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OpenELM-1_1B-DPO-full-max-4-reward

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Files changed (9) hide show
  1. README.md +44 -44
  2. all_results.json +16 -16
  3. eval_results.json +12 -12
  4. model.safetensors +1 -1
  5. runs/Oct06_14-30-18_xe8545-a100-22/events.out.tfevents.1728231189.xe8545-a100-22.2422281.1 +3 -0
  6. runs/Oct07_05-34-25_xe8545-a100-22/events.out.tfevents.1728272702.xe8545-a100-22.2437144.0 +3 -0
  7. train_results.json +4 -4
  8. trainer_state.json +0 -0
  9. training_args.bin +1 -1
README.md CHANGED
@@ -17,15 +17,15 @@ should probably proofread and complete it, then remove this comment. -->
17
 
18
  This model was trained from scratch on an unknown dataset.
19
  It achieves the following results on the evaluation set:
20
- - Loss: 1.5703
21
- - Rewards/chosen: -13.1875
22
- - Rewards/rejected: -14.6875
23
- - Rewards/accuracies: 0.5898
24
- - Rewards/margins: 1.5
25
- - Logps/rejected: -1760.0
26
- - Logps/chosen: -1640.0
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- - Logits/rejected: 8.0
28
- - Logits/chosen: 6.0312
29
 
30
  ## Model description
31
 
@@ -62,41 +62,41 @@ The following hyperparameters were used during training:
62
 
63
  | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
64
  |:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
65
- | 0.6384 | 0.0838 | 80 | 0.6899 | -0.7109 | -0.7969 | 0.5566 | 0.0854 | -368.0 | -390.0 | -9.8125 | -10.125 |
66
- | 0.6225 | 0.1675 | 160 | 0.7090 | -1.4844 | -1.6094 | 0.5508 | 0.1279 | -450.0 | -466.0 | -9.25 | -9.6875 |
67
- | 0.6067 | 0.2513 | 240 | 0.7372 | -2.1875 | -2.375 | 0.5625 | 0.1865 | -528.0 | -540.0 | -6.75 | -7.5312 |
68
- | 0.63 | 0.3351 | 320 | 0.7781 | -2.9688 | -3.1719 | 0.5723 | 0.2051 | -604.0 | -616.0 | -4.3125 | -5.2188 |
69
- | 0.6294 | 0.4188 | 400 | 0.7330 | -2.5938 | -2.8594 | 0.5977 | 0.2773 | -576.0 | -576.0 | -4.6562 | -5.75 |
70
- | 0.6199 | 0.5026 | 480 | 0.7532 | -3.3438 | -3.6719 | 0.5820 | 0.3301 | -656.0 | -652.0 | -7.9688 | -8.9375 |
71
- | 0.5935 | 0.5864 | 560 | 0.7359 | -4.0625 | -4.4062 | 0.5859 | 0.3262 | -728.0 | -724.0 | -7.9062 | -8.875 |
72
- | 0.5859 | 0.6702 | 640 | 0.7149 | -3.6719 | -4.125 | 0.6191 | 0.4648 | -704.0 | -684.0 | -5.125 | -6.4062 |
73
- | 0.6127 | 0.7539 | 720 | 0.7057 | -3.7031 | -4.0312 | 0.6016 | 0.3320 | -692.0 | -688.0 | -6.875 | -8.0 |
74
- | 0.6012 | 0.8377 | 800 | 0.7512 | -3.6406 | -3.9375 | 0.5938 | 0.2988 | -684.0 | -684.0 | -10.75 | -11.125 |
75
- | 0.5948 | 0.9215 | 880 | 0.7496 | -3.0938 | -3.4531 | 0.5918 | 0.3457 | -632.0 | -628.0 | -4.9688 | -6.875 |
76
- | 0.434 | 1.0052 | 960 | 0.7349 | -4.4062 | -4.9688 | 0.6289 | 0.5703 | -784.0 | -760.0 | -2.8906 | -4.75 |
77
- | 0.1948 | 1.0890 | 1040 | 1.0377 | -7.0 | -7.75 | 0.6211 | 0.7539 | -1064.0 | -1016.0 | 1.125 | -0.8477 |
78
- | 0.2139 | 1.1728 | 1120 | 0.9297 | -7.7188 | -8.375 | 0.6133 | 0.6562 | -1128.0 | -1088.0 | 3.2969 | 1.5469 |
79
- | 0.1666 | 1.2565 | 1200 | 1.0332 | -8.375 | -9.0625 | 0.5840 | 0.7109 | -1200.0 | -1152.0 | 2.9219 | 1.0859 |
80
- | 0.1994 | 1.3403 | 1280 | 0.9937 | -7.4688 | -8.25 | 0.5918 | 0.7969 | -1112.0 | -1064.0 | 0.4863 | -1.6797 |
81
- | 0.1836 | 1.4241 | 1360 | 0.9652 | -7.4688 | -8.3125 | 0.6074 | 0.8359 | -1120.0 | -1064.0 | 1.3984 | -0.7383 |
82
- | 0.153 | 1.5079 | 1440 | 1.1797 | -9.6875 | -10.625 | 0.5938 | 0.9102 | -1352.0 | -1288.0 | 4.0 | 1.9219 |
83
- | 0.1398 | 1.5916 | 1520 | 1.0459 | -9.0 | -9.9375 | 0.5996 | 0.9609 | -1280.0 | -1216.0 | 4.1875 | 2.1562 |
84
- | 0.1634 | 1.6754 | 1600 | 1.0131 | -9.0625 | -10.0 | 0.5977 | 0.9375 | -1288.0 | -1224.0 | 2.4062 | 0.1006 |
85
- | 0.1777 | 1.7592 | 1680 | 1.0902 | -8.6875 | -9.625 | 0.625 | 0.9219 | -1248.0 | -1192.0 | 3.5312 | 1.1875 |
86
- | 0.1536 | 1.8429 | 1760 | 1.0046 | -9.125 | -10.0625 | 0.6074 | 0.9180 | -1296.0 | -1232.0 | 3.8281 | 1.6797 |
87
- | 0.1713 | 1.9267 | 1840 | 1.0627 | -9.3125 | -10.375 | 0.6230 | 1.0781 | -1328.0 | -1248.0 | 5.2812 | 3.0781 |
88
- | 0.0282 | 2.0105 | 1920 | 1.0638 | -9.0625 | -10.125 | 0.6133 | 1.0625 | -1304.0 | -1224.0 | 4.125 | 1.8281 |
89
- | 0.0234 | 2.0942 | 2000 | 1.3751 | -11.625 | -12.8125 | 0.5957 | 1.2266 | -1568.0 | -1480.0 | 6.5938 | 4.5 |
90
- | 0.0167 | 2.1780 | 2080 | 1.4855 | -12.625 | -14.0 | 0.5977 | 1.375 | -1688.0 | -1584.0 | 7.5 | 5.4375 |
91
- | 0.0366 | 2.2618 | 2160 | 1.4665 | -12.1875 | -13.5625 | 0.5957 | 1.3594 | -1648.0 | -1536.0 | 7.3125 | 5.25 |
92
- | 0.0268 | 2.3455 | 2240 | 1.5465 | -12.8125 | -14.1875 | 0.5996 | 1.3594 | -1712.0 | -1600.0 | 7.6875 | 5.7188 |
93
- | 0.0241 | 2.4293 | 2320 | 1.5025 | -12.25 | -13.6875 | 0.5996 | 1.4219 | -1656.0 | -1544.0 | 7.25 | 5.1875 |
94
- | 0.0261 | 2.5131 | 2400 | 1.5371 | -12.75 | -14.1875 | 0.5938 | 1.4375 | -1712.0 | -1592.0 | 7.5312 | 5.5 |
95
- | 0.0245 | 2.5969 | 2480 | 1.5284 | -12.625 | -14.0625 | 0.5977 | 1.4453 | -1696.0 | -1584.0 | 7.625 | 5.5625 |
96
- | 0.0249 | 2.6806 | 2560 | 1.5392 | -12.8125 | -14.25 | 0.5938 | 1.4766 | -1712.0 | -1600.0 | 7.7812 | 5.7188 |
97
- | 0.02 | 2.7644 | 2640 | 1.5571 | -13.0625 | -14.5625 | 0.5898 | 1.4922 | -1744.0 | -1624.0 | 7.9375 | 5.9375 |
98
- | 0.0194 | 2.8482 | 2720 | 1.5688 | -13.1875 | -14.6875 | 0.5898 | 1.5 | -1760.0 | -1640.0 | 8.0625 | 6.0312 |
99
- | 0.0163 | 2.9319 | 2800 | 1.5703 | -13.1875 | -14.6875 | 0.5898 | 1.5 | -1760.0 | -1640.0 | 8.0 | 6.0312 |
100
 
101
 
102
  ### Framework versions
 
17
 
18
  This model was trained from scratch on an unknown dataset.
19
  It achieves the following results on the evaluation set:
20
+ - Loss: 1.6190
21
+ - Rewards/chosen: -13.625
22
+ - Rewards/rejected: -15.0625
23
+ - Rewards/accuracies: 0.5996
24
+ - Rewards/margins: 1.4688
25
+ - Logps/rejected: -1800.0
26
+ - Logps/chosen: -1680.0
27
+ - Logits/rejected: 1.0625
28
+ - Logits/chosen: -0.2695
29
 
30
  ## Model description
31
 
 
62
 
63
  | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
64
  |:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
65
+ | 0.6378 | 0.0838 | 80 | 0.6868 | -0.6758 | -0.7656 | 0.5684 | 0.0918 | -366.0 | -386.0 | -9.875 | -10.125 |
66
+ | 0.6219 | 0.1675 | 160 | 0.6949 | -0.9102 | -1.0547 | 0.5977 | 0.1406 | -394.0 | -410.0 | -10.125 | -10.5 |
67
+ | 0.6151 | 0.2513 | 240 | 0.7637 | -2.4531 | -2.6562 | 0.5566 | 0.2031 | -552.0 | -564.0 | -10.9375 | -11.25 |
68
+ | 0.6607 | 0.3351 | 320 | 0.7307 | -2.7344 | -2.9375 | 0.5742 | 0.1992 | -584.0 | -592.0 | -14.25 | -14.4375 |
69
+ | 0.6304 | 0.4188 | 400 | 0.7129 | -2.7344 | -3.0156 | 0.5898 | 0.2715 | -588.0 | -592.0 | -12.5 | -13.0 |
70
+ | 0.623 | 0.5026 | 480 | 0.7718 | -2.5469 | -2.9375 | 0.5859 | 0.3887 | -584.0 | -572.0 | -8.0625 | -9.0 |
71
+ | 0.6091 | 0.5864 | 560 | 0.7543 | -3.3281 | -3.6562 | 0.5957 | 0.3320 | -656.0 | -652.0 | -12.0 | -12.75 |
72
+ | 0.583 | 0.6702 | 640 | 0.7081 | -3.25 | -3.7031 | 0.6406 | 0.4648 | -660.0 | -644.0 | -9.0 | -10.0625 |
73
+ | 0.6183 | 0.7539 | 720 | 0.7397 | -3.7812 | -4.0938 | 0.5996 | 0.3242 | -700.0 | -696.0 | -8.5625 | -9.4375 |
74
+ | 0.5988 | 0.8377 | 800 | 0.7986 | -4.4688 | -4.9375 | 0.5898 | 0.4609 | -784.0 | -764.0 | -7.9062 | -8.9375 |
75
+ | 0.5882 | 0.9215 | 880 | 0.7997 | -3.2656 | -3.6562 | 0.5879 | 0.3906 | -656.0 | -644.0 | -8.3125 | -9.1875 |
76
+ | 0.4256 | 1.0052 | 960 | 0.7816 | -4.5312 | -5.1875 | 0.6172 | 0.6367 | -808.0 | -772.0 | -6.75 | -7.9062 |
77
+ | 0.2006 | 1.0890 | 1040 | 0.9734 | -5.9688 | -6.6875 | 0.6094 | 0.7383 | -960.0 | -916.0 | -4.7812 | -6.0625 |
78
+ | 0.1977 | 1.1728 | 1120 | 0.9420 | -6.25 | -7.0 | 0.6094 | 0.7578 | -988.0 | -944.0 | -5.0 | -6.25 |
79
+ | 0.1717 | 1.2565 | 1200 | 1.0548 | -7.4688 | -8.25 | 0.5918 | 0.7852 | -1112.0 | -1064.0 | -4.5 | -5.8125 |
80
+ | 0.1881 | 1.3403 | 1280 | 0.9567 | -6.9688 | -7.8125 | 0.6035 | 0.8672 | -1072.0 | -1012.0 | -3.2188 | -4.4688 |
81
+ | 0.1897 | 1.4241 | 1360 | 0.9563 | -6.9688 | -7.8438 | 0.6055 | 0.8867 | -1072.0 | -1016.0 | -4.2812 | -5.6875 |
82
+ | 0.1383 | 1.5079 | 1440 | 1.1196 | -8.5625 | -9.5 | 0.6055 | 0.9922 | -1240.0 | -1176.0 | -2.5938 | -3.9062 |
83
+ | 0.146 | 1.5916 | 1520 | 1.0767 | -9.5 | -10.5 | 0.6055 | 1.0078 | -1336.0 | -1264.0 | -1.6797 | -3.0312 |
84
+ | 0.1831 | 1.6754 | 1600 | 0.9776 | -8.0625 | -8.9375 | 0.6055 | 0.8516 | -1184.0 | -1128.0 | -2.2344 | -3.5938 |
85
+ | 0.1667 | 1.7592 | 1680 | 1.0210 | -7.75 | -8.625 | 0.5957 | 0.9023 | -1152.0 | -1088.0 | -1.7344 | -3.2344 |
86
+ | 0.1514 | 1.8429 | 1760 | 1.0214 | -8.6875 | -9.6875 | 0.6133 | 0.9805 | -1256.0 | -1184.0 | -1.1719 | -2.5312 |
87
+ | 0.1594 | 1.9267 | 1840 | 1.0633 | -8.8125 | -9.75 | 0.5977 | 0.9727 | -1264.0 | -1200.0 | -1.2344 | -2.625 |
88
+ | 0.0307 | 2.0105 | 1920 | 1.0948 | -8.75 | -9.75 | 0.6172 | 1.0312 | -1264.0 | -1192.0 | -1.4531 | -2.9844 |
89
+ | 0.0214 | 2.0942 | 2000 | 1.5354 | -12.25 | -13.3125 | 0.6094 | 1.1016 | -1624.0 | -1544.0 | 0.1973 | -1.2031 |
90
+ | 0.0186 | 2.1780 | 2080 | 1.5790 | -13.5625 | -14.9375 | 0.6055 | 1.3906 | -1784.0 | -1680.0 | 0.4902 | -0.9102 |
91
+ | 0.0395 | 2.2618 | 2160 | 1.5234 | -12.0625 | -13.1875 | 0.6035 | 1.1406 | -1608.0 | -1520.0 | 0.5391 | -0.7656 |
92
+ | 0.0217 | 2.3455 | 2240 | 1.5867 | -13.1875 | -14.5625 | 0.6035 | 1.375 | -1744.0 | -1632.0 | 0.8945 | -0.4141 |
93
+ | 0.0268 | 2.4293 | 2320 | 1.5888 | -13.0 | -14.375 | 0.6035 | 1.4219 | -1728.0 | -1616.0 | 0.6797 | -0.6758 |
94
+ | 0.0238 | 2.5131 | 2400 | 1.6647 | -13.625 | -15.0625 | 0.6055 | 1.4453 | -1792.0 | -1680.0 | 0.9648 | -0.3633 |
95
+ | 0.0227 | 2.5969 | 2480 | 1.5873 | -13.125 | -14.5625 | 0.6094 | 1.4375 | -1744.0 | -1632.0 | 0.9258 | -0.4199 |
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+ | 0.0233 | 2.6806 | 2560 | 1.5836 | -13.1875 | -14.625 | 0.6035 | 1.4297 | -1752.0 | -1640.0 | 0.9297 | -0.4180 |
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+ | 0.021 | 2.7644 | 2640 | 1.5917 | -13.4375 | -14.9375 | 0.6094 | 1.4609 | -1776.0 | -1664.0 | 1.0078 | -0.3223 |
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+ | 0.0221 | 2.8482 | 2720 | 1.6077 | -13.5625 | -15.0 | 0.6035 | 1.4609 | -1792.0 | -1672.0 | 1.0469 | -0.2793 |
99
+ | 0.0182 | 2.9319 | 2800 | 1.6190 | -13.625 | -15.0625 | 0.5996 | 1.4688 | -1800.0 | -1680.0 | 1.0625 | -0.2695 |
100
 
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  ### Framework versions
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