| /home/matt/hf/sqllama-V0/.venv/lib/python3.7/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: /opt/conda did not contain libcudart.so as expected! Searching further paths... | |
| warn(msg) | |
| The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. | |
| The tokenizer class you load from this checkpoint is 'LLaMATokenizer'. | |
| The class this function is called from is 'LlamaTokenizer'. | |
| ===================================BUG REPORT=================================== | |
| Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues | |
| ================================================================================ | |
| CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so | |
| CUDA SETUP: Highest compute capability among GPUs detected: 7.5 | |
| CUDA SETUP: Detected CUDA version 113 | |
| CUDA SETUP: Loading binary /home/matt/hf/sqllama-V0/.venv/lib/python3.7/site-packages/bitsandbytes/libbitsandbytes_cuda113.so... | |
| Output exceeds the size limit. Open the full output data in a text editor | |
| table: 2-16050349-13 | |
| columns: Rank,Name,Team,Games,Points | |
| Q: What is Games, when Points is less than 340, and when Rank is greater than 3? | |
| A: SELECT Games FROM 2-16050349-13 WHERE Points < 340 AND Rank > 3 | |
| END | |
| table: 1-28962227-1 | |
| columns: Series,Premiere,Finale,Runners-up,Winner | |
| Q: What is the date of the finale where Holly Bell was runner-up? | |
| A: SELECT Finale FROM 1-28962227-1 WHERE Runners-up = 'Holly Bell' | |
| END | |
| table: 2-10652530-2 | |
| columns: Week,Date,Opponent,Result,Stadium,Record,Attendance | |
| Q: What was the Browns record after they played the game at the Paul Brown stadium? | |
| A: SELECT Record FROM 2-10652530-2 WHERE Stadium = 'paul brown stadium' | |
| END | |
| table: 2-18379129-4 | |
| columns: play,author,company,base,country | |
| Q: Who is the author of the Play Electra? | |
| ... | |
| Q: What is 02-03, when School Year is % Learning In Latvian? | |
| A: SELECT 02-03 FROM 2-16158579-1 WHERE School year = '% learning in latvian' | |
| END | |
| True | |
| 92 | |
| 0 | |
| count 56355.000000 | |
| mean 101.219519 | |
| std 21.740325 | |
| min 63.000000 | |
| 25% 87.500000 | |
| 50% 97.000000 | |
| 75% 109.000000 | |
| max 461.000000 | |
| 32084 | |
| [500/500 7:38:36, Epoch 1/2] | |
| Step Training Loss | |
| 1 2.748800 | |
| 2 2.723800 | |
| 3 2.737600 | |
| 4 2.707100 | |
| 5 2.692800 | |
| 6 2.720700 | |
| 7 2.681400 | |
| 8 2.736400 | |
| 9 2.701800 | |
| 10 2.711700 | |
| 11 2.685800 | |
| 12 2.684300 | |
| 13 2.686300 | |
| 14 2.698800 | |
| 15 2.659300 | |
| 16 2.688900 | |
| 17 2.661800 | |
| 18 2.677700 | |
| 19 2.647100 | |
| 20 2.679800 | |
| 21 2.652000 | |
| 22 2.628900 | |
| 23 2.656100 | |
| 24 2.669100 | |
| 25 2.667800 | |
| 26 2.636300 | |
| 27 2.616800 | |
| 28 2.630600 | |
| 29 2.621000 | |
| 30 2.602000 | |
| 31 2.607900 | |
| 32 2.635800 | |
| 33 2.594600 | |
| 34 2.604400 | |
| 35 2.618900 | |
| 36 2.563400 | |
| 37 2.589200 | |
| 38 2.552100 | |
| 39 2.583600 | |
| 40 2.554500 | |
| 41 2.557400 | |
| 42 2.536700 | |
| 43 2.535000 | |
| 44 2.557900 | |
| 45 2.530100 | |
| 46 2.527900 | |
| 47 2.510100 | |
| 48 2.539100 | |
| 49 2.500100 | |
| 50 2.536200 | |
| 51 2.487100 | |
| 52 2.521700 | |
| 53 2.532600 | |
| 54 2.494500 | |
| 55 2.468900 | |
| 56 2.468700 | |
| 57 2.474300 | |
| 58 2.480900 | |
| 59 2.442800 | |
| 60 2.472800 | |
| 61 2.452900 | |
| 62 2.452000 | |
| 63 2.443100 | |
| 64 2.446700 | |
| 65 2.415100 | |
| 66 2.376300 | |
| 67 2.411500 | |
| 68 2.403900 | |
| 69 2.383800 | |
| 70 2.427800 | |
| 71 2.419400 | |
| 72 2.371900 | |
| 73 2.364400 | |
| 74 2.360000 | |
| 75 2.337600 | |
| 76 2.332800 | |
| 77 2.315700 | |
| 78 2.344200 | |
| 79 2.331700 | |
| 80 2.303100 | |
| 81 2.324700 | |
| 82 2.285900 | |
| 83 2.268000 | |
| 84 2.260600 | |
| 85 2.286100 | |
| 86 2.233600 | |
| 87 2.266200 | |
| 88 2.217000 | |
| 89 2.249300 | |
| 90 2.239000 | |
| 91 2.221900 | |
| 92 2.223300 | |
| 93 2.179500 | |
| 94 2.204400 | |
| 95 2.193200 | |
| 96 2.163800 | |
| 97 2.158200 | |
| 98 2.127700 | |
| 99 2.141400 | |
| 100 2.121400 | |
| 101 2.115500 | |
| 102 2.125200 | |
| 103 2.140100 | |
| 104 2.118400 | |
| 105 2.110400 | |
| 106 2.097300 | |
| 107 2.071400 | |
| 108 2.083400 | |
| 109 2.090200 | |
| 110 2.078200 | |
| 111 2.061100 | |
| 112 2.047500 | |
| 113 2.006100 | |
| 114 2.023800 | |
| 115 2.014000 | |
| 116 2.008800 | |
| 117 1.988800 | |
| 118 1.984900 | |
| 119 1.971000 | |
| 120 1.924100 | |
| 121 1.953100 | |
| 122 1.957800 | |
| 123 1.952500 | |
| 124 1.890400 | |
| 125 1.915900 | |
| 126 1.901100 | |
| 127 1.879900 | |
| 128 1.834100 | |
| 129 1.855900 | |
| 130 1.853800 | |
| 131 1.869200 | |
| 132 1.821400 | |
| 133 1.835100 | |
| 134 1.817700 | |
| 135 1.785800 | |
| 136 1.764000 | |
| 137 1.796800 | |
| 138 1.751100 | |
| 139 1.756500 | |
| 140 1.789900 | |
| 141 1.773100 | |
| 142 1.729200 | |
| 143 1.700200 | |
| 144 1.721200 | |
| 145 1.690600 | |
| 146 1.687700 | |
| 147 1.743500 | |
| 148 1.690000 | |
| 149 1.687200 | |
| 150 1.663000 | |
| 151 1.648600 | |
| 152 1.667100 | |
| 153 1.665600 | |
| 154 1.647000 | |
| 155 1.629500 | |
| 156 1.620800 | |
| 157 1.616400 | |
| 158 1.658500 | |
| 159 1.593900 | |
| 160 1.604300 | |
| 161 1.621200 | |
| 162 1.607900 | |
| 163 1.591100 | |
| 164 1.598100 | |
| 165 1.579700 | |
| 166 1.545500 | |
| 167 1.582100 | |
| 168 1.568300 | |
| 169 1.557900 | |
| 170 1.561300 | |
| 171 1.521800 | |
| 172 1.542500 | |
| 173 1.502300 | |
| 174 1.513900 | |
| 175 1.501500 | |
| 176 1.551200 | |
| 177 1.495600 | |
| 178 1.504000 | |
| 179 1.512500 | |
| 180 1.488200 | |
| 181 1.492200 | |
| 182 1.494300 | |
| 183 1.494800 | |
| 184 1.446100 | |
| 185 1.514700 | |
| 186 1.450900 | |
| 187 1.476900 | |
| 188 1.447100 | |
| 189 1.490800 | |
| 190 1.433200 | |
| 191 1.438100 | |
| 192 1.410500 | |
| 193 1.422600 | |
| 194 1.405500 | |
| 195 1.439400 | |
| 196 1.448100 | |
| 197 1.410200 | |
| 198 1.403800 | |
| 199 1.464400 | |
| 200 1.417700 | |
| 201 1.419500 | |
| 202 1.419400 | |
| 203 1.387700 | |
| 204 1.400400 | |
| 205 1.404700 | |
| 206 1.398400 | |
| 207 1.358000 | |
| 208 1.359600 | |
| 209 1.367700 | |
| 210 1.358600 | |
| 211 1.369200 | |
| 212 1.373700 | |
| 213 1.395100 | |
| 214 1.360800 | |
| 215 1.343900 | |
| 216 1.330300 | |
| 217 1.328800 | |
| 218 1.369900 | |
| 219 1.346300 | |
| 220 1.379700 | |
| 221 1.326000 | |
| 222 1.334600 | |
| 223 1.339100 | |
| 224 1.349200 | |
| 225 1.324800 | |
| 226 1.303600 | |
| 227 1.299900 | |
| 228 1.338800 | |
| 229 1.331800 | |
| 230 1.351400 | |
| 231 1.314200 | |
| 232 1.293600 | |
| 233 1.322100 | |
| 234 1.295800 | |
| 235 1.302500 | |
| 236 1.338900 | |
| 237 1.308900 | |
| 238 1.290100 | |
| 239 1.323300 | |
| 240 1.270500 | |
| 241 1.246300 | |
| 242 1.303900 | |
| 243 1.324800 | |
| 244 1.216000 | |
| 245 1.303500 | |
| 246 1.304900 | |
| 247 1.273300 | |
| 248 1.278300 | |
| 249 1.252000 | |
| 250 1.283400 | |
| 251 1.271600 | |
| 252 1.300300 | |
| 253 1.265800 | |
| 254 1.249200 | |
| 255 1.252600 | |
| 256 1.265500 | |
| 257 1.228600 | |
| 258 1.257300 | |
| 259 1.288900 | |
| 260 1.257200 | |
| 261 1.243700 | |
| 262 1.272100 | |
| 263 1.252000 | |
| 264 1.264900 | |
| 265 1.268800 | |
| 266 1.256000 | |
| 267 1.230200 | |
| 268 1.231700 | |
| 269 1.243400 | |
| 270 1.285200 | |
| 271 1.225500 | |
| 272 1.217900 | |
| 273 1.209200 | |
| 274 1.224200 | |
| 275 1.226400 | |
| 276 1.261500 | |
| 277 1.223900 | |
| 278 1.244000 | |
| 279 1.226600 | |
| 280 1.235000 | |
| 281 1.213400 | |
| 282 1.177600 | |
| 283 1.218100 | |
| 284 1.231900 | |
| 285 1.200900 | |
| 286 1.223400 | |
| 287 1.235100 | |
| 288 1.232500 | |
| 289 1.230100 | |
| 290 1.225900 | |
| 291 1.182700 | |
| 292 1.237100 | |
| 293 1.201000 | |
| 294 1.213000 | |
| 295 1.205500 | |
| 296 1.181900 | |
| 297 1.198300 | |
| 298 1.195200 | |
| 299 1.215000 | |
| 300 1.195500 | |
| 301 1.186100 | |
| 302 1.174900 | |
| 303 1.184400 | |
| 304 1.207100 | |
| 305 1.181100 | |
| 306 1.195300 | |
| 307 1.189000 | |
| 308 1.180200 | |
| 309 1.167200 | |
| 310 1.206700 | |
| 311 1.203600 | |
| 312 1.186600 | |
| 313 1.224100 | |
| 314 1.180000 | |
| 315 1.186600 | |
| 316 1.150700 | |
| 317 1.165700 | |
| 318 1.178100 | |
| 319 1.148300 | |
| 320 1.153600 | |
| 321 1.189200 | |
| 322 1.182100 | |
| 323 1.183800 | |
| 324 1.202900 | |
| 325 1.196600 | |
| 326 1.200800 | |
| 327 1.153100 | |
| 328 1.212400 | |
| 329 1.167300 | |
| 330 1.188300 | |
| 331 1.179300 | |
| 332 1.211400 | |
| 333 1.169900 | |
| 334 1.179300 | |
| 335 1.153300 | |
| 336 1.188900 | |
| 337 1.179200 | |
| 338 1.217300 | |
| 339 1.169700 | |
| 340 1.177700 | |
| 341 1.197300 | |
| 342 1.177800 | |
| 343 1.169700 | |
| 344 1.186800 | |
| 345 1.180000 | |
| 346 1.193400 | |
| 347 1.171900 | |
| 348 1.190000 | |
| 349 1.160900 | |
| 350 1.170800 | |
| 351 1.166900 | |
| 352 1.183200 | |
| 353 1.118200 | |
| 354 1.185900 | |
| 355 1.157800 | |
| 356 1.160200 | |
| 357 1.184200 | |
| 358 1.172100 | |
| 359 1.143800 | |
| 360 1.178000 | |
| 361 1.157900 | |
| 362 1.151700 | |
| 363 1.196600 | |
| 364 1.181800 | |
| 365 1.195600 | |
| 366 1.165000 | |
| 367 1.157300 | |
| 368 1.165200 | |
| 369 1.167700 | |
| 370 1.184900 | |
| 371 1.168400 | |
| 372 1.150500 | |
| 373 1.152900 | |
| 374 1.158900 | |
| 375 1.143900 | |
| 376 1.157200 | |
| 377 1.146800 | |
| 378 1.142600 | |
| 379 1.140600 | |
| 380 1.142400 | |
| 381 1.114100 | |
| 382 1.169700 | |
| 383 1.142500 | |
| 384 1.176000 | |
| 385 1.160600 | |
| 386 1.164700 | |
| 387 1.124000 | |
| 388 1.134500 | |
| 389 1.185500 | |
| 390 1.154300 | |
| 391 1.125500 | |
| 392 1.174400 | |
| 393 1.132800 | |
| 394 1.145200 | |
| 395 1.129800 | |
| 396 1.140600 | |
| 397 1.126000 | |
| 398 1.182800 | |
| 399 1.127800 | |
| 400 1.155000 | |
| 401 1.134600 | |
| 402 1.155900 | |
| 403 1.150400 | |
| 404 1.141700 | |
| 405 1.131500 | |
| 406 1.169600 | |
| 407 1.170500 | |
| 408 1.129100 | |
| 409 1.151700 | |
| 410 1.168200 | |
| 411 1.109100 | |
| 412 1.129700 | |
| 413 1.143900 | |
| 414 1.157300 | |
| 415 1.128900 | |
| 416 1.171500 | |
| 417 1.141600 | |
| 418 1.157700 | |
| 419 1.137000 | |
| 420 1.154000 | |
| 421 1.167300 | |
| 422 1.137400 | |
| 423 1.121500 | |
| 424 1.128500 | |
| 425 1.130300 | |
| 426 1.162100 | |
| 427 1.155100 | |
| 428 1.145300 | |
| 429 1.121000 | |
| 430 1.182200 | |
| 431 1.157000 | |
| 432 1.162300 | |
| 433 1.135200 | |
| 434 1.141300 | |
| 435 1.151700 | |
| 436 1.148000 | |
| 437 1.132500 | |
| 438 1.163000 | |
| 439 1.116300 | |
| 440 1.142000 | |
| 441 1.091700 | |
| 442 1.141500 | |
| 443 1.154900 | |
| 444 1.120400 | |
| 445 1.173700 | |
| 446 1.138300 | |
| 447 1.135600 | |
| 448 1.138800 | |
| 449 1.126800 | |
| 450 1.129400 | |
| 451 1.146300 | |
| 452 1.104200 | |
| 453 1.163500 | |
| 454 1.169300 | |
| 455 1.147100 | |
| 456 1.157100 | |
| 457 1.122100 | |
| 458 1.121900 | |
| 459 1.150500 | |
| 460 1.115700 | |
| 461 1.121100 | |
| 462 1.123400 | |
| 463 1.097500 | |
| 464 1.103800 | |
| 465 1.167700 | |
| 466 1.130000 | |
| 467 1.164500 | |
| 468 1.127200 | |
| 469 1.133800 | |
| 470 1.132700 | |
| 471 1.122800 | |
| 472 1.159500 | |
| 473 1.122900 | |
| 474 1.105000 | |
| 475 1.145700 | |
| 476 1.086400 | |
| 477 1.112600 | |
| 478 1.139300 | |
| 479 1.135000 | |
| 480 1.135200 | |
| 481 1.117500 | |
| 482 1.102300 | |
| 483 1.147700 | |
| 484 1.119200 | |
| 485 1.125800 | |
| 486 1.135400 | |
| 487 1.149500 | |
| 488 1.099400 | |
| 489 1.153900 | |
| 490 1.122700 | |
| 491 1.089400 | |
| 492 1.167200 | |
| 493 1.151300 | |
| 494 1.131400 | |
| 495 1.131400 | |
| 496 1.145200 | |
| 497 1.125700 | |
| 498 1.119300 | |
| 499 1.128600 | |
| 500 1.121000 | |
| /home/matt/hf/sqllama-V0/.venv/lib/python3.7/site-packages/transformers/generation/utils.py:1220: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation) | |
| "You have modified the pretrained model configuration to control generation. This is a" | |
| /home/matt/hf/sqllama-V0/.venv/lib/python3.7/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None | |
| warnings.warn("None of the inputs have requires_grad=True. Gradients will be None") | |
| Output exceeds the size limit. Open the full output data in a text editor | |
| from model | |
| <unk>table: 2-11561331-17 | |
| columns: Name,Actual version,System,Platform,License | |
| Q: Which System's Name is Steem, and has a Freeware License? | |
| A: SELECT Name FROM 2-11561331-17 WHERE License = 'Freeware' AND System = 'Steem' | |
| END | |
| \end{code} | |
| expected answer | |
| SELECT System FROM 2-11561331-17 WHERE License = 'freeware' AND Name = 'steem' | |
| END | |
| from model | |
| <unk>table: 1-18847736-2 | |
| columns: Game,Date,Opponent,Result,Dolphins points,Opponents,Record,Attendance | |
| Q: What is the date when the opponent is the New England Patriots? | |
| A: SELECT Date FROM 1-18847736-2 WHERE Opponent = 'New England Patriots' | |
| END | |
| \end | |
| expected answer | |
| SELECT Date FROM 1-18847736-2 WHERE Opponent = 'New England Patriots' | |
| END | |
| ... | |
| expected answer | |
| SELECT Manufacturer FROM 1-17801022-1 WHERE Date = 'November 2' | |
| END | |