Upload 9 files
Browse files- README.md +636 -3
- added_tokens.json +5 -0
- config.json +27 -0
- generation_config.json +6 -0
- pytorch_model.bin.index.json +298 -0
- special_tokens_map.json +11 -0
- tokenizer.model +3 -0
- tokenizer_config.json +48 -0
- train_params.yaml +35 -0
README.md
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@@ -1,3 +1,636 @@
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---
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license: llama2
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| 1 |
+
---
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| 2 |
+
license: llama2
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| 3 |
+
language:
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| 4 |
+
- ro
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| 5 |
+
base_model: meta-llama/Llama-2-7b-hf
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| 6 |
+
model-index:
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| 7 |
+
- name: OpenLLM-Ro/RoLlama2-7b-Base-2024-05-14
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| 8 |
+
results:
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| 9 |
+
- task:
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| 10 |
+
type: text-generation
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| 11 |
+
dataset:
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| 12 |
+
name: Romanian_Academic_Benchmarks
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| 13 |
+
type: Romanian_Academic_Benchmarks
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| 14 |
+
metrics:
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| 15 |
+
- name: Average accuracy
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| 16 |
+
type: accuracy
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| 17 |
+
value: 38.03
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| 18 |
+
- task:
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| 19 |
+
type: text-generation
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| 20 |
+
dataset:
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| 21 |
+
name: OpenLLM-Ro/ro_arc_challenge
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| 22 |
+
type: OpenLLM-Ro/ro_arc_challenge
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| 23 |
+
metrics:
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| 24 |
+
- name: Average accuracy
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| 25 |
+
type: accuracy
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| 26 |
+
value: 37.95
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| 27 |
+
- task:
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| 28 |
+
type: text-generation
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| 29 |
+
dataset:
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| 30 |
+
name: OpenLLM-Ro/ro_mmlu
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| 31 |
+
type: OpenLLM-Ro/ro_mmlu
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| 32 |
+
metrics:
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| 33 |
+
- name: Average accuracy
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| 34 |
+
type: accuracy
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| 35 |
+
value: 27.22
|
| 36 |
+
- task:
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| 37 |
+
type: text-generation
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| 38 |
+
dataset:
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| 39 |
+
name: OpenLLM-Ro/ro_winogrande
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| 40 |
+
type: OpenLLM-Ro/ro_winogrande
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| 41 |
+
metrics:
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| 42 |
+
- name: Average accuracy
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| 43 |
+
type: accuracy
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| 44 |
+
value: 59.29
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| 45 |
+
- task:
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| 46 |
+
type: text-generation
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| 47 |
+
dataset:
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| 48 |
+
name: OpenLLM-Ro/ro_hellaswag
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| 49 |
+
type: OpenLLM-Ro/ro_hellaswag
|
| 50 |
+
metrics:
|
| 51 |
+
- name: Average accuracy
|
| 52 |
+
type: accuracy
|
| 53 |
+
value: 57.22
|
| 54 |
+
- task:
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| 55 |
+
type: text-generation
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| 56 |
+
dataset:
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| 57 |
+
name: OpenLLM-Ro/ro_gsm8k
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| 58 |
+
type: OpenLLM-Ro/ro_gsm8k
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| 59 |
+
metrics:
|
| 60 |
+
- name: Average accuracy
|
| 61 |
+
type: accuracy
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| 62 |
+
value: 2.53
|
| 63 |
+
- task:
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| 64 |
+
type: text-generation
|
| 65 |
+
dataset:
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| 66 |
+
name: OpenLLM-Ro/ro_truthfulqa
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| 67 |
+
type: OpenLLM-Ro/ro_truthfulqa
|
| 68 |
+
metrics:
|
| 69 |
+
- name: Average accuracy
|
| 70 |
+
type: accuracy
|
| 71 |
+
value: 44
|
| 72 |
+
- task:
|
| 73 |
+
type: text-generation
|
| 74 |
+
dataset:
|
| 75 |
+
name: LaRoSeDa_binary
|
| 76 |
+
type: LaRoSeDa_binary
|
| 77 |
+
metrics:
|
| 78 |
+
- name: Average macro-f1
|
| 79 |
+
type: macro-f1
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| 80 |
+
value: 83.25
|
| 81 |
+
- task:
|
| 82 |
+
type: text-generation
|
| 83 |
+
dataset:
|
| 84 |
+
name: LaRoSeDa_multiclass
|
| 85 |
+
type: LaRoSeDa_multiclass
|
| 86 |
+
metrics:
|
| 87 |
+
- name: Average macro-f1
|
| 88 |
+
type: macro-f1
|
| 89 |
+
value: 61.04
|
| 90 |
+
- task:
|
| 91 |
+
type: text-generation
|
| 92 |
+
dataset:
|
| 93 |
+
name: LaRoSeDa_binary_finetuned
|
| 94 |
+
type: LaRoSeDa_binary_finetuned
|
| 95 |
+
metrics:
|
| 96 |
+
- name: Average macro-f1
|
| 97 |
+
type: macro-f1
|
| 98 |
+
value: 98.97
|
| 99 |
+
- task:
|
| 100 |
+
type: text-generation
|
| 101 |
+
dataset:
|
| 102 |
+
name: LaRoSeDa_multiclass_finetuned
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| 103 |
+
type: LaRoSeDa_multiclass_finetuned
|
| 104 |
+
metrics:
|
| 105 |
+
- name: Average macro-f1
|
| 106 |
+
type: macro-f1
|
| 107 |
+
value: 87.72
|
| 108 |
+
- task:
|
| 109 |
+
type: text-generation
|
| 110 |
+
dataset:
|
| 111 |
+
name: WMT_EN-RO
|
| 112 |
+
type: WMT_EN-RO
|
| 113 |
+
metrics:
|
| 114 |
+
- name: Average bleu
|
| 115 |
+
type: bleu
|
| 116 |
+
value: 10.01
|
| 117 |
+
- task:
|
| 118 |
+
type: text-generation
|
| 119 |
+
dataset:
|
| 120 |
+
name: WMT_RO-EN
|
| 121 |
+
type: WMT_RO-EN
|
| 122 |
+
metrics:
|
| 123 |
+
- name: Average bleu
|
| 124 |
+
type: bleu
|
| 125 |
+
value: 13.03
|
| 126 |
+
- task:
|
| 127 |
+
type: text-generation
|
| 128 |
+
dataset:
|
| 129 |
+
name: WMT_EN-RO_finetuned
|
| 130 |
+
type: WMT_EN-RO_finetuned
|
| 131 |
+
metrics:
|
| 132 |
+
- name: Average bleu
|
| 133 |
+
type: bleu
|
| 134 |
+
value: 27.85
|
| 135 |
+
- task:
|
| 136 |
+
type: text-generation
|
| 137 |
+
dataset:
|
| 138 |
+
name: WMT_RO-EN_finetuned
|
| 139 |
+
type: WMT_RO-EN_finetuned
|
| 140 |
+
metrics:
|
| 141 |
+
- name: Average bleu
|
| 142 |
+
type: bleu
|
| 143 |
+
value: 39.3
|
| 144 |
+
- task:
|
| 145 |
+
type: text-generation
|
| 146 |
+
dataset:
|
| 147 |
+
name: XQuAD
|
| 148 |
+
type: XQuAD
|
| 149 |
+
metrics:
|
| 150 |
+
- name: Average exact_match
|
| 151 |
+
type: exact_match
|
| 152 |
+
value: 30.15
|
| 153 |
+
- task:
|
| 154 |
+
type: text-generation
|
| 155 |
+
dataset:
|
| 156 |
+
name: XQuAD
|
| 157 |
+
type: XQuAD
|
| 158 |
+
metrics:
|
| 159 |
+
- name: Average f1
|
| 160 |
+
type: f1
|
| 161 |
+
value: 47.03
|
| 162 |
+
- task:
|
| 163 |
+
type: text-generation
|
| 164 |
+
dataset:
|
| 165 |
+
name: XQuAD_finetuned
|
| 166 |
+
type: XQuAD_finetuned
|
| 167 |
+
metrics:
|
| 168 |
+
- name: Average exact_match
|
| 169 |
+
type: exact_match
|
| 170 |
+
value: 67.06
|
| 171 |
+
- task:
|
| 172 |
+
type: text-generation
|
| 173 |
+
dataset:
|
| 174 |
+
name: XQuAD_finetuned
|
| 175 |
+
type: XQuAD_finetuned
|
| 176 |
+
metrics:
|
| 177 |
+
- name: Average f1
|
| 178 |
+
type: f1
|
| 179 |
+
value: 79.96
|
| 180 |
+
- task:
|
| 181 |
+
type: text-generation
|
| 182 |
+
dataset:
|
| 183 |
+
name: STS
|
| 184 |
+
type: STS
|
| 185 |
+
metrics:
|
| 186 |
+
- name: Average spearman
|
| 187 |
+
type: spearman
|
| 188 |
+
value: 7.89
|
| 189 |
+
- task:
|
| 190 |
+
type: text-generation
|
| 191 |
+
dataset:
|
| 192 |
+
name: STS
|
| 193 |
+
type: STS
|
| 194 |
+
metrics:
|
| 195 |
+
- name: Average pearson
|
| 196 |
+
type: pearson
|
| 197 |
+
value: 7.98
|
| 198 |
+
- task:
|
| 199 |
+
type: text-generation
|
| 200 |
+
dataset:
|
| 201 |
+
name: STS_finetuned
|
| 202 |
+
type: STS_finetuned
|
| 203 |
+
metrics:
|
| 204 |
+
- name: Average spearman
|
| 205 |
+
type: spearman
|
| 206 |
+
value: 71.75
|
| 207 |
+
- task:
|
| 208 |
+
type: text-generation
|
| 209 |
+
dataset:
|
| 210 |
+
name: STS_finetuned
|
| 211 |
+
type: STS_finetuned
|
| 212 |
+
metrics:
|
| 213 |
+
- name: Average pearson
|
| 214 |
+
type: pearson
|
| 215 |
+
value: 71.99
|
| 216 |
+
- task:
|
| 217 |
+
type: text-generation
|
| 218 |
+
dataset:
|
| 219 |
+
name: OpenLLM-Ro/ro_arc_challenge
|
| 220 |
+
type: OpenLLM-Ro/ro_arc_challenge
|
| 221 |
+
metrics:
|
| 222 |
+
- name: 0-shot
|
| 223 |
+
type: accuracy
|
| 224 |
+
value: 35.56
|
| 225 |
+
- name: 1-shot
|
| 226 |
+
type: accuracy
|
| 227 |
+
value: 36.42
|
| 228 |
+
- name: 3-shot
|
| 229 |
+
type: accuracy
|
| 230 |
+
value: 38.56
|
| 231 |
+
- name: 5-shot
|
| 232 |
+
type: accuracy
|
| 233 |
+
value: 38.39
|
| 234 |
+
- name: 10-shot
|
| 235 |
+
type: accuracy
|
| 236 |
+
value: 39.07
|
| 237 |
+
- name: 25-shot
|
| 238 |
+
type: accuracy
|
| 239 |
+
value: 39.67
|
| 240 |
+
- task:
|
| 241 |
+
type: text-generation
|
| 242 |
+
dataset:
|
| 243 |
+
name: OpenLLM-Ro/ro_mmlu
|
| 244 |
+
type: OpenLLM-Ro/ro_mmlu
|
| 245 |
+
metrics:
|
| 246 |
+
- name: 0-shot
|
| 247 |
+
type: accuracy
|
| 248 |
+
value: 25.82
|
| 249 |
+
- name: 1-shot
|
| 250 |
+
type: accuracy
|
| 251 |
+
value: 25.48
|
| 252 |
+
- name: 3-shot
|
| 253 |
+
type: accuracy
|
| 254 |
+
value: 27.61
|
| 255 |
+
- name: 5-shot
|
| 256 |
+
type: accuracy
|
| 257 |
+
value: 29.96
|
| 258 |
+
- task:
|
| 259 |
+
type: text-generation
|
| 260 |
+
dataset:
|
| 261 |
+
name: OpenLLM-Ro/ro_winogrande
|
| 262 |
+
type: OpenLLM-Ro/ro_winogrande
|
| 263 |
+
metrics:
|
| 264 |
+
- name: 0-shot
|
| 265 |
+
type: accuracy
|
| 266 |
+
value: 58.72
|
| 267 |
+
- name: 1-shot
|
| 268 |
+
type: accuracy
|
| 269 |
+
value: 58.88
|
| 270 |
+
- name: 3-shot
|
| 271 |
+
type: accuracy
|
| 272 |
+
value: 60.38
|
| 273 |
+
- name: 5-shot
|
| 274 |
+
type: accuracy
|
| 275 |
+
value: 59.19
|
| 276 |
+
- task:
|
| 277 |
+
type: text-generation
|
| 278 |
+
dataset:
|
| 279 |
+
name: OpenLLM-Ro/ro_hellaswag
|
| 280 |
+
type: OpenLLM-Ro/ro_hellaswag
|
| 281 |
+
metrics:
|
| 282 |
+
- name: 0-shot
|
| 283 |
+
type: accuracy
|
| 284 |
+
value: 55.85
|
| 285 |
+
- name: 1-shot
|
| 286 |
+
type: accuracy
|
| 287 |
+
value: 57.06
|
| 288 |
+
- name: 3-shot
|
| 289 |
+
type: accuracy
|
| 290 |
+
value: 57.52
|
| 291 |
+
- name: 5-shot
|
| 292 |
+
type: accuracy
|
| 293 |
+
value: 57.89
|
| 294 |
+
- name: 10-shot
|
| 295 |
+
type: accuracy
|
| 296 |
+
value: 57.79
|
| 297 |
+
- task:
|
| 298 |
+
type: text-generation
|
| 299 |
+
dataset:
|
| 300 |
+
name: OpenLLM-Ro/ro_gsm8k
|
| 301 |
+
type: OpenLLM-Ro/ro_gsm8k
|
| 302 |
+
metrics:
|
| 303 |
+
- name: 0-shot
|
| 304 |
+
type: accuracy
|
| 305 |
+
value: 0
|
| 306 |
+
- name: 1-shot
|
| 307 |
+
type: accuracy
|
| 308 |
+
value: 2.96
|
| 309 |
+
- name: 3-shot
|
| 310 |
+
type: accuracy
|
| 311 |
+
value: 4.62
|
| 312 |
+
- task:
|
| 313 |
+
type: text-generation
|
| 314 |
+
dataset:
|
| 315 |
+
name: LaRoSeDa_binary
|
| 316 |
+
type: LaRoSeDa_binary
|
| 317 |
+
metrics:
|
| 318 |
+
- name: 0-shot
|
| 319 |
+
type: macro-f1
|
| 320 |
+
value: 42.78
|
| 321 |
+
- name: 1-shot
|
| 322 |
+
type: macro-f1
|
| 323 |
+
value: 98
|
| 324 |
+
- name: 3-shot
|
| 325 |
+
type: macro-f1
|
| 326 |
+
value: 95.13
|
| 327 |
+
- name: 5-shot
|
| 328 |
+
type: macro-f1
|
| 329 |
+
value: 97.07
|
| 330 |
+
- task:
|
| 331 |
+
type: text-generation
|
| 332 |
+
dataset:
|
| 333 |
+
name: LaRoSeDa_multiclass
|
| 334 |
+
type: LaRoSeDa_multiclass
|
| 335 |
+
metrics:
|
| 336 |
+
- name: 0-shot
|
| 337 |
+
type: macro-f1
|
| 338 |
+
value: 46.41
|
| 339 |
+
- name: 1-shot
|
| 340 |
+
type: macro-f1
|
| 341 |
+
value: 67.36
|
| 342 |
+
- name: 3-shot
|
| 343 |
+
type: macro-f1
|
| 344 |
+
value: 65.16
|
| 345 |
+
- name: 5-shot
|
| 346 |
+
type: macro-f1
|
| 347 |
+
value: 65.23
|
| 348 |
+
- task:
|
| 349 |
+
type: text-generation
|
| 350 |
+
dataset:
|
| 351 |
+
name: WMT_EN-RO
|
| 352 |
+
type: WMT_EN-RO
|
| 353 |
+
metrics:
|
| 354 |
+
- name: 0-shot
|
| 355 |
+
type: bleu
|
| 356 |
+
value: 4.45
|
| 357 |
+
- name: 1-shot
|
| 358 |
+
type: bleu
|
| 359 |
+
value: 8.61
|
| 360 |
+
- name: 3-shot
|
| 361 |
+
type: bleu
|
| 362 |
+
value: 12.25
|
| 363 |
+
- name: 5-shot
|
| 364 |
+
type: bleu
|
| 365 |
+
value: 14.73
|
| 366 |
+
- task:
|
| 367 |
+
type: text-generation
|
| 368 |
+
dataset:
|
| 369 |
+
name: WMT_RO-EN
|
| 370 |
+
type: WMT_RO-EN
|
| 371 |
+
metrics:
|
| 372 |
+
- name: 0-shot
|
| 373 |
+
type: bleu
|
| 374 |
+
value: 1.29
|
| 375 |
+
- name: 1-shot
|
| 376 |
+
type: bleu
|
| 377 |
+
value: 10.78
|
| 378 |
+
- name: 3-shot
|
| 379 |
+
type: bleu
|
| 380 |
+
value: 16.82
|
| 381 |
+
- name: 5-shot
|
| 382 |
+
type: bleu
|
| 383 |
+
value: 23.24
|
| 384 |
+
- task:
|
| 385 |
+
type: text-generation
|
| 386 |
+
dataset:
|
| 387 |
+
name: XQuAD_EM
|
| 388 |
+
type: XQuAD_EM
|
| 389 |
+
metrics:
|
| 390 |
+
- name: 0-shot
|
| 391 |
+
type: exact_match
|
| 392 |
+
value: 5.29
|
| 393 |
+
- name: 1-shot
|
| 394 |
+
type: exact_match
|
| 395 |
+
value: 33.95
|
| 396 |
+
- name: 3-shot
|
| 397 |
+
type: exact_match
|
| 398 |
+
value: 39.24
|
| 399 |
+
- name: 5-shot
|
| 400 |
+
type: exact_match
|
| 401 |
+
value: 42.1
|
| 402 |
+
- task:
|
| 403 |
+
type: text-generation
|
| 404 |
+
dataset:
|
| 405 |
+
name: XQuAD_F1
|
| 406 |
+
type: XQuAD_F1
|
| 407 |
+
metrics:
|
| 408 |
+
- name: 0-shot
|
| 409 |
+
type: f1
|
| 410 |
+
value: 16.17
|
| 411 |
+
- name: 1-shot
|
| 412 |
+
type: f1
|
| 413 |
+
value: 51.84
|
| 414 |
+
- name: 3-shot
|
| 415 |
+
type: f1
|
| 416 |
+
value: 58.82
|
| 417 |
+
- name: 5-shot
|
| 418 |
+
type: f1
|
| 419 |
+
value: 61.29
|
| 420 |
+
- task:
|
| 421 |
+
type: text-generation
|
| 422 |
+
dataset:
|
| 423 |
+
name: STS
|
| 424 |
+
type: STS
|
| 425 |
+
metrics:
|
| 426 |
+
- name: 0-shot
|
| 427 |
+
type: spearman
|
| 428 |
+
value: -1.74
|
| 429 |
+
- name: 1-shot
|
| 430 |
+
type: spearman
|
| 431 |
+
value: 15.47
|
| 432 |
+
- name: 3-shot
|
| 433 |
+
type: spearman
|
| 434 |
+
value: 9.93
|
| 435 |
+
- task:
|
| 436 |
+
type: text-generation
|
| 437 |
+
dataset:
|
| 438 |
+
name: STS
|
| 439 |
+
type: STS
|
| 440 |
+
metrics:
|
| 441 |
+
- name: 0-shot
|
| 442 |
+
type: pearson
|
| 443 |
+
value: -1.4
|
| 444 |
+
- name: 1-shot
|
| 445 |
+
type: pearson
|
| 446 |
+
value: 15
|
| 447 |
+
- name: 3-shot
|
| 448 |
+
type: pearson
|
| 449 |
+
value: 10.33
|
| 450 |
+
datasets:
|
| 451 |
+
- uonlp/CulturaX
|
| 452 |
+
---
|
| 453 |
+
|
| 454 |
+
# Model Card for Model ID
|
| 455 |
+
|
| 456 |
+
<!-- Provide a quick summary of what the model is/does. -->
|
| 457 |
+
|
| 458 |
+
RoLlama2 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **foundational 7B model**. Links to other models can be found at the bottom of this page.
|
| 459 |
+
|
| 460 |
+
## Model Details
|
| 461 |
+
|
| 462 |
+
### Model Description
|
| 463 |
+
|
| 464 |
+
<!-- Provide a longer summary of what this model is. -->
|
| 465 |
+
OpenLLM represents the first open-source effort to build a LLM specialized for Romanian. OpenLLM-Ro developed and publicly releases a collection of Romanian LLMs, both in the form of foundational model and instruct and chat variants.
|
| 466 |
+
|
| 467 |
+
|
| 468 |
+
- **Developed by:** OpenLLM-Ro
|
| 469 |
+
<!-- - **Funded by [optional]:** [More Information Needed] -->
|
| 470 |
+
<!-- - **Shared by [optional]:** [More Information Needed] -->
|
| 471 |
+
<!-- - **Model type:** [More Information Needed] -->
|
| 472 |
+
- **Language(s):** Romanian
|
| 473 |
+
- **License:** Llama2 Community License Agreement
|
| 474 |
+
- **Continual pretrained from model:** [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf)
|
| 475 |
+
- **Trained using:** [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX)
|
| 476 |
+
|
| 477 |
+
|
| 478 |
+
### Model Sources
|
| 479 |
+
|
| 480 |
+
<!-- Provide the basic links for the model. -->
|
| 481 |
+
|
| 482 |
+
- **Repository:** https://github.com/OpenLLM-Ro/llama-recipes
|
| 483 |
+
- **Paper:** https://arxiv.org/abs/2406.18266
|
| 484 |
+
|
| 485 |
+
## Intended Use
|
| 486 |
+
|
| 487 |
+
### Intended Use Cases
|
| 488 |
+
|
| 489 |
+
RoLlama2 is intented for research use in Romanian. Base models can be adapted for a variety of natural language tasks while instruction and chat tuned models are intended for assistant-like chat.
|
| 490 |
+
|
| 491 |
+
### Out-of-Scope Use
|
| 492 |
+
|
| 493 |
+
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
| 494 |
+
|
| 495 |
+
Use in any manner that violates the license, any applicable laws or regluations, use in languages other than Romanian.
|
| 496 |
+
|
| 497 |
+
|
| 498 |
+
|
| 499 |
+
## How to Get Started with the Model
|
| 500 |
+
|
| 501 |
+
Use the code below to get started with the model.
|
| 502 |
+
|
| 503 |
+
```python
|
| 504 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
| 505 |
+
|
| 506 |
+
tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Base-2024-05-14")
|
| 507 |
+
model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama2-7b-Base-2024-05-14")
|
| 508 |
+
|
| 509 |
+
input_text = "Mihai Eminescu a fost "
|
| 510 |
+
input_ids = tokenizer(input_text, return_tensors="pt")
|
| 511 |
+
|
| 512 |
+
outputs = model.generate(**input_ids, max_new_tokens=100)
|
| 513 |
+
print(tokenizer.decode(outputs[0]))
|
| 514 |
+
```
|
| 515 |
+
|
| 516 |
+
## Academic Benchmarks
|
| 517 |
+
|
| 518 |
+
<table>
|
| 519 |
+
<tbody>
|
| 520 |
+
<tr>
|
| 521 |
+
<td><strong>Model</strong></td>
|
| 522 |
+
<td><strong><center>Average</center></strong></td>
|
| 523 |
+
<td><strong><center>ARC</center></strong></td>
|
| 524 |
+
<td><strong><center>MMLU</center></strong></td>
|
| 525 |
+
<td><strong><center>Winogrande</center></strong></td>
|
| 526 |
+
<td><strong><center>Hellaswag</center></strong></td>
|
| 527 |
+
<td><strong><center>GSM8k</center></strong></td>
|
| 528 |
+
<td><strong><center>TruthfulQA</center></strong></td>
|
| 529 |
+
</tr>
|
| 530 |
+
<tr>
|
| 531 |
+
<td>Llama-2-7b</td><td><center>37.04</center></td><td><center>36.05</center></td><td><center><strong>33.66</strong></center></td><td><center>57.56</center></td><td><center>48.00</center></td><td><center><strong>4.75</strong></center></td><td><center>42.22</center></td>
|
| 532 |
+
</tr>
|
| 533 |
+
<tr>
|
| 534 |
+
<td><em>RoLlama2-7b-Base-2024-05-14</em></td><td><center><em><strong>38.03</strong></em></center></td><td><center><em><strong>37.95</strong></em></center></td><td><center><em>27.22</em></center></td><td><center><em><strong>59.29</strong></em></center></td><td><center><em><strong>57.22</strong></em></center></td><td><center><em>2.53</em></center></td><td><center><em><strong>44.00</strong></em></center></td>
|
| 535 |
+
</tr>
|
| 536 |
+
</tbody>
|
| 537 |
+
</table>
|
| 538 |
+
|
| 539 |
+
## Downstream Tasks
|
| 540 |
+
|
| 541 |
+
|
| 542 |
+
<table>
|
| 543 |
+
<tbody>
|
| 544 |
+
<tr>
|
| 545 |
+
<td></td>
|
| 546 |
+
<td colspan="4"><center><strong>LaRoSeDa</strong></center></td>
|
| 547 |
+
<td colspan="4"><center><strong>WMT</strong></center></td>
|
| 548 |
+
</tr>
|
| 549 |
+
<tr>
|
| 550 |
+
<td></td>
|
| 551 |
+
<td colspan="2"><center><strong>Few-shot</strong></center></td>
|
| 552 |
+
<td colspan="2"><center><strong>Finetuned</strong></center></td>
|
| 553 |
+
<td colspan="2"><center><strong>Few-shot</strong></center></td>
|
| 554 |
+
<td colspan="2"><center><strong>Finetuned</strong></center></td>
|
| 555 |
+
</tr>
|
| 556 |
+
<tr>
|
| 557 |
+
<td><strong>Model</strong></td>
|
| 558 |
+
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
|
| 559 |
+
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
|
| 560 |
+
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
|
| 561 |
+
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
|
| 562 |
+
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
|
| 563 |
+
<td><center><strong>RO-EN<br>(Bleu)</strong></center></td>
|
| 564 |
+
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
|
| 565 |
+
<td><center><strong>RO-EN<br>(Bleu)</strong></center>
|
| 566 |
+
</tr>
|
| 567 |
+
<tr>
|
| 568 |
+
<td>Llama-2-7b</td><td><center><strong>93.19</strong></center></td><td><center>54.11</center></td><td><center>98.43</center></td><td><center>87.22</center></td><td><center><strong>14.90</strong></center></td><td><center><strong>26.61</strong></center></td><td><center>24.95</center></td><td><center>39.09</center></td>
|
| 569 |
+
</tr>
|
| 570 |
+
<tr>
|
| 571 |
+
<td><em>RoLlama2-7b-Base-2024-05-14</em></td><td><center><em>83.25</em></center></td><td><center><em><strong>61.04</strong></em></center></td><td><center><em><strong>98.97</strong></em></center></td><td><center><em><strong>87.72</strong></em></center></td><td><center><em>10.01</em></center></td><td><center><em>13.03</em></center></td><td><center><em><strong>27.85</strong></em></center></td><td><center><em><strong>39.30</strong></em></center></td>
|
| 572 |
+
</tr>
|
| 573 |
+
</tbody>
|
| 574 |
+
</table>
|
| 575 |
+
|
| 576 |
+
|
| 577 |
+
<table>
|
| 578 |
+
<tbody>
|
| 579 |
+
<tr>
|
| 580 |
+
<td></td>
|
| 581 |
+
<td colspan="4"><center><strong>XQuAD</strong></center></td>
|
| 582 |
+
<td colspan="4"><center><strong>STS</strong></center></td>
|
| 583 |
+
</tr>
|
| 584 |
+
<tr>
|
| 585 |
+
<td></td>
|
| 586 |
+
<td colspan="2"><center><strong>Few-shot</strong></center></td>
|
| 587 |
+
<td colspan="2"><center><strong>Finetuned</strong></center></td>
|
| 588 |
+
<td colspan="2"><center><strong>Few-shot</strong></center></td>
|
| 589 |
+
<td colspan="2"><center><strong>Finetuned</strong></center></td>
|
| 590 |
+
</tr>
|
| 591 |
+
<tr>
|
| 592 |
+
<td><strong>Model</strong></td>
|
| 593 |
+
<td><center><strong>(EM)</strong></center></td>
|
| 594 |
+
<td><center><strong>(F1)</strong></center></td>
|
| 595 |
+
<td><center><strong>(EM)</strong></center></td>
|
| 596 |
+
<td><center><strong>(F1)</strong></center></td>
|
| 597 |
+
<td><center><strong>(Spearman)</strong></center></td>
|
| 598 |
+
<td><center><strong>(Pearson)</strong></center></td>
|
| 599 |
+
<td><center><strong>(Spearman)</strong></center></td>
|
| 600 |
+
<td><center><strong>(Pearson)</strong></center></td>
|
| 601 |
+
</tr>
|
| 602 |
+
<tr>
|
| 603 |
+
<td>Llama-2-7b</td><td><center><strong>38.91</strong></center></td><td><center><strong>56.82</strong></center></td><td><center>65.46</center></td><td><center>79.42</center></td><td><center><strong>9.08</strong></center></td><td><center><strong>9.07</strong></center></td><td><center><strong>79.93</strong></center></td><td><center><strong>81.08</strong></center></td>
|
| 604 |
+
</tr>
|
| 605 |
+
<tr>
|
| 606 |
+
<td><em>RoLlama2-7b-Base-2024-05-14</em></td><td><center><em>30.15</em></center></td><td><center><em>47.03</em></center></td><td><center><em><strong>67.06</strong></em></center></td><td><center><em><strong>79.96</strong></em></center></td><td><center><em>7.89</em></center></td><td><center><em>7.98</em></center></td><td><center><em>71.75</em></center></td><td><center><em>71.99</em></center></td>
|
| 607 |
+
</tr>
|
| 608 |
+
</tbody>
|
| 609 |
+
</table>
|
| 610 |
+
|
| 611 |
+
|
| 612 |
+
## RoLlama2 Model Family
|
| 613 |
+
|
| 614 |
+
| Model | Link |
|
| 615 |
+
|--------------------|:--------:|
|
| 616 |
+
|RoLlama2-7b-Base-2024-05-14 | [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Base-2024-05-14) |
|
| 617 |
+
|RoLlama2-7b-Instruct-2024-05-14 | [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Instruct-2024-05-14) |
|
| 618 |
+
|*RoLlama2-7b-Instruct-2024-10-09*| [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Instruct-2024-10-09) |
|
| 619 |
+
|RoLlama2-7b-Instruct-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Instruct-DPO-2024-10-09) |
|
| 620 |
+
|
| 621 |
+
## Citation
|
| 622 |
+
|
| 623 |
+
```
|
| 624 |
+
@misc{masala2024vorbecstiromanecsterecipetrain,
|
| 625 |
+
title={"Vorbe\c{s}ti Rom\^ane\c{s}te?" A Recipe to Train Powerful Romanian LLMs with English Instructions},
|
| 626 |
+
author={Mihai Masala and Denis C. Ilie-Ablachim and Alexandru Dima and Dragos Corlatescu and Miruna Zavelca and Ovio Olaru and Simina Terian-Dan and Andrei Terian-Dan and Marius Leordeanu and Horia Velicu and Marius Popescu and Mihai Dascalu and Traian Rebedea},
|
| 627 |
+
year={2024},
|
| 628 |
+
eprint={2406.18266},
|
| 629 |
+
archivePrefix={arXiv},
|
| 630 |
+
primaryClass={cs.CL},
|
| 631 |
+
url={https://arxiv.org/abs/2406.18266},
|
| 632 |
+
}
|
| 633 |
+
```
|
| 634 |
+
<!-- **APA:**
|
| 635 |
+
|
| 636 |
+
[More Information Needed] -->
|
added_tokens.json
ADDED
|
@@ -0,0 +1,5 @@
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+
{
|
| 2 |
+
"</s>": 2,
|
| 3 |
+
"<s>": 1,
|
| 4 |
+
"<unk>": 0
|
| 5 |
+
}
|
config.json
ADDED
|
@@ -0,0 +1,27 @@
|
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|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "meta-llama/Llama-2-7b-hf",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"LlamaForCausalLM"
|
| 5 |
+
],
|
| 6 |
+
"attention_bias": false,
|
| 7 |
+
"bos_token_id": 1,
|
| 8 |
+
"eos_token_id": 2,
|
| 9 |
+
"hidden_act": "silu",
|
| 10 |
+
"hidden_size": 4096,
|
| 11 |
+
"initializer_range": 0.02,
|
| 12 |
+
"intermediate_size": 11008,
|
| 13 |
+
"max_position_embeddings": 4096,
|
| 14 |
+
"model_type": "llama",
|
| 15 |
+
"num_attention_heads": 32,
|
| 16 |
+
"num_hidden_layers": 32,
|
| 17 |
+
"num_key_value_heads": 32,
|
| 18 |
+
"pretraining_tp": 1,
|
| 19 |
+
"rms_norm_eps": 1e-05,
|
| 20 |
+
"rope_scaling": null,
|
| 21 |
+
"rope_theta": 10000.0,
|
| 22 |
+
"tie_word_embeddings": false,
|
| 23 |
+
"torch_dtype": "float32",
|
| 24 |
+
"transformers_version": "4.34.0",
|
| 25 |
+
"use_cache": true,
|
| 26 |
+
"vocab_size": 32000
|
| 27 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,6 @@
|
|
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|
|
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|
|
|
|
|
|
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|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"bos_token_id": 1,
|
| 4 |
+
"eos_token_id": 2,
|
| 5 |
+
"transformers_version": "4.34.0"
|
| 6 |
+
}
|
pytorch_model.bin.index.json
ADDED
|
@@ -0,0 +1,298 @@
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|
| 1 |
+
{
|
| 2 |
+
"metadata": {
|
| 3 |
+
"total_size": 26953662464
|
| 4 |
+
},
|
| 5 |
+
"weight_map": {
|
| 6 |
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"lm_head.weight": "pytorch_model-00003-of-00003.bin",
|
| 7 |
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"model.embed_tokens.weight": "pytorch_model-00001-of-00003.bin",
|
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"model.layers.0.input_layernorm.weight": "pytorch_model-00001-of-00003.bin",
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"model.layers.0.mlp.down_proj.weight": "pytorch_model-00001-of-00003.bin",
|
| 10 |
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"model.layers.0.mlp.gate_proj.weight": "pytorch_model-00001-of-00003.bin",
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"model.layers.0.mlp.up_proj.weight": "pytorch_model-00001-of-00003.bin",
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"model.layers.0.self_attn.k_proj.weight": "pytorch_model-00001-of-00003.bin",
|
| 14 |
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"model.layers.0.self_attn.o_proj.weight": "pytorch_model-00001-of-00003.bin",
|
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"model.layers.0.self_attn.q_proj.weight": "pytorch_model-00001-of-00003.bin",
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|
| 17 |
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"model.layers.1.input_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
| 18 |
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"model.layers.1.mlp.down_proj.weight": "pytorch_model-00001-of-00003.bin",
|
| 19 |
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|
| 20 |
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"model.layers.1.mlp.up_proj.weight": "pytorch_model-00001-of-00003.bin",
|
| 21 |
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"model.layers.1.post_attention_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
| 22 |
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"model.layers.1.self_attn.k_proj.weight": "pytorch_model-00001-of-00003.bin",
|
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| 26 |
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| 27 |
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"model.layers.10.mlp.down_proj.weight": "pytorch_model-00001-of-00003.bin",
|
| 28 |
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"model.layers.10.mlp.gate_proj.weight": "pytorch_model-00001-of-00003.bin",
|
| 29 |
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"model.layers.10.mlp.up_proj.weight": "pytorch_model-00001-of-00003.bin",
|
| 30 |
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"model.layers.10.post_attention_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
| 31 |
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|
| 32 |
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"model.layers.10.self_attn.o_proj.weight": "pytorch_model-00001-of-00003.bin",
|
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|
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|
| 297 |
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|
| 298 |
+
}
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special_tokens_map.json
ADDED
|
@@ -0,0 +1,11 @@
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"additional_special_tokens": [
|
| 3 |
+
"<unk>",
|
| 4 |
+
"<s>",
|
| 5 |
+
"</s>"
|
| 6 |
+
],
|
| 7 |
+
"bos_token": "<s>",
|
| 8 |
+
"eos_token": "</s>",
|
| 9 |
+
"pad_token": "<unk>",
|
| 10 |
+
"unk_token": "<unk>"
|
| 11 |
+
}
|
tokenizer.model
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
|
| 3 |
+
size 499723
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,48 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
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|
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|
|
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|
|
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|
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|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_bos_token": true,
|
| 3 |
+
"add_eos_token": false,
|
| 4 |
+
"added_tokens_decoder": {
|
| 5 |
+
"0": {
|
| 6 |
+
"content": "<unk>",
|
| 7 |
+
"lstrip": false,
|
| 8 |
+
"normalized": false,
|
| 9 |
+
"rstrip": false,
|
| 10 |
+
"single_word": false,
|
| 11 |
+
"special": true
|
| 12 |
+
},
|
| 13 |
+
"1": {
|
| 14 |
+
"content": "<s>",
|
| 15 |
+
"lstrip": false,
|
| 16 |
+
"normalized": false,
|
| 17 |
+
"rstrip": false,
|
| 18 |
+
"single_word": false,
|
| 19 |
+
"special": true
|
| 20 |
+
},
|
| 21 |
+
"2": {
|
| 22 |
+
"content": "</s>",
|
| 23 |
+
"lstrip": false,
|
| 24 |
+
"normalized": false,
|
| 25 |
+
"rstrip": false,
|
| 26 |
+
"single_word": false,
|
| 27 |
+
"special": true
|
| 28 |
+
}
|
| 29 |
+
},
|
| 30 |
+
"additional_special_tokens": [
|
| 31 |
+
"<unk>",
|
| 32 |
+
"<s>",
|
| 33 |
+
"</s>"
|
| 34 |
+
],
|
| 35 |
+
"bos_token": "<s>",
|
| 36 |
+
"clean_up_tokenization_spaces": false,
|
| 37 |
+
"eos_token": "</s>",
|
| 38 |
+
"legacy": false,
|
| 39 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 40 |
+
"pad_token": "<unk>",
|
| 41 |
+
"padding_side": "right",
|
| 42 |
+
"sp_model_kwargs": {},
|
| 43 |
+
"spaces_between_special_tokens": false,
|
| 44 |
+
"tokenizer_class": "LlamaTokenizer",
|
| 45 |
+
"tokenizer_file": null,
|
| 46 |
+
"unk_token": "<unk>",
|
| 47 |
+
"use_default_system_prompt": true
|
| 48 |
+
}
|
train_params.yaml
ADDED
|
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
batch_size_training: '32'
|
| 2 |
+
checkpoint_type: StateDictType.FULL_STATE_DICT
|
| 3 |
+
dataset: foundational_dataset
|
| 4 |
+
dist_checkpoint_folder: fine-tuned
|
| 5 |
+
dist_checkpoint_root_folder: test_run_save
|
| 6 |
+
enable_fsdp: 'True'
|
| 7 |
+
freeze_layers: 'False'
|
| 8 |
+
fsdp_activation_checkpointing: 'True'
|
| 9 |
+
gamma: '0.9'
|
| 10 |
+
load_peft_model: 'False'
|
| 11 |
+
low_cpu_fsdp: 'False'
|
| 12 |
+
lr: '0.0001'
|
| 13 |
+
micro_batch_size: '32'
|
| 14 |
+
mixed_precision: 'True'
|
| 15 |
+
model_name: models/v3/llama7b-full-1e-4_low-chunk1024-009-017
|
| 16 |
+
num_epochs: '1'
|
| 17 |
+
num_freeze_layers: '1'
|
| 18 |
+
num_workers_dataloader: '2'
|
| 19 |
+
one_gpu: 'False'
|
| 20 |
+
optimizer: AdamW
|
| 21 |
+
output_dir: PATH/to/save/PEFT/model
|
| 22 |
+
peft_method: lora
|
| 23 |
+
pure_bf16: 'True'
|
| 24 |
+
quantization: 'False'
|
| 25 |
+
run_validation: 'True'
|
| 26 |
+
save_model: 'True'
|
| 27 |
+
save_optimizer: 'False'
|
| 28 |
+
seed: '42'
|
| 29 |
+
sharding_strategy: ShardingStrategy.FULL_SHARD
|
| 30 |
+
type_of_model: foundational
|
| 31 |
+
use_fast_kernels: 'False'
|
| 32 |
+
use_fp16: 'False'
|
| 33 |
+
use_peft: 'False'
|
| 34 |
+
val_batch_size: '64'
|
| 35 |
+
weight_decay: '0.0'
|