qwen] step 240/3375 loss=1.1121 lr=1.99e-04 elapsed=179s
+[qwen] step 250 val_loss=1.0646
+[qwen] step 260/3375 loss=0.9831 lr=1.99e-04 elapsed=196s
+[qwen] step 280/3375 loss=1.1533 lr=1.99e-04 elapsed=209s
+[qwen] step 300/3375 loss=1.1168 lr=1.98e-04 elapsed=222s
+[qwen] step 300 val_loss=1.1228
+[qwen] step 320/3375 loss=1.4393 lr=1.98e-04 elapsed=239s
+[qwen] step 340/3375 loss=1.1998 lr=1.97e-04 elapsed=253s
+[qwen] step 350 val_loss=1.1543
+[qwen] step 360/3375 loss=1.1141 lr=1.97e-04 elapsed=270s
+[qwen] step 380/3375 loss=0.9754 lr=1.96e-04 elapsed=283s
+[qwen] step 400/3375 loss=1.1409 lr=1.96e-04 elapsed=296s
+[qwen] step 400 val_loss=1.1501
+[qwen] step 420/3375 loss=1.2336 lr=1.95e-04 elapsed=313s
+[qwen] step 440/3375 loss=1.1110 lr=1.95e-04 elapsed=326s
+[qwen] step 450 val_loss=1.1601
+[qwen] step 460/3375 loss=1.0750 lr=1.94e-04 elapsed=343s
+[qwen] step 480/3375 loss=1.3495 lr=1.93e-04 elapsed=356s
+[qwen] step 500/3375 loss=1.2462 lr=1.93e-04 elapsed=369s
+[qwen] step 500 val_loss=1.1908
+[qwen] step 520/3375 loss=1.2390 lr=1.92e-04 elapsed=387s
+[qwen] step 540/3375 loss=1.3574 lr=1.91e-04 elapsed=400s
+[qwen] step 550 val_loss=1.2341
+[qwen] step 560/3375 loss=1.2452 lr=1.90e-04 elapsed=417s
+[qwen] step 580/3375 loss=1.0924 lr=1.90e-04 elapsed=430s
+[qwen] step 600/3375 loss=1.1892 lr=1.89e-04 elapsed=443s
+[qwen] step 600 val_loss=1.2004
+[qwen] step 620/3375 loss=1.2295 lr=1.88e-04 elapsed=460s
+[qwen] step 640/3375 loss=1.0972 lr=1.87e-04 elapsed=473s
+[qwen] step 650 val_loss=1.2291
+[qwen] step 660/3375 loss=1.3711 lr=1.86e-04 elapsed=490s
+[qwen] step 680/3375 loss=1.1601 lr=1.85e-04 elapsed=503s
+[qwen] step 700/3375 loss=1.2286 lr=1.84e-04 elapsed=517s
+[qwen] step 700 val_loss=1.2262
+[qwen] step 720/3375 loss=1.2472 lr=1.83e-04 elapsed=534s
+[qwen] step 740/3375 loss=1.4153 lr=1.82e-04 elapsed=547s
+[qwen] step 750 val_loss=1.2386
+[qwen] step 760/3375 loss=1.3557 lr=1.81e-04 elapsed=564s
+[qwen] step 780/3375 loss=1.4020 lr=1.79e-04 elapsed=577s
+[qwen] step 800/3375 loss=1.4806 lr=1.78e-04 elapsed=590s
+[qwen] step 800 val_loss=1.2390
+[qwen] step 820/3375 loss=1.3951 lr=1.77e-04 elapsed=607s
+[qwen] step 840/3375 loss=1.0590 lr=1.76e-04 elapsed=620s
+[qwen] step 850 val_loss=1.2599
+[qwen] step 860/3375 loss=1.5092 lr=1.75e-04 elapsed=638s
+[qwen] step 880/3375 loss=1.2216 lr=1.73e-04 elapsed=651s
+[qwen] step 900/3375 loss=1.0819 lr=1.72e-04 elapsed=664s
+[qwen] step 900 val_loss=1.2727
+[qwen] step 920/3375 loss=1.4366 lr=1.71e-04 elapsed=681s
+[qwen] step 940/3375 loss=1.3029 lr=1.69e-04 elapsed=694s
+[qwen] step 950 val_loss=1.2912
+[qwen] step 960/3375 loss=1.4412 lr=1.68e-04 elapsed=711s
+[qwen] step 980/3375 loss=1.3547 lr=1.66e-04 elapsed=725s
+[qwen] step 1000/3375 loss=1.3509 lr=1.65e-04 elapsed=738s
+[qwen] step 1000 val_loss=1.2792
+[qwen] step 1020/3375 loss=1.1238 lr=1.64e-04 elapsed=755s
+[qwen] step 1040/3375 loss=1.3718 lr=1.62e-04 elapsed=768s
+[qwen] step 1050 val_loss=1.2756
+[qwen] step 1060/3375 loss=1.3287 lr=1.61e-04 elapsed=785s
+[qwen] step 1080/3375 loss=1.3912 lr=1.59e-04 elapsed=798s
+[qwen] step 1100/3375 loss=1.3113 lr=1.57e-04 elapsed=811s
+[qwen] step 1100 val_loss=1.2876
+[qwen] step 1120/3375 loss=1.3817 lr=1.56e-04 elapsed=829s
+[qwen] step 1140/3375 loss=1.3074 lr=1.54e-04 elapsed=842s
+[qwen] step 1150 val_loss=1.2899
+[qwen] step 1160/3375 loss=1.3559 lr=1.53e-04 elapsed=859s
+[qwen] step 1180/3375 loss=1.3667 lr=1.51e-04 elapsed=872s
+[qwen] step 1200/3375 loss=1.2641 lr=1.49e-04 elapsed=885s
+[qwen] step 1200 val_loss=1.2895
+[qwen] step 1220/3375 loss=1.2989 lr=1.48e-04 elapsed=902s
+[qwen] step 1240/3375 loss=1.4462 lr=1.46e-04 elapsed=915s
+[qwen] step 1250 val_loss=1.2965
+[qwen] step 1260/3375 loss=1.2883 lr=1.44e-04 elapsed=932s
+[qwen] step 1280/3375 loss=1.2552 lr=1.42e-04 elapsed=945s
+[qwen] step 1300/3375 loss=1.1436 lr=1.41e-04 elapsed=959s
+[qwen] step 1300 val_loss=1.3136
+[qwen] step 1320/3375 loss=1.2648 lr=1.39e-04 elapsed=976s
+[qwen] step 1340/3375 loss=1.3910 lr=1.37e-04 elapsed=989s
+[qwen] step 1350 val_loss=1.3282
+[qwen] step 1360/3375 loss=1.3565 lr=1.35e-04 elapsed=1006s
+[qwen] step 1380/3375 loss=1.1782 lr=1.34e-04 elapsed=1019s
+[qwen] step 1400/3375 loss=1.4795 lr=1.32e-04 elapsed=1032s
+[qwen] step 1400 val_loss=1.3184
+[qwen] step 1420/3375 loss=1.6362 lr=1.30e-04 elapsed=1049s
+[qwen] step 1440/3375 loss=1.2179 lr=1.28e-04 elapsed=1062s
+[qwen] step 1450 val_loss=1.3086
+[qwen] step 1460/3375 loss=1.2888 lr=1.26e-04 elapsed=1080s
+[qwen] step 1480/3375 loss=1.3422 lr=1.24e-04 elapsed=1093s
+[qwen] step 1500/3375 loss=1.5065 lr=1.23e-04 elapsed=1106s
+[qwen] step 1500 val_loss=1.3138
+[qwen] step 1520/3375 loss=1.3482 lr=1.21e-04 elapsed=1123s
+[qwen] step 1540/3375 loss=1.3934 lr=1.19e-04 elapsed=1136s
+[qwen] step 1550 val_loss=1.3221
+[qwen] step 1560/3375 loss=1.3778 lr=1.17e-04 elapsed=1153s
+[qwen] step 1580/3375 loss=1.3863 lr=1.15e-04 elapsed=1166s
+[qwen] step 1600/3375 loss=1.3966 lr=1.13e-04 elapsed=1179s
+[qwen] step 1600 val_loss=1.3248
+[qwen] step 1620/3375 loss=1.2380 lr=1.11e-04 elapsed=1197s
+[qwen] step 1640/3375 loss=1.1116 lr=1.09e-04 elapsed=1210s
+[qwen] step 1650 val_loss=1.3187
+[qwen] step 1660/3375 loss=1.2834 lr=1.07e-04 elapsed=1227s
+[qwen] step 1680/3375 loss=1.4427 lr=1.06e-04 elapsed=1240s
+[qwen] step 1700/3375 loss=1.1298 lr=1.04e-04 elapsed=1253s
+[qwen] step 1700 val_loss=1.3172
+[qwen] step 1720/3375 loss=1.2671 lr=1.02e-04 elapsed=1270s
+[qwen] step 1740/3375 loss=1.4189 lr=9.98e-05 elapsed=1283s
+[qwen] step 1750 val_loss=1.3274
+[qwen] step 1760/3375 loss=1.1769 lr=9.78e-05 elapsed=1301s
+[qwen] step 1780/3375 loss=1.5192 lr=9.59e-05 elapsed=1314s
+[qwen] step 1800/3375 loss=1.4004 lr=9.40e-05 elapsed=1327s
+[qwen] step 1800 val_loss=1.3293
+[qwen] step 1820/3375 loss=1.2495 lr=9.21e-05 elapsed=1344s
+[qwen] step 1840/3375 loss=1.4134 lr=9.02e-05 elapsed=1357s
+[qwen] step 1850 val_loss=1.3086
+[qwen] step 1860/3375 loss=1.2312 lr=8.83e-05 elapsed=1374s
+[qwen] step 1880/3375 loss=1.2386 lr=8.64e-05 elapsed=1387s
+[qwen] step 1900/3375 loss=1.4536 lr=8.45e-05 elapsed=1400s
+[qwen] step 1900 val_loss=1.2963
+[qwen] step 1920/3375 loss=1.1664 lr=8.26e-05 elapsed=1417s
+[qwen] step 1940/3375 loss=1.1863 lr=8.07e-05 elapsed=1431s
+[qwen] step 1950 val_loss=1.2917
+[qwen] step 1960/3375 loss=1.4012 lr=7.88e-05 elapsed=1448s
+[qwen] step 1980/3375 loss=1.2864 lr=7.69e-05 elapsed=1461s
+[qwen] step 2000/3375 loss=1.4254 lr=7.51e-05 elapsed=1474s
+[qwen] step 2000 val_loss=1.2892
+[qwen] step 2020/3375 loss=1.2463 lr=7.32e-05 elapsed=1491s
+[qwen] step 2040/3375 loss=1.2330 lr=7.14e-05 elapsed=1504s
+[qwen] step 2050 val_loss=1.2848
+[qwen] step 2060/3375 loss=1.3204 lr=6.96e-05 elapsed=1521s
+[qwen] step 2080/3375 loss=1.1087 lr=6.77e-05 elapsed=1534s
+[qwen] step 2100/3375 loss=1.3137 lr=6.59e-05 elapsed=1547s
+[qwen] step 2100 val_loss=1.2806
+[qwen] step 2120/3375 loss=1.1110 lr=6.41e-05 elapsed=1565s
+[qwen] step 2140/3375 loss=1.3364 lr=6.23e-05 elapsed=1578s
+[qwen] step 2150 val_loss=1.2771
+[qwen] step 2160/3375 loss=1.2026 lr=6.06e-05 elapsed=1595s
+[qwen] step 2180/3375 loss=1.5130 lr=5.88e-05 elapsed=1608s
+[qwen] step 2200/3375 loss=1.3101 lr=5.71e-05 elapsed=1621s
+[qwen] step 2200 val_loss=1.2735
+[qwen] step 2220/3375 loss=1.1430 lr=5.54e-05 elapsed=1638s
+[qwen] step 2240/3375 loss=1.4382 lr=5.36e-05 elapsed=1651s
+[qwen] step 2250 val_loss=1.2697
+[qwen] step 2260/3375 loss=1.2799 lr=5.20e-05 elapsed=1668s
+[qwen] step 2280/3375 loss=1.2135 lr=5.03e-05 elapsed=1681s
+[qwen] step 2300/3375 loss=1.5658 lr=4.86e-05 elapsed=1694s
+[qwen] step 2300 val_loss=1.2722
+[qwen] step 2320/3375 loss=1.0929 lr=4.70e-05 elapsed=1712s
+[qwen] step 2340/3375 loss=1.3269 lr=4.54e-05 elapsed=1725s
+[qwen] step 2350 val_loss=1.2670
+[qwen] step 2360/3375 loss=1.1419 lr=4.38e-05 elapsed=1742s
+[qwen] step 2380/3375 loss=1.1317 lr=4.22e-05 elapsed=1755s
+[qwen] step 2400/3375 loss=1.3087 lr=4.06e-05 elapsed=1768s
+[qwen] step 2400 val_loss=1.2614
+[qwen] step 2420/3375 loss=1.3487 lr=3.91e-05 elapsed=1785s
+[qwen] step 2440/3375 loss=1.3098 lr=3.76e-05 elapsed=1798s
+[qwen] step 2450 val_loss=1.2615
+[qwen] step 2460/3375 loss=1.0641 lr=3.61e-05 elapsed=1816s
+[qwen] step 2480/3375 loss=1.1441 lr=3.46e-05 elapsed=1829s
+[qwen] step 2500/3375 loss=1.4658 lr=3.32e-05 elapsed=1842s
+[qwen] step 2500 val_loss=1.2598
+[qwen] step 2520/3375 loss=1.0757 lr=3.18e-05 elapsed=1859s
+[qwen] step 2540/3375 loss=1.2793 lr=3.04e-05 elapsed=1872s
+[qwen] step 2550 val_loss=1.2584
+[qwen] step 2560/3375 loss=1.1938 lr=2.90e-05 elapsed=1889s
+[qwen] step 2580/3375 loss=1.2766 lr=2.77e-05 elapsed=1902s
+[qwen] step 2600/3375 loss=1.2427 lr=2.64e-05 elapsed=1915s
+[qwen] step 2600 val_loss=1.2535
+[qwen] step 2620/3375 loss=1.3159 lr=2.51e-05 elapsed=1933s
+[qwen] step 2640/3375 loss=1.2127 lr=2.38e-05 elapsed=1946s
+[qwen] step 2650 val_loss=1.2495
+[qwen] step 2660/3375 loss=1.2671 lr=2.26e-05 elapsed=1963s
+[qwen] step 2680/3375 loss=1.3560 lr=2.14e-05 elapsed=1976s
+[qwen] step 2700/3375 loss=1.3448 lr=2.02e-05 elapsed=1989s
+[qwen] step 2700 val_loss=1.2487
+[qwen] step 2720/3375 loss=1.4807 lr=2.00e-05 elapsed=2006s
+[qwen] step 2740/3375 loss=1.2928 lr=2.00e-05 elapsed=2020s
+[qwen] step 2750 val_loss=1.2438
+[qwen] step 2760/3375 loss=1.4174 lr=2.00e-05 elapsed=2037s
+[qwen] step 2780/3375 loss=1.1116 lr=2.00e-05 elapsed=2050s
+[qwen] step 2800/3375 loss=1.3646 lr=2.00e-05 elapsed=2063s
+[qwen] step 2800 val_loss=1.2422
+[qwen] step 2820/3375 loss=1.1873 lr=2.00e-05 elapsed=2080s
+[qwen] step 2840/3375 loss=1.2777 lr=2.00e-05 elapsed=2093s
+[qwen] step 2850 val_loss=1.2417
+[qwen] step 2860/3375 loss=1.2430 lr=2.00e-05 elapsed=2110s
+[qwen] step 2880/3375 loss=1.3079 lr=2.00e-05 elapsed=2123s
+[qwen] step 2900/3375 loss=1.1310 lr=2.00e-05 elapsed=2136s
+[qwen] step 2900 val_loss=1.2389
+[qwen] step 2920/3375 loss=1.0678 lr=2.00e-05 elapsed=2154s
+[qwen] step 2940/3375 loss=1.4525 lr=2.00e-05 elapsed=2167s
+[qwen] step 2950 val_loss=1.2381
+[qwen] step 2960/3375 loss=1.2042 lr=2.00e-05 elapsed=2184s
+[qwen] step 2980/3375 loss=0.9134 lr=2.00e-05 elapsed=2197s
+[qwen] step 3000/3375 loss=1.2430 lr=2.00e-05 elapsed=2210s
+[qwen] step 3000 val_loss=1.2379
+[qwen] step 3020/3375 loss=1.3611 lr=2.00e-05 elapsed=2227s
+[qwen] step 3040/3375 loss=1.4916 lr=2.00e-05 elapsed=2240s
+[qwen] step 3050 val_loss=1.2373
+[qwen] step 3060/3375 loss=1.2767 lr=2.00e-05 elapsed=2257s
+[qwen] step 3080/3375 loss=1.2944 lr=2.00e-05 elapsed=2271s
+[qwen] step 3100/3375 loss=1.5489 lr=2.00e-05 elapsed=2284s
+[qwen] step 3100 val_loss=1.2352
+[qwen] step 3120/3375 loss=1.3566 lr=2.00e-05 elapsed=2301s
+[qwen] step 3140/3375 loss=1.3777 lr=2.00e-05 elapsed=2314s
+[qwen] step 3150 val_loss=1.2327
+[qwen] step 3160/3375 loss=1.3102 lr=2.00e-05 elapsed=2331s
+[qwen] step 3180/3375 loss=1.3609 lr=2.00e-05 elapsed=2344s
+[qwen] step 3200/3375 loss=1.1203 lr=2.00e-05 elapsed=2357s
+[qwen] step 3200 val_loss=1.2295
+[qwen] step 3220/3375 loss=1.2794 lr=2.00e-05 elapsed=2374s
+[qwen] step 3240/3375 loss=1.3918 lr=2.00e-05 elapsed=2387s
+[qwen] step 3250 val_loss=1.2313
+[qwen] step 3260/3375 loss=1.3777 lr=2.00e-05 elapsed=2405s
+[qwen] step 3280/3375 loss=1.1877 lr=2.00e-05 elapsed=2418s
+[qwen] step 3300/3375 loss=1.0857 lr=2.00e-05 elapsed=2431s
+[qwen] step 3300 val_loss=1.2278
+[qwen] step 3320/3375 loss=1.3276 lr=2.00e-05 elapsed=2448s
+[qwen] step 3340/3375 loss=1.1286 lr=2.00e-05 elapsed=2461s
+[qwen] step 3350 val_loss=1.2278
+[qwen] step 3360/3375 loss=1.1440 lr=2.00e-05 elapsed=2478s
+[qwen] FINAL val_loss=1.2273
+Done. Best val_loss=1.0025 at step 100  path=/data/user_data/jgai/hybrid-code-gen/ckpt/full/02_lr2e-4/02_single_ar/step100-val1.0025

02_lr2e-4/02_single_ar/config.json

@@ -0,0 +1,31 @@
+{
+  "base_model": "Qwen/Qwen2.5-Coder-1.5B",
+  "train_jsonl": "/data/user_data/jgai/hybrid-code-gen/data/stack10k/train.jsonl",
+  "val_jsonl": "/data/user_data/jgai/hybrid-code-gen/data/stack10k/val.jsonl",
+  "output_dir": "/data/user_data/jgai/hybrid-code-gen/ckpt/full/02_lr2e-4/02_single_ar",
+  "lora_r": 32,
+  "lora_alpha": 64,
+  "lora_dropout": 0.05,
+  "lora_target_modules": [
+    "q_proj",
+    "k_proj",
+    "v_proj",
+    "o_proj",
+    "gate_proj",
+    "up_proj",
+    "down_proj"
+  ],
+  "max_steps": 3375,
+  "batch_size": 1,
+  "grad_accum": 1,
+  "lr": 0.0002,
+  "weight_decay": 0.01,
+  "warmup_steps": 100,
+  "grad_clip": 1.0,
+  "max_seq_len": 2048,
+  "loss_scope": "full",
+  "log_interval": 20,
+  "eval_interval": 50,
+  "save_interval": 1000,
+  "seed": 42
+}

02_lr2e-4/02_single_ar/step100-val1.0025/README.md

@@ -0,0 +1,207 @@
+---
+base_model: Qwen/Qwen2.5-Coder-1.5B
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:Qwen/Qwen2.5-Coder-1.5B
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.17.0

02_lr2e-4/02_single_ar/step100-val1.0025/adapter_config.json

@@ -0,0 +1,42 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen2.5-Coder-1.5B",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "down_proj",
+    "up_proj",
+    "v_proj",
+    "k_proj",
+    "q_proj",
+    "o_proj",
+    "gate_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

02_lr2e-4/02_single_ar/step100-val1.0025/adapter_model.safetensors

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+oid sha256:ab1f0fa2c8956315e741358b1d84df2eba9ed2c6b89e49ad2204e67f34672e5a
+size 1079797096

02_lr2e-4/02_single_ar/step100-val1.0025/added_tokens.json

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+{
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+  "<tool_call>": 151657,
+  "<|body_0|>": 151667,
+  "<|body_1|>": 151668,
+  "<|body_2|>": 151669,
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+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
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+  "<|end_body_8|>": 151695,
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+  "<|end_skeleton|>": 151666,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
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+}

02_lr2e-4/02_single_ar/step100-val1.0025/chat_template.jinja

@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

02_lr2e-4/02_single_ar/step100-val1.0025/special_tokens_map.json

@@ -0,0 +1,242 @@
+{
+  "additional_special_tokens": [
+    {
+      "content": "<|skeleton|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|end_skeleton|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_0|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_1|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_2|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_3|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_4|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_5|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_6|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_7|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_8|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_9|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
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+      "content": "<|body_start_0|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
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+      "single_word": false
+    },
+    {
+      "content": "<|end_body_9|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    }
+  ],
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

02_lr2e-4/02_single_ar/step100-val1.0025/tokenizer.json

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+oid sha256:e418905c4788c5f5ea0dd1ca15c904591f50fce71e16fc311f1b4d84e7ed50d8
+size 11428087

02_lr2e-4/02_single_ar/step100-val1.0025/tokenizer_config.json

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+  "added_tokens_decoder": {
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+    }
+  },
+  "additional_special_tokens": [
+    "<|skeleton|>",
+    "<|end_skeleton|>",
+    "<|body_0|>",
+    "<|body_1|>",
+    "<|body_2|>",
+    "<|body_3|>",
+    "<|body_4|>",
+    "<|body_5|>",
+    "<|body_6|>",
+    "<|body_7|>",
+    "<|body_8|>",
+    "<|body_9|>",
+    "<|body_start_0|>",
+    "<|body_start_1|>",
+    "<|body_start_2|>",
+    "<|body_start_3|>",
+    "<|body_start_4|>",
+    "<|body_start_5|>",
+    "<|body_start_6|>",
+    "<|body_start_7|>",
+    "<|body_start_8|>",
+    "<|body_start_9|>",
+    "<|end_body_0|>",
+    "<|end_body_1|>",
+    "<|end_body_2|>",
+    "<|end_body_3|>",
+    "<|end_body_4|>",
+    "<|end_body_5|>",
+    "<|end_body_6|>",
+    "<|end_body_7|>",
+    "<|end_body_8|>",
+    "<|end_body_9|>"
+  ],
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

02_lr2e-4/02_single_ar/step50-val0.9883/README.md

@@ -0,0 +1,207 @@
+---
+base_model: Qwen/Qwen2.5-Coder-1.5B
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:Qwen/Qwen2.5-Coder-1.5B
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.17.0

02_lr2e-4/02_single_ar/step50-val0.9883/adapter_config.json

@@ -0,0 +1,42 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen2.5-Coder-1.5B",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "down_proj",
+    "up_proj",
+    "v_proj",
+    "k_proj",
+    "q_proj",
+    "o_proj",
+    "gate_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

02_lr2e-4/02_single_ar/step50-val0.9883/adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c2766251903a846ca9d530ee155b11492300064a8ffb5e186789497d37dc06d
+size 1079797096

02_lr2e-4/02_single_ar/step50-val0.9883/added_tokens.json

@@ -0,0 +1,56 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|body_0|>": 151667,
+  "<|body_1|>": 151668,
+  "<|body_2|>": 151669,
+  "<|body_3|>": 151670,
+  "<|body_4|>": 151671,
+  "<|body_5|>": 151672,
+  "<|body_6|>": 151673,
+  "<|body_7|>": 151674,
+  "<|body_8|>": 151675,
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+  "<|body_start_0|>": 151677,
+  "<|body_start_1|>": 151678,
+  "<|body_start_2|>": 151679,
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+  "<|body_start_4|>": 151681,
+  "<|body_start_5|>": 151682,
+  "<|body_start_6|>": 151683,
+  "<|body_start_7|>": 151684,
+  "<|body_start_8|>": 151685,
+  "<|body_start_9|>": 151686,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|end_body_0|>": 151687,
+  "<|end_body_1|>": 151688,
+  "<|end_body_2|>": 151689,
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+  "<|end_body_4|>": 151691,
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+  "<|end_body_6|>": 151693,
+  "<|end_body_7|>": 151694,
+  "<|end_body_8|>": 151695,
+  "<|end_body_9|>": 151696,
+  "<|end_skeleton|>": 151666,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
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+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

02_lr2e-4/02_single_ar/step50-val0.9883/chat_template.jinja

@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

02_lr2e-4/02_single_ar/step50-val0.9883/special_tokens_map.json

@@ -0,0 +1,242 @@
+{
+  "additional_special_tokens": [
+    {
+      "content": "<|skeleton|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|end_skeleton|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_0|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_1|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_2|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_3|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_4|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_5|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_6|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_7|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_8|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|body_9|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
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02_lr2e-4/02_single_ar/topk_val.json

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