mihaimasala
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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
CHANGED
@@ -1,3 +1,636 @@
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---
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license: llama2
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+
---
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license: llama2
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+
language:
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+
- ro
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base_model: meta-llama/Llama-2-7b-hf
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model-index:
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- name: OpenLLM-Ro/RoLlama2-7b-Base-2024-05-14
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results:
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- task:
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type: text-generation
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dataset:
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name: Romanian_Academic_Benchmarks
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type: Romanian_Academic_Benchmarks
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metrics:
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- name: Average accuracy
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type: accuracy
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value: 38.03
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- task:
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type: text-generation
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dataset:
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name: OpenLLM-Ro/ro_arc_challenge
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type: OpenLLM-Ro/ro_arc_challenge
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metrics:
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- name: Average accuracy
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type: accuracy
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value: 37.95
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- task:
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type: text-generation
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dataset:
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name: OpenLLM-Ro/ro_mmlu
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type: OpenLLM-Ro/ro_mmlu
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metrics:
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- name: Average accuracy
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type: accuracy
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value: 27.22
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- task:
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type: text-generation
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dataset:
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name: OpenLLM-Ro/ro_winogrande
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type: OpenLLM-Ro/ro_winogrande
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metrics:
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- name: Average accuracy
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type: accuracy
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value: 59.29
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- task:
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type: text-generation
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dataset:
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name: OpenLLM-Ro/ro_hellaswag
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type: OpenLLM-Ro/ro_hellaswag
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metrics:
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- name: Average accuracy
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type: accuracy
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value: 57.22
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- task:
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type: text-generation
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dataset:
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name: OpenLLM-Ro/ro_gsm8k
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type: OpenLLM-Ro/ro_gsm8k
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metrics:
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- name: Average accuracy
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type: accuracy
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value: 2.53
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- task:
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type: text-generation
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dataset:
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name: OpenLLM-Ro/ro_truthfulqa
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type: OpenLLM-Ro/ro_truthfulqa
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metrics:
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- name: Average accuracy
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type: accuracy
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value: 44
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- task:
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type: text-generation
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dataset:
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name: LaRoSeDa_binary
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type: LaRoSeDa_binary
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metrics:
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- name: Average macro-f1
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type: macro-f1
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value: 83.25
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- task:
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type: text-generation
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dataset:
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name: LaRoSeDa_multiclass
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type: LaRoSeDa_multiclass
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metrics:
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- name: Average macro-f1
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type: macro-f1
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value: 61.04
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- task:
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type: text-generation
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dataset:
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name: LaRoSeDa_binary_finetuned
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type: LaRoSeDa_binary_finetuned
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metrics:
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- name: Average macro-f1
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type: macro-f1
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value: 98.97
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- task:
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type: text-generation
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dataset:
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name: LaRoSeDa_multiclass_finetuned
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type: LaRoSeDa_multiclass_finetuned
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metrics:
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- name: Average macro-f1
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type: macro-f1
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value: 87.72
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- task:
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type: text-generation
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dataset:
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name: WMT_EN-RO
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type: WMT_EN-RO
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metrics:
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- name: Average bleu
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type: bleu
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value: 10.01
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- task:
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type: text-generation
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dataset:
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name: WMT_RO-EN
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type: WMT_RO-EN
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metrics:
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- name: Average bleu
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type: bleu
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value: 13.03
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- task:
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type: text-generation
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dataset:
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name: WMT_EN-RO_finetuned
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type: WMT_EN-RO_finetuned
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metrics:
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- name: Average bleu
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type: bleu
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value: 27.85
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- task:
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type: text-generation
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dataset:
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name: WMT_RO-EN_finetuned
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type: WMT_RO-EN_finetuned
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metrics:
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- name: Average bleu
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type: bleu
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value: 39.3
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144 |
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- task:
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145 |
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type: text-generation
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146 |
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dataset:
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147 |
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name: XQuAD
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type: XQuAD
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metrics:
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- name: Average exact_match
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151 |
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type: exact_match
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152 |
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value: 30.15
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- task:
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154 |
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type: text-generation
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155 |
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dataset:
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156 |
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name: XQuAD
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157 |
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type: XQuAD
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158 |
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metrics:
|
159 |
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- name: Average f1
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160 |
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type: f1
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161 |
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value: 47.03
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162 |
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- task:
|
163 |
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type: text-generation
|
164 |
+
dataset:
|
165 |
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name: XQuAD_finetuned
|
166 |
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type: XQuAD_finetuned
|
167 |
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metrics:
|
168 |
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- name: Average exact_match
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169 |
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type: exact_match
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170 |
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value: 67.06
|
171 |
+
- task:
|
172 |
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type: text-generation
|
173 |
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dataset:
|
174 |
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name: XQuAD_finetuned
|
175 |
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type: XQuAD_finetuned
|
176 |
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metrics:
|
177 |
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- name: Average f1
|
178 |
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type: f1
|
179 |
+
value: 79.96
|
180 |
+
- task:
|
181 |
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type: text-generation
|
182 |
+
dataset:
|
183 |
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name: STS
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184 |
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type: STS
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185 |
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metrics:
|
186 |
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- name: Average spearman
|
187 |
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type: spearman
|
188 |
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value: 7.89
|
189 |
+
- task:
|
190 |
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type: text-generation
|
191 |
+
dataset:
|
192 |
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name: STS
|
193 |
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type: STS
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194 |
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metrics:
|
195 |
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- name: Average pearson
|
196 |
+
type: pearson
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197 |
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value: 7.98
|
198 |
+
- task:
|
199 |
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type: text-generation
|
200 |
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dataset:
|
201 |
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name: STS_finetuned
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202 |
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type: STS_finetuned
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203 |
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metrics:
|
204 |
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- name: Average spearman
|
205 |
+
type: spearman
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206 |
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value: 71.75
|
207 |
+
- task:
|
208 |
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type: text-generation
|
209 |
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dataset:
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210 |
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name: STS_finetuned
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211 |
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type: STS_finetuned
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212 |
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metrics:
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213 |
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- name: Average pearson
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214 |
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type: pearson
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215 |
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value: 71.99
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216 |
+
- task:
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217 |
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type: text-generation
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218 |
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dataset:
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219 |
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name: OpenLLM-Ro/ro_arc_challenge
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220 |
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type: OpenLLM-Ro/ro_arc_challenge
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221 |
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metrics:
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222 |
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- name: 0-shot
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223 |
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type: accuracy
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224 |
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value: 35.56
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225 |
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- name: 1-shot
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226 |
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type: accuracy
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227 |
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value: 36.42
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228 |
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- name: 3-shot
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229 |
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type: accuracy
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230 |
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value: 38.56
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- name: 5-shot
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232 |
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type: accuracy
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233 |
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value: 38.39
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234 |
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- name: 10-shot
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235 |
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type: accuracy
|
236 |
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value: 39.07
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237 |
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- name: 25-shot
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238 |
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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 |
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<!-- **APA:**
|
635 |
+
|
636 |
+
[More Information Needed] -->
|
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+
"model.layers.8.self_attn.v_proj.weight": "pytorch_model-00001-of-00003.bin",
|
287 |
+
"model.layers.9.input_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
288 |
+
"model.layers.9.mlp.down_proj.weight": "pytorch_model-00001-of-00003.bin",
|
289 |
+
"model.layers.9.mlp.gate_proj.weight": "pytorch_model-00001-of-00003.bin",
|
290 |
+
"model.layers.9.mlp.up_proj.weight": "pytorch_model-00001-of-00003.bin",
|
291 |
+
"model.layers.9.post_attention_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
292 |
+
"model.layers.9.self_attn.k_proj.weight": "pytorch_model-00001-of-00003.bin",
|
293 |
+
"model.layers.9.self_attn.o_proj.weight": "pytorch_model-00001-of-00003.bin",
|
294 |
+
"model.layers.9.self_attn.q_proj.weight": "pytorch_model-00001-of-00003.bin",
|
295 |
+
"model.layers.9.self_attn.v_proj.weight": "pytorch_model-00001-of-00003.bin",
|
296 |
+
"model.norm.weight": "pytorch_model-00003-of-00003.bin"
|
297 |
+
}
|
298 |
+
}
|
special_tokens_map.json
ADDED
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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'
|