File size: 4,951 Bytes
2915066
 
 
 
 
60f572b
2915066
 
 
60f572b
 
 
 
 
2915066
 
 
 
 
 
 
 
 
 
60f572b
2915066
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
---
library_name: transformers
license: other
base_model: trl-lib/qwen1.5-0.5b-sft
tags:
- alignment-handbook
- trl
- simpo
- generated_from_trainer
- trl
- simpo
- generated_from_trainer
datasets:
- yakazimir/ultrafeedback_binarized
model-index:
- name: qwen_cCPO_entropy_0_01
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# qwen_cCPO_entropy_0_01

This model is a fine-tuned version of [trl-lib/qwen1.5-0.5b-sft](https://huggingface.co/trl-lib/qwen1.5-0.5b-sft) on the yakazimir/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4896
- Sft Loss: 1.6913
- Rewards/chosen: -1.6159
- Rewards/rejected: -2.1942
- Rewards/accuracies: 0.6714
- Rewards/margins: 0.5783
- Logps/rejected: -2.1942
- Logps/chosen: -1.6159
- Logits/rejected: 0.2372
- Logits/chosen: 0.1346

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Sft Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.5602        | 0.2141 | 400  | 0.5593          | 1.3702   | -1.3432        | -1.4801          | 0.5579             | 0.1369          | -1.4801        | -1.3432      | 0.3397          | 0.2525        |
| 0.5448        | 0.4282 | 800  | 0.5318          | 1.4044   | -1.3785        | -1.6209          | 0.5883             | 0.2424          | -1.6209        | -1.3785      | 0.3618          | 0.2727        |
| 0.5415        | 0.6422 | 1200 | 0.5156          | 1.4648   | -1.4398        | -1.7890          | 0.6187             | 0.3493          | -1.7890        | -1.4398      | 0.3220          | 0.2312        |
| 0.4953        | 0.8563 | 1600 | 0.5101          | 1.4980   | -1.4449        | -1.7958          | 0.6261             | 0.3509          | -1.7958        | -1.4449      | 0.3743          | 0.2752        |
| 0.5743        | 1.0704 | 2000 | 0.5047          | 1.4884   | -1.4330        | -1.8072          | 0.6306             | 0.3742          | -1.8072        | -1.4330      | 0.3107          | 0.2131        |
| 0.4824        | 1.2845 | 2400 | 0.4963          | 1.6007   | -1.5341        | -2.0179          | 0.6536             | 0.4838          | -2.0179        | -1.5341      | 0.3099          | 0.2106        |
| 0.5266        | 1.4986 | 2800 | 0.4947          | 1.6155   | -1.5391        | -2.0193          | 0.6573             | 0.4801          | -2.0193        | -1.5391      | 0.2939          | 0.1953        |
| 0.5053        | 1.7127 | 3200 | 0.4936          | 1.5759   | -1.5037        | -1.9595          | 0.6484             | 0.4558          | -1.9595        | -1.5037      | 0.3131          | 0.2133        |
| 0.4712        | 1.9267 | 3600 | 0.4894          | 1.6467   | -1.5640        | -2.0770          | 0.6662             | 0.5129          | -2.0770        | -1.5640      | 0.3113          | 0.2089        |
| 0.4297        | 2.1408 | 4000 | 0.4894          | 1.6624   | -1.5827        | -2.1264          | 0.6699             | 0.5437          | -2.1264        | -1.5827      | 0.2311          | 0.1311        |
| 0.4418        | 2.3549 | 4400 | 0.4909          | 1.7121   | -1.6395        | -2.2277          | 0.6736             | 0.5882          | -2.2277        | -1.6395      | 0.2582          | 0.1535        |
| 0.4422        | 2.5690 | 4800 | 0.4894          | 1.6890   | -1.6151        | -2.1880          | 0.6699             | 0.5729          | -2.1880        | -1.6151      | 0.2371          | 0.1340        |
| 0.4463        | 2.7831 | 5200 | 0.4895          | 1.6851   | -1.6106        | -2.1856          | 0.6706             | 0.5751          | -2.1856        | -1.6106      | 0.2327          | 0.1306        |
| 0.4311        | 2.9972 | 5600 | 0.4896          | 1.6913   | -1.6159        | -2.1942          | 0.6714             | 0.5783          | -2.1942        | -1.6159      | 0.2372          | 0.1346        |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1