Hugging Face's logo

argilla
/
phi2-lora-distilabel-intel-orca-dpo-pairs

Text Generation
PEFT
TensorBoard
Safetensors
English
trl
dpo
Generated from Trainer
distilabel
argilla
Model card Files Metrics Community
davidberenstein1957 HF staff commited on
Commit
95a69a1
•
1 Parent(s): 0b11e33

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +25 -25
README.md CHANGED
@@ -27,15 +27,15 @@ This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/m
27
  The full training notebook can be found [here](https://colab.research.google.com/drive/1PGMj7jlkJaCiSNNihA2NtpILsRgkRXrJ?usp=sharing).
28
 
29
  It achieves the following results on the evaluation set:
30
- - Loss: 0.0972
31
- - Rewards/chosen: 0.2699
32
- - Rewards/rejected: -5.8246
33
- - Rewards/accuracies: 0.9623
34
- - Rewards/margins: 6.0944
35
- - Logps/rejected: -311.1872
36
- - Logps/chosen: -115.6127
37
- - Logits/rejected: 0.0766
38
- - Logits/chosen: 0.0242
39
 
40
  ## Model description
41
 
@@ -146,22 +146,22 @@ The following hyperparameters were used during training:
146
 
147
  | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
148
  |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
149
- | 0.6805 | 0.06 | 20 | 0.6540 | 0.0096 | -0.0728 | 0.8367 | 0.0824 | -253.6698 | -118.2153 | 0.3760 | 0.3395 |
150
- | 0.5821 | 0.12 | 40 | 0.4977 | 0.0383 | -0.4385 | 0.9199 | 0.4768 | -257.3268 | -117.9285 | 0.3836 | 0.3356 |
151
- | 0.4163 | 0.19 | 60 | 0.3225 | 0.0641 | -1.1656 | 0.9257 | 1.2298 | -264.5979 | -117.6701 | 0.3836 | 0.3192 |
152
- | 0.275 | 0.25 | 80 | 0.2245 | 0.0476 | -2.1180 | 0.9316 | 2.1656 | -274.1212 | -117.8351 | 0.3399 | 0.2698 |
153
- | 0.1808 | 0.31 | 100 | 0.1771 | -0.0012 | -3.2019 | 0.9366 | 3.2007 | -284.9609 | -118.3238 | 0.2615 | 0.1964 |
154
- | 0.1405 | 0.37 | 120 | 0.1528 | 0.0185 | -4.0396 | 0.9425 | 4.0581 | -293.3371 | -118.1262 | 0.1983 | 0.1407 |
155
- | 0.1121 | 0.44 | 140 | 0.1389 | 0.0285 | -4.6518 | 0.9471 | 4.6802 | -299.4591 | -118.0267 | 0.1493 | 0.0980 |
156
- | 0.1544 | 0.5 | 160 | 0.1289 | 0.0745 | -4.9025 | 0.9506 | 4.9771 | -301.9670 | -117.5659 | 0.1257 | 0.0785 |
157
- | 0.1594 | 0.56 | 180 | 0.1204 | 0.1435 | -4.8770 | 0.9561 | 5.0205 | -301.7119 | -116.8765 | 0.1168 | 0.0696 |
158
- | 0.0988 | 0.62 | 200 | 0.1136 | 0.1830 | -5.1569 | 0.9576 | 5.3400 | -304.5108 | -116.4809 | 0.1078 | 0.0579 |
159
- | 0.1141 | 0.68 | 220 | 0.1080 | 0.2052 | -5.4532 | 0.9580 | 5.6584 | -307.4731 | -116.2591 | 0.0962 | 0.0460 |
160
- | 0.0943 | 0.75 | 240 | 0.1037 | 0.2326 | -5.6061 | 0.9592 | 5.8387 | -309.0026 | -115.9850 | 0.0913 | 0.0393 |
161
- | 0.1108 | 0.81 | 260 | 0.1008 | 0.2500 | -5.7399 | 0.9607 | 5.9900 | -310.3409 | -115.8109 | 0.0827 | 0.0316 |
162
- | 0.1088 | 0.87 | 280 | 0.0987 | 0.2677 | -5.7068 | 0.9619 | 5.9745 | -310.0096 | -115.6346 | 0.0825 | 0.0301 |
163
- | 0.0741 | 0.93 | 300 | 0.0975 | 0.2701 | -5.7873 | 0.9623 | 6.0574 | -310.8145 | -115.6102 | 0.0788 | 0.0261 |
164
- | 0.1059 | 1.0 | 320 | 0.0972 | 0.2699 | -5.8246 | 0.9623 | 6.0944 | -311.1872 | -115.6127 | 0.0766 | 0.0242 |
165
 
166
 
167
  ### Framework versions
 
27
  The full training notebook can be found [here](https://colab.research.google.com/drive/1PGMj7jlkJaCiSNNihA2NtpILsRgkRXrJ?usp=sharing).
28
 
29
  It achieves the following results on the evaluation set:
30
+ - Loss: 0.4537
31
+ - Rewards/chosen: -0.0837
32
+ - Rewards/rejected: -1.2628
33
+ - Rewards/accuracies: 0.8301
34
+ - Rewards/margins: 1.1791
35
+ - Logps/rejected: -224.8409
36
+ - Logps/chosen: -203.2228
37
+ - Logits/rejected: 0.4773
38
+ - Logits/chosen: 0.3062
39
 
40
  ## Model description
41
 
 
146
 
147
  | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
148
  |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
149
+ | 0.6853 | 0.06 | 20 | 0.6701 | 0.0133 | -0.0368 | 0.6905 | 0.0501 | -212.5803 | -202.2522 | 0.3853 | 0.2532 |
150
+ | 0.6312 | 0.12 | 40 | 0.5884 | 0.0422 | -0.2208 | 0.8138 | 0.2630 | -214.4207 | -201.9638 | 0.4254 | 0.2816 |
151
+ | 0.547 | 0.19 | 60 | 0.5146 | 0.0172 | -0.5786 | 0.8278 | 0.5958 | -217.9983 | -202.2132 | 0.4699 | 0.3110 |
152
+ | 0.4388 | 0.25 | 80 | 0.4893 | -0.0808 | -1.0789 | 0.8293 | 0.9981 | -223.0014 | -203.1934 | 0.5158 | 0.3396 |
153
+ | 0.4871 | 0.31 | 100 | 0.4818 | -0.1298 | -1.2346 | 0.8297 | 1.1048 | -224.5586 | -203.6837 | 0.5133 | 0.3340 |
154
+ | 0.4863 | 0.37 | 120 | 0.4723 | -0.1230 | -1.1718 | 0.8301 | 1.0488 | -223.9305 | -203.6159 | 0.4910 | 0.3167 |
155
+ | 0.4578 | 0.44 | 140 | 0.4666 | -0.1257 | -1.1772 | 0.8301 | 1.0515 | -223.9844 | -203.6428 | 0.4795 | 0.3078 |
156
+ | 0.4587 | 0.5 | 160 | 0.4625 | -0.0746 | -1.1272 | 0.8301 | 1.0526 | -223.4841 | -203.1310 | 0.4857 | 0.3139 |
157
+ | 0.4688 | 0.56 | 180 | 0.4595 | -0.0584 | -1.1194 | 0.8297 | 1.0610 | -223.4062 | -202.9692 | 0.4890 | 0.3171 |
158
+ | 0.4189 | 0.62 | 200 | 0.4579 | -0.0666 | -1.1647 | 0.8297 | 1.0982 | -223.8598 | -203.0511 | 0.4858 | 0.3138 |
159
+ | 0.4392 | 0.68 | 220 | 0.4564 | -0.0697 | -1.1915 | 0.8301 | 1.1219 | -224.1278 | -203.0823 | 0.4824 | 0.3110 |
160
+ | 0.4659 | 0.75 | 240 | 0.4554 | -0.0826 | -1.2245 | 0.8301 | 1.1419 | -224.4574 | -203.2112 | 0.4761 | 0.3052 |
161
+ | 0.4075 | 0.81 | 260 | 0.4544 | -0.0823 | -1.2328 | 0.8301 | 1.1504 | -224.5403 | -203.2089 | 0.4749 | 0.3044 |
162
+ | 0.4015 | 0.87 | 280 | 0.4543 | -0.0833 | -1.2590 | 0.8301 | 1.1757 | -224.8026 | -203.2188 | 0.4779 | 0.3067 |
163
+ | 0.4365 | 0.93 | 300 | 0.4539 | -0.0846 | -1.2658 | 0.8301 | 1.1812 | -224.8702 | -203.2313 | 0.4780 | 0.3067 |
164
+ | 0.4589 | 1.0 | 320 | 0.4537 | -0.0837 | -1.2628 | 0.8301 | 1.1791 | -224.8409 | -203.2228 | 0.4773 | 0.3062 |
165
 
166
 
167
  ### Framework versions