| Output exceeds the size limit. Open the full output data in a text editor |
|
|
| table: 2-1137692-1 |
| columns: Entrant,Constructor,Chassis,Engine †,Tyre,Driver,Rounds |
| Q: What were the rounds on the Engine † of the Ferrari 048? |
| A: SELECT Rounds FROM 2-1137692-1 WHERE Engine † = 'ferrari 048' |
| END |
|
|
|
|
| table: 1-21530474-1 |
| columns: Chassis code,Model no.,Production years,Drivetrain,Transmission,Engine type,Engine code,Region(s) |
| Q: Name the drivetrain for 1ur-fse for usf41 |
| A: SELECT Drivetrain FROM 1-21530474-1 WHERE Engine code = '1UR-FSE' AND Chassis code = 'USF41' |
| END |
|
|
|
|
| table: 2-14155087-1 |
| columns: Callsign,Area served,Frequency,Band,On-air ID,Purpose |
| Q: What is the Callsign with an Area of tamworth and frequency of 0 88.9? |
| A: SELECT Callsign FROM 2-14155087-1 WHERE Area served = 'tamworth' AND Frequency = '0 88.9' |
| END |
|
|
|
|
| table: 2-17580726-2 |
| columns: Date,Opponent,Venue,Score,Attendance,Scorers |
| Q: What is the number of people in attendance when Tonbridge Angels is the opponent? |
| ... |
| Q: What were the match points when Bordeaux-Bègles was eliminated from competition? |
| A: SELECT Match points FROM 1-27986200-3 WHERE Eliminated from competition = 'Bordeaux-Bègles' |
| END |
|
|
| /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... |
| 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 |
| [250/250 3:49:26, Epoch 0/1] |
| Step Training Loss |
| 1 2.748800 |
| 2 2.699100 |
| 3 2.670200 |
| 4 2.600500 |
| 5 2.560100 |
| 6 2.556800 |
| 7 2.498100 |
| 8 2.515400 |
| 9 2.436100 |
| 10 2.411700 |
| 11 2.346400 |
| 12 2.276300 |
| 13 2.238000 |
| 14 2.189100 |
| 15 2.109200 |
| 16 2.058000 |
| 17 1.983900 |
| 18 1.928600 |
| 19 1.824100 |
| 20 1.794700 |
| 21 1.681200 |
| 22 1.598900 |
| 23 1.562000 |
| 24 1.527200 |
| 25 1.518700 |
| 26 1.493100 |
| 27 1.500500 |
| 28 1.464000 |
| 29 1.386900 |
| 30 1.373400 |
| 31 1.362200 |
| 32 1.360800 |
| 33 1.321000 |
| 34 1.310500 |
| 35 1.302600 |
| 36 1.256100 |
| 37 1.252500 |
| 38 1.202300 |
| 39 1.249100 |
| 40 1.188600 |
| 41 1.203200 |
| 42 1.150000 |
| 43 1.182000 |
| 44 1.192300 |
| 45 1.133100 |
| 46 1.119600 |
| 47 1.097000 |
| 48 1.142100 |
| 49 1.117200 |
| 50 1.129200 |
| 51 1.087300 |
| 52 1.098700 |
| 53 1.135400 |
| 54 1.071700 |
| 55 1.087300 |
| 56 1.051400 |
| 57 1.068300 |
| 58 1.092500 |
| 59 1.068600 |
| 60 1.072800 |
| 61 1.074000 |
| 62 1.060400 |
| 63 1.065800 |
| 64 1.075900 |
| 65 1.059500 |
| 66 1.039600 |
| 67 1.051400 |
| 68 1.049500 |
| 69 1.023800 |
| 70 1.071900 |
| 71 1.051000 |
| 72 1.034700 |
| 73 1.041600 |
| 74 1.030900 |
| 75 1.010800 |
| 76 1.019800 |
| 77 1.005000 |
| 78 1.043800 |
| 79 1.009200 |
| 80 1.017100 |
| 81 1.044600 |
| 82 1.022600 |
| 83 1.011400 |
| 84 0.996600 |
| 85 1.029900 |
| 86 0.988200 |
| 87 1.005600 |
| 88 0.986600 |
| 89 1.025300 |
| 90 1.012500 |
| 91 0.988100 |
| 92 1.001800 |
| 93 0.987100 |
| 94 1.017600 |
| 95 0.998500 |
| 96 0.966600 |
| 97 0.983700 |
| 98 0.961800 |
| 99 0.969000 |
| 100 0.989200 |
| 101 0.956400 |
| 102 0.976000 |
| 103 1.000100 |
| 104 1.001500 |
| 105 0.995900 |
| 106 0.989700 |
| 107 0.965700 |
| 108 0.968400 |
| 109 1.019600 |
| 110 1.000100 |
| 111 0.978500 |
| 112 0.978900 |
| 113 0.952600 |
| 114 0.975400 |
| 115 0.989400 |
| 116 0.968500 |
| 117 0.960100 |
| 118 0.979100 |
| 119 0.955100 |
| 120 0.934800 |
| 121 0.943600 |
| 122 0.976700 |
| 123 0.998700 |
| 124 0.930500 |
| 125 0.953500 |
| 126 0.978000 |
| 127 0.967300 |
| 128 0.929400 |
| 129 0.963100 |
| 130 0.961500 |
| 131 0.978500 |
| 132 0.937200 |
| 133 0.953400 |
| 134 0.962000 |
| 135 0.950700 |
| 136 0.925100 |
| 137 0.958800 |
| 138 0.926200 |
| 139 0.930600 |
| 140 0.968900 |
| 141 0.970400 |
| 142 0.927100 |
| 143 0.911800 |
| 144 0.953200 |
| 145 0.907100 |
| 146 0.935900 |
| 147 0.970600 |
| 148 0.920400 |
| 149 0.930200 |
| 150 0.926700 |
| 151 0.913400 |
| 152 0.926800 |
| 153 0.967200 |
| 154 0.939500 |
| 155 0.910600 |
| 156 0.926400 |
| 157 0.935400 |
| 158 0.967700 |
| 159 0.899000 |
| 160 0.916600 |
| 161 0.961600 |
| 162 0.898200 |
| 163 0.944600 |
| 164 0.935700 |
| 165 0.922500 |
| 166 0.897600 |
| 167 0.968600 |
| 168 0.927400 |
| 169 0.910900 |
| 170 0.904700 |
| 171 0.899800 |
| 172 0.896400 |
| 173 0.862100 |
| 174 0.909100 |
| 175 0.903200 |
| 176 0.958600 |
| 177 0.902500 |
| 178 0.894900 |
| 179 0.937900 |
| 180 0.900700 |
| 181 0.922300 |
| 182 0.939300 |
| 183 0.932600 |
| 184 0.913300 |
| 185 0.941700 |
| 186 0.886300 |
| 187 0.918000 |
| 188 0.884000 |
| 189 0.947400 |
| 190 0.894500 |
| 191 0.929300 |
| 192 0.877300 |
| 193 0.894300 |
| 194 0.867800 |
| 195 0.913500 |
| 196 0.908100 |
| 197 0.931200 |
| 198 0.911000 |
| 199 0.941800 |
| 200 0.913000 |
| 201 0.921800 |
| 202 0.921700 |
| 203 0.914500 |
| 204 0.910500 |
| 205 0.906600 |
| 206 0.915100 |
| 207 0.881600 |
| 208 0.884700 |
| 209 0.902900 |
| 210 0.882600 |
| 211 0.891000 |
| 212 0.914400 |
| 213 0.930400 |
| 214 0.891100 |
| 215 0.859300 |
| 216 0.891800 |
| 217 0.873000 |
| 218 0.925900 |
| 219 0.905700 |
| 220 0.921200 |
| 221 0.890200 |
| 222 0.915800 |
| 223 0.887300 |
| 224 0.898300 |
| 225 0.865600 |
| 226 0.873900 |
| 227 0.904800 |
| 228 0.917900 |
| 229 0.923400 |
| 230 0.939700 |
| 231 0.913400 |
| 232 0.873100 |
| 233 0.896700 |
| 234 0.892100 |
| 235 0.902100 |
| 236 0.927200 |
| 237 0.912900 |
| 238 0.872900 |
| 239 0.904700 |
| 240 0.879600 |
| 241 0.879800 |
| 242 0.908800 |
| 243 0.909800 |
| 244 0.838400 |
| 245 0.889200 |
| 246 0.912900 |
| 247 0.879700 |
| 248 0.910700 |
| 249 0.845400 |
| 250 0.882200 |
| /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: 1-12028543-3 |
| columns: Season,Cup FinalDate,WinningTeam,Score,LosingTeam,Location,Cup Final Attendance |
| Q: Who was the winning team in the 1989 season? |
| A: SELECT WinningTeam FROM 1-12028543-3 WHERE Season = '1989' |
| END |
| END |
| END |
| END |
|
|
| expected answer |
| SELECT WinningTeam FROM 1-12028543-3 WHERE Season = '1989' |
| END |
|
|
| from model |
| <unk>table: 2-18096431-5 |
| columns: Place,Player,Country,Score,To par |
| Q: What is To par, when Country is "United States", and when Player is "Mark Brooks"? |
| A: 18-1 |
| END |
|
|
|
|
| expected answer |
| SELECT To par FROM 2-18096431-5 WHERE Country = 'united states' AND Player = 'mark brooks' |
| END |
| ... |
| expected answer |
| SELECT Score FROM 2-17978030-6 WHERE Set 3 = '26–28' |
| END |
|
|
